The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites, which provides a very high temporal (four times per day) and spatial (1 km) resolution, has become one of the most important and widely used sensors for a broad range of applications. We analyze 529 articles from 159 journals in the Scopus database from 2009 to 2018 to understand the global and longitudinal trends of MODIS land surface temperature (LST) data applications. The results show that the publications of papers related to MODIS LST data have been steadily rising annually. They spanned 19 subject areas from environmental, agricultural, and biological science to social science and medicine, indicating a wide range of MODIS LST data applications. Among the 159 journals, Remote Sensing of Environment, Remote Sensing, and the International Journal of Remote Sensing published the most articles. The study also showed that urban heat island (UHI), air temperature estimation/mapping (Ta estimation), soil moisture, evapotranspiration estimation, and drought monitoring/estimation were the most popular applications of MODIS LST data. Furthermore, we discuss the strengths, limitations, and future direction of research using MODIS LST data.

A large variety of remote sensing-based land use/land cover (LULC) products are currently available on national and global scales. This literature review and in situ validation study evaluate the suitability of these products for local scale applications in the complex terrain of the Ethiopian mountains. For the review, 146 research papers are analyzed. Most studies (73%) have been published since 2013 and are based on individually computed maps. Not a single study relied on readily available LULC products. Nine readily available LULC products with 20-, 30-, 300-, 500-, and 1000-m spatial resolution have been identified at national and global scales. To complement and extend this body of research, the recent (since 2013) LULC products are validated using 185 ground truth points collected in the Bale Mountains National Park between 1500 and 4385 m a.s.l. The results indicate a rather poor overall accuracy (<50  %  ).

This guest editorial summarizes the Special Section on Optics Propagation and Adaptive Systems

A common method for synthesizing turbulent imagery is to model phase perturbations on a wavefront and then propagate the wavefront to the entrance pupil of an imaging system. The point spread function (PSF) that results from the wavefront in the pupil is then computed and used to synthesize images by the usual means of convolution. In a recent publication, a method was disclosed using sparse and redundant dictionaries of turbulent characteristics to construct PSFs directly in the image plane and simulate image formation without making phase models and computing wavefront propagation. However, the dictionary method, as disclosed in the recent publication, is limited to modeling PSFs characterized by the Fried parameter of the data used to construct the dictionary. Herein, we demonstrate that a dictionary constructed from data with a given Fried parameter can be scaled to construct turbulent PSFs corresponding to larger and smaller values of the Fried parameter. This enables a single dictionary, or a small number of dictionaries, to serve for the simulation of turbulent images over a range of turbulence conditions.

The history of the development of works on the creation of the elemental base of adaptive optics for a solar Russian telescope is briefly described. We consider separate issues of the development of the wavefront sensor (WFS) based on the Shack–Hartmann correlation sensor to provide measurements under conditions of strong turbulence. The parameters of the WFS are calculated on the basis of long-term observations of meteorological characteristics and optical measurements of the Fried parameter. A special adjusting device with a block of fast rasters changing is developed. The use of rasters of various dimensions is provided in the WFS. The sensor uses the original algorithm for determining the maximum of the correlation function. The effect on the accuracy of measuring the phase distortions of turbulence occurring in the rooms of the telescope itself is analyzed.

Deleterious effects of the atmosphere on remotely acquired images includes absorption and scattering of light by aerosol particulates, which not only attenuates the signal but can potentially cause blurring due to forward-scattered light accepted by the imaging system. Proposed aerosol scattering models (e.g., Ishimaru) provide a method for simulating the contrast and spatial detail expected when imaging through atmospheres with significant aerosol optical depth. This work explores closure between modulation transfer functions (MTFs) obtained from directly measured images and MTFs calculated from theory using measured cloud properties. The closure experiments are performed in a laboratory cloud chamber in which cloud droplet number density and size distribution are directly measured. Images of a binary knife-edge target were taken with an optical detector on the other side of a water cloud generated through reduction of pressure in the humidified chamber. The key results of this closure experiment are: the theoretical expression for the aerosol MTF is likely overly simplistic and does not account for broad particle size distributions. The significance of optical blurring from light scattering by aerosol particles depends sensitively on the properties of both the particles and the imaging system.

Coherent properties of diffraction-free Bessel-like optical beams propagating in the turbulent atmosphere are theoretically studied. An analytical solution of the equation for the second-order transverse function of mutual coherence of the optical radiation field, which was obtained from paraxial approximation of the scalar wave equation, is analyzed. The behavior of the degree of coherence, coherence radius, and integral scale of the degree of coherence of the Bessel–Gaussian beam and conic wave obtained through conic focusing of a Gaussian beam by an axicon is analyzed for different beam parameters and characteristics of the turbulent atmosphere. Significant qualitative and quantitative differences are discovered between the studied coherence characteristics of the Bessel–Gaussian beam and the conic wave. In general, under identical propagation conditions in the turbulent atmosphere, the coherence of the conic wave is higher than that of the Bessel–Gaussian beam. The comparison of two spatial scales of the degree of coherence of optical beams reveals that the integral scale for diffraction-free beams is a more representative characteristic than the coherence radius. The integral scale of the degree of coherence of diffraction-free beams is more unequivocally connected with the conditions of propagation of optical radiation in the turbulent atmosphere.

High-resolution imagery of satellites from ground-based telescopes plays an important role in space situational awareness (SSA). Current approaches generally rely on large aperture telescopes equipped with adaptive optics. The cost and operational demands of such systems place severe limitations on worldwide surveillance capability. Coverage of the sky is restricted to the regions that happen to be above the small number of geographical locations where facilities exist. However, recent advances in carbon fiber reinforced polymer (CFRP) mirrors offer the potential for field-deployable telescopes with an aperture diameter of 1 m or larger for ground-based imaging of space objects. Concave CFRP mirrors are made from convex mandrels, which are challenging to fabricate. Therefore, a major cost driver of a CFRP telescope is the optical quality of the mandrel. Here we show, using both numerical simulations and real data, that a telescope with significant optical aberration can be used for high-resolution imaging if the telescope is equipped with a wavefront sensor (WFS) and the recorded images and WFS data are processed with an appropriate image restoration algorithm. We anticipate that the maturation of the technology to manufacture telescopes at this size from light-weight replicated components, at a cost considerably lower than conventional telescopes, will address a growing demand for SSA data.

We use a non-Kolmogorov power spectrum for theoretical investigation of laser beam propagation in the double-passage problem: transmitter–target–receiver. The major application of our work is the light detection and ranging (LIDAR) system operating in the zone of the atmosphere where non-Kolmogorov turbulence may be present. We show that the scintillation index is substantially affected by non-Kolmogorov turbulent channels at any turbulence strength. We consider the case of a small unresolved target and our analysis is valid for both the bistatic and monostatic configuration of the LIDAR system, in which the latter leads to the enhanced backscattering effects. Additionally, the paper contains a discussion of the simulation approach to light propagation through non-Kolmogorov turbulence. The simulation results and a discussion of their agreement with the theory are included. We found that the theory and the results of the simulations agree only to a certain extent. The “giant spikes” that occur when propagating through “deep turbulence” are responsible for the lack of better agreement. Their influence on the scintillation index and also on the probability density function of intensity probably requires a theoretical approach.

This guest editorial summarizes the Special Section on Advances in Remote Sensing for Air Quality Management

Satellite remote sensing technology provides the only viable means for global monitoring of atmosphere systems, such as ozone. The ozone mapping and profiler suite (OMPS) onboard Suomi-NPP satellite, which was launched in the year 2011, has a primary purpose of measuring ozone. Suomi-NPP has been on operation for more than 3 years, and it is crucial to keep the satellite data precise and trusted. By using 17 months of satellite and ground-based total ozone column (TOC) data, this study performs an evaluation of OMPS products. Ozone monitoring instrument (OMI) data generated from a similar satellite instrument were also used to compare with the OMPS TOC data, and both the TOC products were generated using TOMS version 8.5 (TOMS-V8.5) algorithm. The evaluation consists of intercomparisons with ground-based Brewer measurements, similar satellite instruments, and accuracy analysis as a function of time and solar zenith angle. Results show that after 3 years of operation, OMPS-derived TOC data still have good correlation (R²  >  0.99, RMSE  =  1.51  %  ) with ground-based measurements. The results also give some evidence that the OMPS TOC data have better accuracy than those from OMI using the same algorithm.

Inhalation of airborne particulate matter (PM) is associated with a variety of adverse health outcomes. However, the relative toxicity of specific PM types—mixtures of particles of varying sizes, shapes, and chemical compositions—is not well understood. A major impediment has been the sparse distribution of surface sensors, especially those measuring speciated PM. Aerosol remote sensing from Earth orbit offers the opportunity to improve our understanding of the health risks associated with different particle types and sources. The Multi-angle Imaging SpectroRadiometer (MISR) instrument aboard NASA’s Terra satellite has demonstrated the value of near-simultaneous observations of backscattered sunlight from multiple view angles for remote sensing of aerosol abundances and particle properties over land. The Multi-Angle Imager for Aerosols (MAIA) instrument, currently in development, improves on MISR’s sensitivity to airborne particle composition by incorporating polarimetry and expanded spectral range. Spatiotemporal regression relationships generated using collocated surface monitor and chemical transport model data will be used to convert fractional aerosol optical depths retrieved from MAIA observations to near-surface PM₁₀, PM_2.5, and speciated PM_2.5. Health scientists on the MAIA team will use the resulting exposure estimates over globally distributed target areas to investigate the association of particle species with population health effects.

Aerosol observations with ceilometers have been made worldwide recently. To use ceilometer data to retrieve aerosol profiles, raw signals should be accurately converted to the attenuated backscattering coefficient. Hence, the calibration coefficient for the system constant has to be determined correctly. We conducted a ceilometer–lidar comparative experiment to evaluate the Lufft CHM15k Nimbus product. The attenuated backscattering coefficient using CHM15k was smaller by a factor of 1.48 compared to that of lidar. The calibration coefficient should be periodically corrected using the ceilometer signal itself since lidar data are generally unavailable in the field observations. We recalibrated the product using both Rayleigh fitting and cloud attenuation methods. The correction factor, determined from the recalibration, was 15% (9%) smaller when using the Rayleigh fitting (cloud attenuation) method than the factor determined from lidar. Uncertainties from backscattering ratios at the reference height and the lidar ratio can cause systematic errors in the correction factor determined from the Rayleigh fitting method. Uncertainties due to the multiple scattering factor contribute to systematic errors for the cloud attenuation method. We propose a calibration method using depolarization ratios for future polarization-sensitive ceilometers, which can estimate the calibration coefficient without multiple scattering factors.

Numerous studies employed remote sensing techniques on regional air quality in terms of aerosols, in particular, the observations from polar-orbiting satellites offer more detail on spatial distribution. Since the air pollutants/aerosols dramatically vary in location with time, diurnal observations on a timescale are restricted by the temporal resolution of the polar-orbiting satellite. To address this issue, this research proposes a spatially and temporally adaptive reflectance fusion model for measuring atmospheric properties to synthesize high-spatial–temporal resolution images from polar and geostationary satellite imagery for air quality monitoring. The reflectivity from short-wave infrared is employed to preserve the atmospheric effect within the fused image in the green band for further aerosol optical depth (AOD) retrieval. Taking the Landsat-8 Operational Land Imager as the reference, the spatial resolution of the Himawari-8 Advanced Himawari Imager (in kilometers) can thus be resampled into 30 m every 10 min during the daytime, by considering the surface bidirectional reflectivity from the variation of the solar zenith angle. The AOD retrieved with fused images containing atmospheric effect could have a better performance after comparison with in situ measurements, and therefore, be suggested for high-spatial–temporal aerosol monitoring.

GaoFen-4 (GF-4) is China’s first optical remote sensing geostationary satellite, which observes the Earth’s surface every 20 s. GF-4 has a gaze camera with a ground resolution of 50 m in the visible spectral bands that are sensitive to aerosol optical depth (AOD). AOD retrieval was completed using initial prior surface reflectance in ground-atmosphere decoupling. Surface reflectance was then updated using satellite measurements. The algorithm used in this study was based on two time-adjacent images of GF-4 (which have a constant viewing angle in the same area) and on the following two assumptions: (1) AOD varies quickly with time, but slowly with location and (2) surface reflectance varies quickly with location, but slowly with time. AOD retrieval was then accomplished using a lookup table strategy. The data from June 2016, in the North China Plain were selected for the AOD retrieval test. GF-4-derived AOD was validated using ground measurements from the aerosol robotic network and Sun–Sky radiometer network with a correlation coefficient of R  =  0.794. The derived results agreed reasonably well with the moderate resolution imaging spectroradiometer collection 6.0 aerosol product, with a correlation coefficient of R  =  0.893. The atmospheric distribution and changes in some parts of the North China Plain were analyzed using the aerosol products of GF-4. The results showed that it is feasible to use the time-series imaging method to conduct high spatiotemporal resolution aerosol inversion using GF-4’s data, which can be used for detecting changes in air pollution.

Satellites offer an unprecedented opportunity to evaluate patterns and trends in air pollution, especially in regions with few or no ground-based monitors. We evaluate the 100 most populous cities worldwide, comparing nitrogen dioxide (NO₂) vertical column densities from the Aura satellite to population, gross urban product (GUP), and emissions estimates. We find a positive relationship between GUP and NO₂ for 38 of the 56 low-income cities, where NO₂ increases with GUP, and a negative relationship for the 7 high-income cities, where NO₂ decreases with GUP. This trend is consistent with the environmental Kuznets curve model. However, we found the GUP of 36 cities in the middle range of incomes did not display any consistent relationship with NO₂. This partial Kuznets curve relationship arises across the 2005 to 2011 years of our study period, where specific cities show a positive or negative trend in NO₂ with GUP growth over time. We analyze a global emissions inventory to compare the relationship between GUP per capita and pollution, which shows a similar relationship. The difference between observed NO₂ and the emissions inventory could be due to atmospheric processes or could be due to city-specific changes in energy that are not well captured in the inventory.

We present the instrumentation and products of the NASA Plankton Aerosol, Cloud, ocean Ecosystem (PACE) mission relevant to air quality management. Since PACE will launch in the 2022 to 2023 timeframe, we discuss several activities in anticipation of a robust air quality applications program using PACE products. Products from the PACE ocean color imager and two multiangle polarimeters will be used synergistically to retrieve properties relevant to air quality applications. These instruments provide high spectral and spatial resolution measurements used to derive key properties of aerosols and clouds including effective particle radii, particle shapes, aerosol and cloud optical depths, refractive indices and single scattering albedos, all of which are critical for characterizing airmasses for managing air quality, hazardous episodes of wildfire and volcanic emissions, and long range transport of pollution. Because of the number of products with potential societal benefits, the PACE mission is highly pertinent to NASA’s Applied Sciences Program’s efforts to promote, discover, and demonstrate innovative, practical, and sustainable uses of the Earth observations. We discuss plans to support these efforts by establishing a prelaunch early adopter program and outline communication strategies to engage the air quality user community.

This paper compares three dust detection algorithms over land that were developed for operational, near-real-time processing using the Suomi National Polar Orbiting Partnership Visible Infrared Imaging Radiometer Suite instrument. The three algorithm approaches use different spectral bands, namely deep blue bands, infrared (IR)-visible bands, and IR bands, and are applied for dust observed over dark as well as bright surfaces. The evaluations are performed both using case studies and AERONET matchup data over western CONUS-Mexico region and North Africa-Arabian Peninsula region. The deep blue-based algorithm is found to have the most false detections and its detection performance depends on the Sun-satellite geometries. Simulation analysis shows that there are three causes of this problem: surface reflectance, air mass factors, and phase functions in different geometries. The algorithm based on IR-visible bands has much less false detection than the deep blue bands-based algorithm and has better true positive detection than the IR-based algorithm. The IR bands-based algorithm performs well in the case studies over CONUS–Mexico region, but it fails to detect most of the dust cases over North Africa–Arabian Peninsula region. The results suggest that the IR-visible algorithm is the most suitable for the dust detection of the three algorithms with a small modification. Because the IR-visible algorithm is not able to detect all the dust pixels, detections from the deep blue algorithm only and those from the IR-visible algorithm with relaxed criteria are also provided but are distinguished with a lower quality.

Tropospheric NO₂ concentrations obtained from the measurements of the Ozone Monitoring Instrument on board the NASA Aura satellite from 2005 to 2016 were studied to identify major NO₂ emission hot spots, trends, and seasonal variations over Kazakhstan. Emission hot spots are observed over the locations of thermal power plants (Ekibastuz) and major urban and industrial regions (Almaty and Shymkent), as well as the capital city (Astana). Some decreasing trends have been observed for NO₂ over Ekibastuz, whereas the regions of Almaty and Shymkent showed increasing trends due to industrial growth. The seasonal pattern of the NO₂ concentration shows a difference between three industrial cities of Almaty, Shymkent, and Ekibastuz versus the rest of Kazakhstan. In these three cities, a NO₂ maximum is found during wintertime, which we attribute to seasonality of emissions associated with electricity production and the longer chemical lifetime of NO₂ in winter. In contrast, in Astana and the rest of Kazakhstan, the NO₂ concentration reaches a maximum in the summer.

Starting in 2011, a team-based approach has been developed to connect NASA science with air quality and health communities. These teams, funded by the NASA Applied Sciences Program, promote collaboration within the team, communication with end-user communities, and the rapid advancement of applied research. The team structure provides increased flexibility to address high-priority research areas, better aligning research questions with user needs. The first NASA team built on this structure was the Air Quality and Applied Sciences Team (AQAST, 2011 to 2016), and continued with the Health and Applied Sciences Team (HAQAST, 2016 to 2019). Over the years of AQAST and HAQAST, we have experimented with different approaches to manage an Applied Sciences Team. We have adjusted our approach based on lessons learned and feedback gathered from stakeholders, team members, program mangers, and meeting attendees. We have found that this type of team succeeds by building a culture of collaboration, advancing communication with stakeholder communities, and identifying issues where the team structure can provide a rapid response. AQAST and HAQAST represent a model of funding and research with positive outcomes for air quality and public health engagement with NASA data and tools. This team-based approach is well suited to mission-driven, applied science activities.

This guest editorial introduces the Special Section on Advances in Agro-Hydrological Remote Sensing for Water Resources Conservation

This study proposed a green–red band quasianalytical algorithm, QAA-GRI, and evaluates its performance using an in situ dataset from Lake Qiandaohu, a drinking water source, in China. First, by shifting the reference wavelength from 555 to 510 nm, a green–red index (GRI) can be calculated from remote sensing reflectance at 510, 560, and 620 nm, and the index was then used to retrieve the total absorption coefficients at the reference band, a  (  510  )  . Second, a semianalytical model based on a  (  510  )   and the GRI was deduced to establish the QAA-GRI, replacing the empirical model in quasianalytical algorithm version 5 (QAA-v5). The QAA-GRI was applied to retrieve absorption coefficients from an in situ dataset of Lake Qiandaohu, and the QAA-GRI’s performance was compared with that of QAA-v5 and another red–green QAA-based approach (QAA-RGR). The results demonstrate that, for this dataset, the QAA-GRI exhibited better performance (R2  =  0.81, mean absolute percentage error, MAPE  =  15.7  %  ), which indicated a clear improvement in the accuracy of absorption coefficient retrieval, compared with QAA-v5 (R2  =  0.56, MAPE  =  21.2  %  ) and QAA-RGR (R2  =  0.67, MAPE  =  22.6  %  ). In addition, to illustrate the QAA-GRI’s assumptions and its applicability, the QAA-GRI was also applied to an in situ dataset from Taihu Lake, a highly turbid and eutrophic water source. As predicted, when applied to Taihu Lake, the QAA-GRI did not perform as well as it did when applied to Lake Qiandaohu because of the former’s extremely high turbidity, which can cause greater uncertainty with regard to backscattering coefficients. This study suggests that the QAA-GRI is better suited to the retrieval of absorption coefficients from drinking water resources. Our algorithm will also have potential applications to satellite data from the Medium Resolution Imaging Spectrometer or Ocean and Land Color Instrument.

Land use/land cover (LULC) changes create the need for regular monitoring of soil properties. Modern technology and data sources offer possibilities to perform it. In this context, the objective of the study is to explore the possibility of predicting soil organic matter (SOM) content and texture from the spectral information of the three last generation satellites [Landsat-8, Sentinel-2, and the Environmental Mapping and Analysis Program (EnMAP)]. Soil samples (113) were collected in areas affected by wildfires and cropland abandonment in Aragón, Northern Spain. Reflectance spectra of soils were obtained in controlled laboratory conditions using the analytical spectral device Fieldspec4 spectroradiometer (spectral range 350 to 2500 nm). Reflectances simulated for Landsat-8, Sentinel-2, and EnMAP bands were used as predictors in multivariate models developed using partial least squares regression (PLSR) and step-down variable selection algorithm (SD). Modeling of all soil variables was performed simultaneously. The EnMAP models employed few predictors (10 to 58 of 244) and demonstrated good fit (Rcal2<0.8 and Rval2∼0.8), especially for SOM (Rcal2 and Rval2=0.9; RMSEP ∼1.6). Landsat models showed the least reliable estimates (Rcal2 0.54 to 0.77 and Rval2 0.42 to 0.80), whereas Sentinel-2 models showed Rcal2 of 0.70 to 0.81 and Rval2 between 0.67 (clay) and 0.79 (SOM). The results confirm high potential of spectral data from multispectral and hyperspectral satellites for soil monitoring. Application of PLSR combined with SD results in sparser and better-fit models. SOM and soil texture can be estimated with an acceptable accuracy from EnMAP and Sentinel-2 data enabling soil status monitoring in areas of wildfire burns and cropland abandonment.

Convolutional neural network (CNN) models achieve state-of-the-art performance for natural image semantic segmentation. An approach for extracting vegetation from Gaofen-2 (GF-2) remote sensing imagery based on the CNN model is presented. We constructed a convolutional encoder neural networks (CENN) consisting of two layers. The first layer has two sets of convolutional kernels for extracting the features of farmland and woodland, respectively. The second layer consists of two encoders that use nonlinear functions to encode the learned features and map the encoding results to the corresponding category number. In the training stage, samples of farmland, woodland, and other lands are categorically used to train the CENN. After training is accomplished, the CENN would acquire enough ability to accurately extract farmland and woodland from GF-2 imagery. The CENN was trained on 36 GF-2 images and tested on three other GF-2 images. We compared the proposed method to a deep belief network, a fully convolutional network, and a DeepLab model using the same images. The experiments demonstrate that the proposed approach improves upon the accuracy of existing approaches. The average precision, recall, and kappa coefficient of the proposed approach were 0.91, 0.87, and 0.86, respectively. Thus, the proposed approach is proven to effectively extract vegetation from GF-2 imagery.

Mexico’s largest freshwater body, Lake Chapala, is the main source of drinking water for over 4-million inhabitants. This study used satellite and field data to obtain annual estimates of Lake Chapala’s area and water volume from 1973 to 2009, as well as water intakes from 1934 to 2013. Twenty-one Landsat images (multispectral scanner system, 2, 5, and 7) for the period of 1973 to 2009 were processed. Gap filling correction was applied to Landsat 7 enhanced thematic mapper plus scan line corrector-off images. Binary water/no-water segmentation was obtained with a Markov random field model using the normalized difference water index. An area-based model was developed to calculate water volume, which used the lake’s area estimated based on the images as well as an exponential function obtained from area-elevation curves. Three of the resulting water/no-water layers were validated against reference points taken from aerial photography. Overall accuracies of 77% to 90% were obtained. A correlation coefficient of 0.9 resulted when comparing the results from using remote sensing to calculate water volume against field measurements. Given the observed reduction in volume, we concluded that it will be difficult to continue to use the lake as the main source of freshwater for the Guadalajara metropolitan area without substantial interventions.

Soil moisture data obtained by inversion of Fengyun 3B remote sensing data, are widely used in drought monitoring and global climate change research, however, some regional data are missing in this data set, which reduces the application effect. Based on backpropagation neural network (BPNN), we established a filling method and filled the missing area with moderate resolution imaging spectroradiometer (MODIS) inversion products, including land surface temperature, normalized difference vegetation index, and albedo. We named it the multilayer BPNN filling algorithm. The algorithm consists of two neural network layers. The first network layer is used for the spatial scaling of MODIS inversion products, and the second network layer uses the scaling products to further generate soil moisture values. We compared the proposed method to a discrete cosine transform and partial least square (DCT-PLS) and a kriging using the same data set. The experiments demonstrate that our method could obtain good filling results in both homogeneous areas and areas with high data variations, whereas DCT-PLS and kriging could only get good filling results in homogeneous areas.

The Snow and Water Imaging Spectrometer (SWIS) is a science-grade imaging spectrometer designed for CubeSat integration, spanning a 350- to 1700-nm spectral range with 5.7-nm sampling, a 10-degree field-of-view, and 0.3-mrad spatial resolution. The system operates at F  /  1.8, providing the high throughput for low-reflectivity (&lt;1  %  ) water surfaces, while avoiding saturation over bright snow or clouds. The SWIS design utilizes heritage from previously demonstrated instruments on airborne platforms while advancing the state of the art in compact sensors of this kind in terms of size and spectral coverage. Compared with airborne campaigns, the CubeSat platform allows for more frequent and regular sampling, while maintaining intermediate to high resolution relative to heritage global sensors. Through frequent repeat observations from space at a moderate spatial resolution, SWIS can address key science questions concerning aquatic and terrestrial ecosystem changes, cryosphere warming and melt behavior, cloud and atmospheric science, and potential impacts of climate change and human activities on the environment.

Long-term data records obtained from Earth observing sensors depend not only on the calibration accuracy of individual sensors but also on the consistency across instruments and platforms. Hence, sensor calibration intercomparison plays a vital role for a better understanding of various science products. The Moderate Resolution Imaging Spectroradiometer (MODIS) and enhanced thematic mapper plus (ETM+) on the Terra and Landsat 7 platforms have operated successfully since their launch, collecting measurements in the reflective solar and infrared parts of the spectrum. Terra MODIS has employed a reflectance-based calibration since beginning its mission. In the case of ETM+, a radiance-based calibration was employed until recent years, when a reflectance-based calibration was introduced. Being in the AM constellation with less than 30 min difference in overpass times, near-simultaneous Earth scene measurements can be effectively used to assess the calibration differences between the spectrally matching bands of these two instruments. The pseudoinvariant calibration sites (PICS) in the North African desert are widely used for on-orbit calibration and validation of satellite sensors. Four PICS from this region have been employed to assess the multitemporal reflectance differences. Correction for bidirectional reflectance, spectral response function mismatch, and impacts of atmospheric water-vapor have been incorporated to provide an assessment of the long-term stability of each spectral band and reflectance differences amongst them. Results indicate that the spectral bands of both instruments show a long-term stability to within 2% from 2000 to 2017. The top-of-atmosphere reflectances between the two instruments postcorrection agree to within 4%. Also included in this paper is a detailed discussion of various parameters contributing to the uncertainties of this cross-calibration. The techniques presented in this paper can be further extended to perform similar intercomparison between Landsat 8 Operational Land Imager, Aqua MODIS, and Suomi-NPP VIIRS.

The rational function model (RFM) has been widely used in space-borne photogrammetry owing to its simplicity and generality. One of the key issues for using the RFM is to robustly estimate the rational polynomial coefficients (RPCs). However, owing to over-parameterization of the RFM, estimating the RPCs is an ill-posed problem. A feasible method for estimating the RPCs based on singular value decomposition is presented. The presented method can estimate the RPCs directly based on an observation vector and three matrices decomposed from the design matrix. Consequently, the numerical instability of computing the inverse matrix of the ill-conditioned normal matrix can be avoided. Experimental results of the SPOT-5, ZiYuan-3, and GaoFen-2 images show that the presented method can benefit the terrain-dependent and terrain-independent scenarios. Under the terrain-dependent scenario, the influence of the observation errors of actual ground control points (GCPs) can be eliminated effectively and the RPCs can be estimated robustly by selecting a proper regularization parameter and truncating some small singular values. Under the terrain-independent scenario, owing to a very important characteristic that the virtual GCPs do not have observation errors, the presented method can estimate the RPCs robustly even without the selection of the regularization parameter.

Due to the surge in the development of unmanned aerial vehicles (UAVs) and small spacecraft (CubeSats and SmallSats) in recent years, there has been a push to develop miniaturized instrumentation to be incorporated on such platforms. A compact hyperspectral imager integrated with these vehicles provides a cost-effective platform for a range of environmental sensing applications that include the monitoring of vegetation, urban development, and lightning. We present the snapshot hyperspectral imaging system (SNAP-IMS), requiring no scanning and capable of integration with a UAV. The collected hyperspectral data cube is 350  ×  400  ×  55 (x  ,  y  ,  λ) and is acquired within a single camera exposure. The system (288  mm  ×  150  mm  ×  160  mm) weighs 3.6 kg (7.9 lb), and its power consumption is marginal as there are no scanning components. Experimental testing included several flights over an area covered by diverse types of vegetation and man-made structures. Data cubes are recorded at a 1/100 s integration time, which mitigated motion-related artifacts. The low size, mass, and power consumption of the imager can enable longer and higher flights at smaller drone sizes and allow easy, portable spectral imaging. Imaging results and the system description are presented and discussed.

To consistently observe deteriorating air quality over East Asia, the National Institute of Environmental Research, Republic of Korea, is planning to launch an environmental observation sensor, the Geostationary Environment Monitoring Spectrometer (GEMS), onboard the GK-2B satellite (a successor to the GeoKOMPSAT-1) in late 2019. GEMS is a hyperspectral spectrometer that covers the ultraviolet–visible range (300 to 500 nm) with full-width at half-maximum of 0.6 nm. It has been designed for the observation of air pollutants and short-lived climate pollutants. GEMS captures images at hourly intervals in the daytime, alternating with the Geostationary Ocean Color Imager-II every 30 min. Over the Seoul Special Metropolitan area, South Korea, the spatial sampling resolution of GEMS is 3.5  ×  8  km (north–south and east–west, respectively). There are 16 baseline products, including aerosol optical depth and the vertical column density of trace gases such as nitrogen dioxide, sulfur dioxide, formaldehyde, and ozone. Research continues into additional applications (e.g., ground-level concentrations and emissions).

For forward-looking linear-array synthetic aperture radar with high speed, the traditional algorithm neglects its high speed, which leads to poor focusing imaging quality. Due to the high speed movement of the platform, range cell migration (RCM) and two-dimensional spatial variation are more serious. To overcome this problem, an equivalent range model is proposed, which takes the platform’s high speed into consideration. Based on this model, linear range walk correction and second-order keystone transform method are used to eliminate the RCM and range-azimuth coupling, and then a modified nonlinear chirp scaling algorithm based on a circle model is used to improve the focus quality of edge targets. Finally, simulation results are presented to confirm the efficiency of the proposed algorithm.

Processing of multitemporal satellite images generally suffers from uncertainties caused by differences in illumination and observation angles, as well as variation in atmospheric conditions. Moreover, satellite images acquired from different sensors contain not only the uncertainties but also disparate relative spectral response. Given that radiometric calibration and correction of satellite images are difficult without ground measurements during data acquisition, this study addresses pseudoinvariant feature selection for relative radiometric normalization (RRN) that minimizes the radiometric differences among images caused by atmospheric and spectral band inconsistencies during data acquisition. The key to a successful RRN is the selection of pseudoinvariant features (PIFs) among bitemporal images. To select PIFs, multivariate alteration detection (MAD) algorithm is adopted with kernel canonical correlation analysis (KCCA) instead of canonical correlation analysis (CCA). KCCA, which assumes that the relation between at-sensor radiance is spatially nonlinear, can obtain more appropriate PIFs for cross-sensor images than that of CCA, which assumes that the relation between the at-sensor radiances of bitemporal image is spatially linear. In addition, a regularization term is added to the optimization of KCCA to avoid trivial solutions and overfitting. Qualitative and quantitative analyses on bitemporal images acquired by Landsat-7 enhanced thematic mapper plus and Landsat-8 operational and imager sensors were conducted to evaluate the proposed method. The experimental results demonstrate the superiority of the proposed KCCA-based MAD to the CCA-based MAD in terms of PIF selection, particularly for images containing significant cloud covers.

This paper proposes a Bayesian sparse signal recovery algorithm. To improve performance on sparse representation, the log-Laplacian distribution is first defined. With a narrow main lobe and high tail values, it is used as a prior of the sparse signal to model the sparse characteristic. Note that analytical inference of the posterior of the sparse signal is a challenge, because the proposed log-Laplacian prior is not conjugate to the Gaussian likelihood. A maximum a posterior (MAP) estimation-based sparse signal recovery algorithm is further proposed. During the reconstruction of the sparse signal, MAP and maximum likelihood estimation are utilized to estimate the scaling parameter and noise variance, respectively, so as to avoid manual tuning of parameters. Additionally, with the use of the conjugate-gradient algorithm, large matrix inversion is avoided and computational efficiency is improved. Experimental results based on both simulated and measured data validate the effectiveness of the proposed log-Laplacian prior-based sparse signal recovery algorithm. Further, it is applied to micromotion parameters estimation and inverse synthetic aperture radar imaging to confirm its validity.

Cross-sensor compatibility of spectral vegetation indices (VIs) between Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) was investigated using their near-coincident observation pairs obtained along overlapped orbital tracks across the globe for the year 2015. The “top-of-atmosphere (TOA)” and “top-of-canopy (TOC)” normalized difference vegetation indices (NDVIs), TOC-enhanced vegetation index (EVI), and TOC two-band EVI (EVI2) were investigated. For all four VIs, VIIRS and MODIS VIs were subject to systematic differences in which VIIRS VIs were higher than their MODIS counterparts. The overall systematic differences and uncertainties (measured as mean differences and root mean square differences, respectively) were small (0.010 to 0.020 VI units and 0.015 to 0.022 VI units, respectively). TOA NDVI cross-sensor differences were neither seasonally nor view zenith angle dependent, whereas TOC NDVI cross-sensor differences slightly varied seasonally, but were not view zenith angle dependent. TOC EVI and TOC EVI2 cross-sensor differences were view zenith angle dependent, where systematic differences increased with increasing view zenith angle and, for large view zenith angles, they were higher during the summer seasons. These results support the normalization of view zenith angles as a required step to extend the MODIS VI record with VIIRS data.

Region segmentation is a basic and important procedure in region-based classification of remote sensing images. Compared with single-scale segmentation, multiscale segmentation can obtain different structure information and shows good performance in classification. Nonetheless, in the previous multiscale algorithms, different scales are regarded as having equal contributions to classification, thus, multiscale segmentation can contain some unsuitable scales, which affects the classification accuracy. To overcome this drawback and sufficiently utilize structural information, a weighted multiscale region-level sparse representation classification (WMRSRC) algorithm is proposed. In the WMRSRC algorithm, different weights are utilized for the region-level features of different scales. The weights are determined by the segmentation quality, which is evaluated by interregion and intraregion heterogeneity measures (Local Moran’s I and variance, respectively). Once the region weights on different scales are determined, the weighted joint sparse representation model is used to classify the multiscale regions. Through three classification experiments based on high-spatial resolution remote sensing images, we find that the proposed WMRSRC algorithm gives better results than other state-of-the-art algorithms.

Recently, superpixel-based methods have shown promising performance for synthetic aperture radar (SAR) image interpretation. In these methods, the statistical model-based local iterative clustering represents the mainstream of superpixel generation for SAR images. However, errors in the model parameter estimation degrade the accuracy of the model-based distance measure between a pixel and a cluster, which directly affects the performance of superpixel segmentation results. Further, the relative weight between statistical similarity and spatial proximity should be carefully selected to control the balance between boundary adherence and regularity of superpixels. An edge-dominated local clustering method is proposed to overcome these limitations. Edge information is introduced not only to define the dissimilarity of a pixel and a cluster but also to provide an adaptive grid with multiple layers for the initialization of cluster centers. Experiments on simulated and real datasets show that, compared with the previous algorithms using the statistical model-based dissimilarity, the proposed method produces superpixels, which have better edge adherence and stable performance.

Motion compensation (MOCO) is an essential procedure for synthetic aperture radar under a complicated flight path. Wide swath and low-precision navigation data limit traditional MOCO algorithms. We present a space-variant MOCO method to solve two problems: (1) estimation of the deviations of radar during data acquisition and (2) range envelope correction (REC) before range cell migration correction. In the first problem, we apply phase-gradient autofocus with correlate input and weighted total least square jointly. In the second one, the chirp-scaling-based (CS) REC is proposed. Finally, both simulation and real data experiments validate the proposed method.

Aerial images can greatly facilitate rescue efforts and recovery in the aftermath of hurricane disasters. Although supervised classification methods have been successfully applied to aerial imaging for building damage evaluation, their use remains challenging since supervised classifiers have to be trained using a large number of labeled samples, which are not available soon after disasters. However, rapid response is crucial for rescue tasks, which places greater demands on classification methods. To accelerate their deployment, a semisupervised classification method is proposed in this paper using a large number of unlabeled samples and only a few labeled samples that could be rapidly obtained. The proposed approach consists of three steps: segmentation, unsupervised pretraining using convolutional autoencoders (CAE), and supervised fine-tuning using convolutional neural networks (CNN). Leveraging the representation capability of CAE, the learned knowledge from CAE could be transferred to the counterparts of CNN. After pretraining, the CNN classifier is further refined with a few labeled samples to improve feature discrimination. To demonstrate this methodology, a recognition strategy of damaged buildings based on context information using only vertical postevent aerial two-dimensional images is presented in this paper. As a case study, a coastal area affected by the 2012 Sandy hurricane is investigated. Experimental results show that the proposed semisupervised method produces an overall accuracy of 88.3% and obtains an improvement of up to 9% against a CNN classifier trained from scratch.

Hyperspectral unmixing (HU) refers to the process of decomposing the hyperspectral image into a set of endmember spectra and the corresponding set of abundance fractions. Non-negative matrix factorization (NMF) has been widely used in HU. However, most NMF-based unmixing methods have single-decomposition structures, which may have poor performance for highly mixed and ill-conditioned data. We proposed a sparsity-constrained multilayer NMF (MLNMF) method for spectral unmixing of highly mixed data. The MLNMF structure was established by decomposing the abundance matrix layer-by-layer to acquire the endmember matrix and the abundance matrix in the next layer. To reduce the space of solutions, sparsity constraints were added to the multilayer model by incorporating an L₁ regularizer to the abundance matrix in each layer. Moreover, a layerwise strategy based on the Nesterov’s optimal gradient method was also proposed to optimize the multifactor NMF problem. Experiments on both synthetic data and real data demonstrate that our proposed method outperforms several other state-of-art approaches.

Detecting vehicles from satellite images is of great importance for both traffic monitoring and urban planning. However, it is still a challenging task, because vehicles are small and the background is complex. To deal with the issue, an improved morphological reconstruction approach is proposed to model the complex background in satellite images. Bright building regions are first removed by the normalized difference built-up index. Directional filters are next designed to generate the marker image for morphological reconstruction. As the directional filters can match any directional structure of the background, the newly proposed approach has a good ability to eliminate the complex background in satellite images. Finally, vehicles are detected using the Reed–Xiaoli algorithm. The proposed method is applied to real QuickBird images for vehicle detection. The results show that the proposed approach has strong robustness and high efficiency merits and can be used for vehicle detection in city areas.

Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. High-resolution synthetic aperture radar (SAR) sensors are able to detect flood extents in urban areas during both day- and night-time. If obtained in near real time, these flood extents can be used for emergency flood relief management or as observations for assimilation into flood forecasting models. A method for detecting flooding in urban areas using near real-time SAR data is developed and extensively tested under a variety of scenarios involving different flood events and different images. The method uses an SAR simulator in conjunction with LiDAR data of the urban area to predict areas of radar shadow and layover in the image caused by buildings and taller vegetation. Of the urban water pixels visible to the SAR, the flood detection accuracy averaged over the test examples is 83%, with a false alarm rate of 9%. The results indicate that flooding can be detected in the urban area to reasonable accuracy but that this accuracy is limited partly by the SAR’s poor visibility of the urban ground surface due to shadow and layover.

Bistatic inverse synthetic aperture radar (ISAR) can help image the high-speed target for its advantage in tracking and more observation angle to provide more information than monostatic ISAR. However, the complex image geometry makes it difficult to achieve a clear image of the target with geometry distortion correction and calibration. Furthermore, high-speed motion will make the image blurred or defocussed. To address these problems, a bistatic ISAR (B-ISAR) imaging method for high-speed motion target with geometric distortion correction and calibration is proposed. According to the motion decomposition idea, we established the B-ISAR echo model of the high-speed motion target. Then, based on the range Doppler algorithm, we deduce the analytic formula of the geometric distortion factor and calibration factor, and transform the imaging problem into a parameter estimation problem. With the sparsity of the scattering points, the required parameters are solved using the expectation maximization algorithm based on the maximum a posteriori probability criterion. With the estimated parameters, a clear B-ISAR image of a high-speed motion target with geometric correction and calibration is obtained. The simulations show that the proposed method has better resolution and simultaneously attains geometric distortion correction and calibration of the image.

We deal with the unimodular waveform design for cognitive radar in signal-dependent interference (clutter). In addition to the signal-to-clutter-plus-noise ratio (SCNR), we also consider the filtered integrated sidelobe level (ISL) and optimize these two metrics simultaneously. It is assumed that cognitive radar can obtain environment information based on database and prior return. We relate the SCNR and ISL to the signal power spectrum density (PSD) and compare the optimal PSDs for optimizing SCNR and ISL solely. It is found that these two metrics are coupled in a conflicting way. So, the weighted sum of these two metrics is exploited as objective function to optimize the detection performance and pulse compression simultaneously. In order to solve the resulting problem efficiently, we utilize the gradient method and devise a way to employ fast Fourier transform in calculating the gradient of the objective function to accelerate the gradient method. Finally, numerical simulations are performed to demonstrate the superiority of the proposed method over state-of-the-art method in terms of the output SCNR, filtered ISL, and computational efficiency.

Unmanned aerial vehicles (UAV) are a useful supplement to traditional synthetic aperture radar (SAR) platforms. In some cases, UAV-based SAR systems have to fly at low altitude. In this case, range-dependent phase errors due to platform motion affect the imaging quality. To solve the problem of motion compensation, an angle-dependent model and a second-order range-dependent model are introduced into autofocusing by previous researchers, but the first one relies too much on the geometric angle while the latter has limited fitting order for solution. We present a higher order range-dependent model, which can approximate analytical solution. Nevertheless, an increase in the fitting order makes the matrix in this model underdetermined. Based on the theoretical proof, this higher order model can be tackled by exploitation of compressive sensing (CS) theory. A CS reconstruction of higher order fitting coefficients is performed in the experiments, and corresponding performance analysis is given. Finally, the range-dependent phase error is compensated under the condition of low altitude.

The hyperspectral unmixing technique is of great significance for hyperspectral data analysis. The non-negative matrix factorization (NMF)-based method is one of the most popular unmixing methods. However, the sparse constraints commonly used in NMF methods are not differentiable at zero, which affects the stability and accuracy of the results. In addition, sparse constraints are supposed to have endmember departure limitations. An improved sparse NMF algorithm is proposed. First, a biased L_{1  /  2} sparse constraint is used to effectively explore the sparsity of abundances, which can also have a sparse effect under the condition of the abundance sum-to-one constraint. Second, to alleviate the endmember departure effect, a minimum energy difference constraint is introduced. Simulated and real-data experiments verify the advantages of the proposed method against state-of-the-art sparse NMF methods.

The iterative shrinkage/thresholding regularization method is an efficient and robust approach to implement super-resolution imaging for forward-looking scanning radar (FLSR). Nevertheless, numerous iterations are normally required to obtain a high super-resolution ratio and thus it can be time consuming. A vector extrapolation accelerated iterative shrinkage/thresholding regularization method is proposed for FLSR. In order to achieve effective acceleration, a predicted point is particularly formulated by linearly extrapolating the historical iterates before conducting the iterative shrinkage/thresholding operation. To reduce the prediction error and stabilize the acceleration process, an adaptively adjusted acceleration step size is derived according to the similarity of the adjacent iterative vectors. Last, a fixed-threshold constraining operator is conducted on the step size to guarantee the convergence of the acceleration method. The results of simulation experiment and real data processing verify the advantage of the proposed method in both the convergence speed and super-resolution performance when compared with the traditional acceleration methods.

A total variation (TV) regularization-based compressed sensing (CS) synthetic aperture radar (SAR) imaging algorithm is proposed. Using chirp scaling algorithm as the approximation observation operator and taking TV minimization as the magnitude sparse constraint, an optimization model is designed by separating the magnitude from complex-valued scene. The reconstruction of the SAR image is formulated as a double-parameter joint optimization problem, which is solved by the second-order convex approximation and block coordinate descent approach. Compared with matching filtering method, TV-based image denoising method, and approximation observation-based CS method, the proposed algorithm can reconstruct both sparse and nonsparse scenes with higher accuracy using downsampling raw data. The experimental results via real data validate the effectiveness of the proposed method.

Relative radiometric normalization (RRN) of remotely sensed images is often a preprocessing step during time series analysis and change detection. Conventional RRN methods may lessen the radiation difference of changed pixels in images during the RRN process, thus reducing the accuracy of change detection. To solve this problem, we propose a relative radiometric correction method based on wavelet transform and iteratively reweighted multivariate alteration detection (IR-MAD). A wavelet transform is applied to separate high and low frequency components of both the target image and reference image. The high frequency components remain unprocessed to preserve high frequency information. We use the IR-MAD algorithm to normalize the low frequency component of the target image. A reverse wavelet transform reconstructs the radiometrically normalized image. We tested the proposed method with traditional histogram matching, mean variance, the original IR-MAD method, and a method combining wavelet transform and low-pass filtering, and change detection was conducted to evaluate the RRN quality. The experiments show that the proposed method can not only effectively eliminate the overall radiation difference between images but also enable higher accuracy of change detection.

Light detection and ranging (LiDAR) technology can provide accurate data for the vertical structure of a forest. Such data are fundamental for the study of aboveground biomass in the global carbon cycle. Most studies to date have used Geoscience Laser Altimetry System (GLAS) waveforms and terrain parameters as raw data in creating forest canopy height estimation models. Whether other parameters can be introduced to improve the accuracy of these models remain to be studied. In this study, we introduce parameters for forest texture to augment the topographic index model. Texture parameters were combined with field observations of canopy height to increase the accuracy of canopy height estimates over sloping terrain in the Lushuihe Forest District, a typical forest in the Changbai Mountains, Jilin Province, China. The study area consists mainly of broadleaf forest, coniferous forest, and mixed forest. Fifty-five sample plots covering different terrain reliefs and forest types were selected. The study areas were divided into high and low relief areas (i.e., hills and plains) using four different terrain relief thresholds (5, 10, 15, and 20 m) to select the best partition value. The results showed that the most accurate estimate of canopy height was given by the improved model using a 20-m threshold. Compared with the baseline topographic index model, R² for leave-one-out cross-validation between the estimates given by the improved model and observed data increased from 0.777 to 0.952 and RMSE decreased from 2.90 to 1.35 m. The increased accuracy of canopy height estimates is due to the correction of GLAS waveform parameters by introducing texture parameters to incorporate changes in canopy structure because of forest aging and different forest types.

With the advent of high-resolution remote sensing images, automatic building extraction methods play a more important role in rapidly acquiring information about large-scale buildings. Although advanced building extraction methods have been introduced to improve building extraction results, these methods involve complex processing and high-computation times. We put forward an effective method to extract building information, based on a proposed spectral building index. The basic idea of the spectral building index is to generate an optimized index based on the computation and analysis of spectral bands, which are beneficial for image enhancement for buildings in images. Aiming at the band number of the multispectral satellite images in high-resolution remote sensing images, we propose two spectral indices for building extraction, including the normalized spectral building index (NSBI) and the difference spectral building index (DSBI). Considering the current spectral band number of high-resolution satellite images, NSBI is suited for satellite images with eight spectral bands, whereas DSBI is suited for satellite images with four spectral bands. The proposed method is validated on various high-resolution images including WorldView-2, GF-1, GF-2, and QuickBird images with 13 experiment datasets, as well as a detailed comparison to the state-of-the-art methods, such as the morphological building index, nonhomogeneous feature difference, and building condition index. The experimental results reveal that the proposed method can achieve promising results for different building conditions, such as regular and irregular building shapes and concrete and metal roofing materials. The average overall accuracy was over 85% with low-time consumption (&lt;1  s).

The frequency of extreme weather events in China has been increasing due to global warming. The variety of soil types makes for varying levels of water retention capacity across Southwest China. However, spatially detailed information about soil water retention capacity is often difficult to obtain on a large scale. It has been hypothesized that agricultural drought is directly related to evapotranspiration fluxes and soil water retention capacity. This paper presents the relative water supply capacity index (RWSI), which is based on the integrated standardized precipitation evapotranspiration index (SPEI) and vegetation condition index (VCI) to explore the potential of using the temporal moderate resolution imaging spectroradiometer archive and meteorological data collected from stations in Southwest China to estimate the soil water retention capacity. The results showed that the spatial pattern of RWSI correlated well with that of the soil clay content in Southwest China, which suggests that RWSI could reflect the variations in relative soil water retention capacity in the research region. The RWSI was significantly correlated with relative soil moisture at depths of 0 to 10 cm and 10 to 20 cm. These data showed that RWSI could be used to examine and distinguish the relative differences in soil water retention capacity with an integrated implication of VCI and SPEI in Southwest China.

Spatial data on vegetation dynamics are sparse for the Djerid oases of Tunisia, but such data are urgently needed by policy makers for natural resource management purposes. These data can be collected by remote sensing and analyzed using Geographic Information System software. We analyzed the changing dynamics of the Tozeur oases in southwestern Tunisia using normalized difference vegetation index (NDVI) index data that were generated from SPOT-5 (Take5) satellite imagery taken from April to September, 2015. We used agglomerative hierarchical clustering (AHC) to produce a dendrogram that segmented the area into similar NDVI classes, then analyzed these clusters with reference to ground-truth data collected by field surveys. The unsupervised classification map produced by AHC represents a spatial model of the NDVI distribution in the oases. The results revealed seven different clusters with very high spatial heterogeneity that were linked to biophysical parameters in the field.

Many indices based on MODIS data are used to monitor the process of agricultural drought, such as apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI). Notable differences in performance and geographic predictions exist among these indices. We statistically evaluated the performance of different drought indices for a known drought process in 2014 in the typical rainfed maize region of Songliao Plain, China, using a linear regression model based on the relationships between indices and soil moisture data. Our results show that during the growth season of May to September, the indices performed independently with changing curves, particularly in different phenological periods. By contrast, correlations tended to be higher for ATI than for other indices in the early vegetative growth stage, whereas small differences were detected among the other indices in the late vegetative to late reproductive growth stages. Our results confirm that the TVDI can be the best choice to detect agricultural drought in the study area.

Net radiation (Rn) is a critical variable in estimating energy balance and evapotranspiration by remote sensing techniques on a regional scale. The objective of this paper is to evaluate the effect of estimated and measured instantaneous solar radiation (Rg) and the atmospheric correction of surface temperature by Landsat 8 images on the estimated surface Rn in northern Pantanal. Rn and meteorological factors were measured on a floodplain and on two nonflooded areas in northern Pantanal. Rg was estimated by models proposed by Zillman et al. (Rg_Zillman) and Allen et al. (Rg_Allen). Surface temperature was corrected by atmospheric and emissivity effects. Annual and seasonal averages of solar radiation were not different among the experimental sites. The floodplain area featured higher net radiation than both nonflooded areas. Instantaneous Rn_inst estimated by measured Rg and with Rg_Allen were not different from measured Rn_inst. Rn_inst estimated by surface temperature was, on average, 5.7% higher, with a 22% error lower than Rn_inst estimated with brightness temperature. The current study highlights the possibility of estimating Rn_inst by Rg_Allen and the need for correcting the effect of the atmosphere and surface emissivity to estimate surface temperature and Rn_inst.

An attempt has been made to retrieve columnar density of methane (XCH₄) over a suburban area of the Shadnagar in India from the high spatial and moderate spectral resolution (5  nm  ±  0.5  nm) airborne visible/infrared imaging spectrometer next generation (AVIRIS-NG) data. The data collected were part of the joint airborne campaign by National Aeronautics Space Administration/Jet Propulsion Laboratory and Indian Space Research Organisation over several Indian sites during December 2015 to January 2016. The XCH₄ was retrieved following the Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor retrieval algorithm and was compared with the ground-based Fourier transform infrared (FTIR) observations. FTIR measured CH₄ transmittance was fitted against Fast Atmospheric Signature Code3 (FASCODE3) simulated transmittance, and found their residual lies within ±1  %  , which shows a very good retrieval sensitivity of FTIR. Hence FTIR observations are used for the validation of AVIRIS-NG derived XCH₄ and found in good agreement. In the present study, we utilized airborne imaging radiance data in the CH₄ absorbing channel at 2.365  ±  0.006  μm and nonabsorbing channels at 2.325  ±  0.006  μm and 2.365  ±  0.006  μm to compute atmospheric transmittance spectrum. Using a line-by-line radiative transfer algorithm, the XCH₄ was estimated from AVIRIS-NG transmittance data. Prior to the application of the MODIS water vapor algorithm on AVIRIS-NG transmittance, it was also compared with the FASCODE3 simulated transmittance and found comparable. Study revealed that within the Shadnagar region, retrieved XCH₄ varies little to moderate with an average of 9  ×  10²⁷  molecules cm^−  2 with the highest value of 12  ×  10²⁷  molecules cm^−  2 near the source region of the agriculture field. The study demonstrates the capability of AVIRIS-NG to quantify the column density of atmospheric species such as CH₄ over a region.

To date, remote sensing-based algorithms for inferring urban surface evapotranspiration (ET) remain little studied. Based on the modifications of the remote sensing Penman–Monteith (RS-PM) model, we propose an urban RS-PM model for estimating urban surface ET. Compared with the traditional RS-PM model, our urban RS-PM model is specifically developed for urban areas and is characterized by the following improvements: (1) excluding the interference of impervious surface components in urban areas by replacing the vegetation cover fraction index with land surface component fraction parameters inversed through linear spectral mixture analysis for calculating the area proportions of vegetation and soil; (2) considering the effect of the component fractions of vegetation or soil on all energy components of the surface energy balance by applying the modified multisource parallel model for estimating the component latent heat flux; and (3) optimizing the calculation of the component net radiation flux by considering the component surface characteristics. This urban RS-PM model was tested on an urban area of Xuzhou in the eastern Chinese province of Jiangsu. Landsat 8 operational land imager and thermal infrared sensor satellite images acquired between 2014 and 2016, together with their corresponding meteorological data and flux observation data, were used for estimating the ET of the study area for eight dates with the model. The results were validated by the latent heat flux data observed by an open path eddy covariance system. Validation shows the goodness of fit (R²), the root-mean-square error, the mean relative error, and the correlation coefficient (r) between estimated ET and observed ET for the eight dates were 0.8965, 24.14  W  ·  m^−  2, 18.5%, and 0.9546, respectively. The results prove that the urban RS-PM model is effective in estimating ET of urban areas with an acceptable accuracy.

Dynamic and static characteristics of nonstationary radiation fields are studied. The system on-board of an unmanned aerial vehicle is used for mapping of certain heavily contaminated areas of the Chornobyl nuclear reactor zone. Sources of ionizing radiation under uncertain and unfavorable conditions, with limited time of observation and measurement, have been measured in real time in one single process with a spatial resolution of around 0.5 m and a surface contamination sensitivity of 0.5  kBq  ·  m^−  2. Small radioactive spots in an undulating forested area abundantly contaminated with high intensity radioactive isotopes have been plotted on a map.

Image fusion is an important technique for enhancing the spatial resolution of multispectral imagery by merging with a corresponding high-resolution panchromatic image. Many algorithms have been developed to obtain a better trade-off between spectral fidelity and spatial resolution enhancement. The recently developed techniques use sophisticated algorithms; however, three issues are often ignored. The first involves the progressively increasing amount of high-resolution satellite image data. Second, the designed method needs to be versatile to process a variety of complex terrain and surface features. The third is that the proposed method must satisfy the requirements of both visual inspection and classification. Based on the previous studies, this work proposes a generalized intensity–hue–saturation and Brovey transform framework (GF) to tackle these three problems. Eight modules are integrated into this GF: four modules are designed for classification with a new intensity matching technique to improve the spectral quality; the other four modules are the spatial-enhancement version for visual inspection by adding more native spatial details. To verify the proposed technique, the experiments are conducted for QuickBird and WorldView-2 images. The results show that the GF modules can offer the maximal spatial information content while preserving good spectral information quality.

This study compares the estimations of biophysical parameters of Platanus hispanica urban trees, namely total height, crown height, crown volume, and the amount of residual biomass from pruning, obtained by terrestrial laser scanner (TLS), airborne laser scanner (ALS) of low density (0.7  points  ·  m^−  2), and measured by standard field methods. Regression models were calculated to obtain the relationships among parameters retrieved by all techniques, testing all possible combinations (manual-TLS, manual-ALS, TLS-ALS, and vice versa). The most accurate fits were found for vegetation attributes (stem and crown diameter) estimated by TLS and ALS data with R² between 0.84 and 0.96, respectively. The least accurate models were found when crown height and pruning biomass were estimated from ALS data (R²  =  0.68 and R²  =  0.59, respectively). The methods reported in this research might be of interest for the management of urban forests to study residual biomass calculation, sink CO₂, the influence of humidity and of shadow areas whatever the information capture system used, whether it is derived from ALS, TLS, or classical dendrometry measurements.

The hyperspectral remote sensor acquires hundreds of contiguous spectral images, resulting in large data that contain a significant amount of redundant information. This high-dimensional and redundant data always influence the efficiency of the data processing. Therefore, feature extraction becomes one of the critical tasks in hyperspectral image classification. A transform-domain-based feature extraction technique, three-dimensional discrete cosine transform (3-D DCT), is proposed. The reason behind the transform domains is that, generally, an invertible linear transform reconstructs the image data to provide the independent information about the spectra or more separable transformation coefficients. Moreover, DCT has excellent energy compaction properties for highly correlated images, such as hyperspectral images, which reduces the complexity of the separation significantly. Unlike the discrete wavelet transform that requires sequential transform to obtain the approximation and detailed coefficients, DCT extracts all coefficients simultaneously. As a result, computation time in the feature extraction can be reduced. The experimental results on three benchmark datasets (Indian Pines, Pavia University, and Salinas) show that the proposed approach produces a good classification in terms of overall accuracy, average accuracy as well as Cohen’s kappa coefficient (κ) when compared with some traditional as well as transform-based feature extraction algorithms. Experimental result also shows that the proposed method requires less computational time than the transform-based feature extraction method.

Owing to a large number of spectral bands, it is always a challenge to devise an optimal visualization method for hyperspectral images. An algorithm must maintain a balance between dimensionality reduction and restoration of maximum spectral information. A methodology for visualization of hyperspectral imagery is proposed based on extraction of salient regions. For that, spectral bands are selected from different combinations of principal component analysis, minimum noise fraction, and saliency maps. A hierarchical fusion method is proposed, which is applied on the selected bands to obtain a final three band RGB image. The qualitative and quantitative results of the proposed method are very encouraging once compared with other state-of-the-art methods.

We present further development of the empirical orthogonal functions (EOF)-based retrieval algorithm. The algorithm output is a regression formula that relates principal components of the reflected sunlight spectra with CO₂ total column amount. The algorithm was implemented and tested for the observations from the Japanese satellite Greenhouse gases Observing Satellite (GOSAT). Training of the EOF-based algorithm with the collocated ground-based and space-borne data (e.g., Total Carbon Column Observing Network and GOSAT observations, respectively) was shown to impose some errors that were interpreted as a result of implicit averaging over the collocation area. Alternative training with the small subset (∼5  %   to 10%) of the full-physics algorithm is free of such errors; however, this option requires additional filtering of the space-borne observations that are strongly affected by atmospheric light scattering. This filtering was implemented by the comparison of the EOF-regression estimates of surface pressure with corresponding meteorological data.

Inspired by the idea of multiantenna interferometric processing in interferometric inverse synthetic aperture radar, based on monostatic low-resolution narrowband radar with three antennas, a three-dimensional (3-D) interferometric imaging and micromotion feature extraction method for spinning space debris is proposed. First, the micromotion characteristic of the target in low-resolution radar is explained. Next, by taking advantage of the target’s micro-Doppler characteristic on time-frequency (TF) domain, the time-varying two-dimensional imaging of scatterers in both the normal mode and the squint mode is achieved through interferometric processing. Finally, the height information of scatterers is solved by an ellipse fitting-based method; thus 3-D imaging for the spinning space debris is realized. Meanwhile, some 3-D micromotion features are exactly extracted during imaging, and the relevant issues, including window width determination of TF analysis, scatterer separation, and noise suppression are discussed in detail. Simulation results in diverse occasions validate the effectiveness and the robustness of the method.

A channel-estimation (CE) scheme is proposed to estimate the complex amplitude, Doppler shift, angle-of-departure, and angle-of-arrival of the channel taps for sparse and doubly selective channels for hyperspectral image transmission from unmanned aircraft vehicles (UAVs) to ground stations. The proposed method is dubbed as compressed-sensing joint parameter estimation (CS-JPE) and finds the channel parameters matrix by employing a compressed-sensing (CS)-based method. Afterward, a modified version of the joint parameter estimation (JPE) is proposed as CS-JPE and is dubbed as M-CS-JPE, which employs the elevation-azimuth angles of the line-of-sight channel tap to estimate the channel parameters with higher accuracy and lower computational complexity compared to the CS-JPE scheme. For higher accuracy of the M-CS-JPE, an elevation-azimuth angle estimation is proposed and is dubbed as fractal-structure-array since it uses a fractal structure for the placement of the UAV antennas. The performance of the CE methods is appraised by simulating transmission of AVIRIS hyperspectral data via the communication channel and evaluating their accuracy for the classification after demodulation. Compared to the least-square method, the simulation results indicate up to 30-dB figure of merit in the bit-error-rate and 10 times improvement in the hyperspectral image classification fidelity.

The presence of a significant amount of information in the hyperspectral image makes it suitable for numerous applications. However, extraction of the suitable and informative features from the high-dimensional data is a tedious task. A feature extraction technique using expectation–maximization (EM) clustering and weighted average fusion technique is proposed. Bhattacharya distance measure is used for computing the distance among all the spectral bands. With this distance information, the spectral bands are grouped into the clusters by employing the EM clustering method. The EM algorithm automatically converges to an optimum number of clusters, thereby specifying the absence of need for the required number of clusters. The bands in each cluster are fused together applying the weighted average fusion method. The weight of each band is calculated on the basis of the criteria of minimizing the distance inside the cluster and maximizing the distance among the different clusters. The fused bands from each cluster are then considered as the extracted features. These features are used to train the support vector machine for classification of the hyperspectral image. The performance of the proposed technique has been validated against three small-size standard bench-mark datasets, Indian Pines, Pavia University, Salinas, and one large-size dataset, Botswana. The proposed method achieves an overall accuracy (OA) of 92.19%, 94.10%, 93.96%, and 84.92% for Indian Pines, Pavia University, Salinas, and Botswana datasets, respectively. The experimental results prove that the proposed technique attains significant classification performance in terms of the OA, average accuracy, and Cohen’s kappa coefficient (k) when compared to the other competing methods.

The radiation characteristics of a target change when a microstructured surface is used. Temperature and infrared (IR) radiation models are established for a flying spatial target with a microstructured surface. A simultaneous comparison was implemented for the temperature distribution and IR radiation characteristics of targets with normal and microstructured surfaces. This investigation was focused on the effects of detection angles on the IR radiation characteristics of a target with a microstructured surface. The results indicate that the utilization of a microstructured surface reduces the IR radiance of the spatial target, and a sudden change in the detection capacity was found for different detection angles, which is significant for the study of radiation characteristics of a target with a microstructured surface.

Mapping plastic-mulched landcover (PML) is an important agricultural monitoring task. Because of its daily revisiting, imagery from moderate-resolution imaging spectroradiometer (MODIS) has been widely used to detect PML over a large area. However, the coarse spatial resolution and small field size make subpixel PML a problem for accurate PML mapping from MODIS. This study applies the improved spatial attraction model (ISAM), which estimates the spatial attraction of the central subpixel of a moving window by all subpixels in the window, to map large-scale subpixel PML from MODIS imagery. The linear spectral mixing model is used to obtain fractions of PML and three other landcover classes in each MODIS pixel as the inputs to ISAM for obtaining the hard subpixel PML maps at spatial resolution of 31.25 m. The accuracy evaluation, with validated landcover classification derived from Landsat-8 imagery as the ground truth, shows that overall accuracy, Kappa coefficients, producer accuracy, and user accuracy are 83.51%, 0.69, 90.97%, and 81.51%, respectively, indicating large-scale PML mapping at spatial resolution comparable to Landsat-8 can be derived from MODIS images with acceptable accuracy by ISAM. This study provides a practical and economic way for mapping PML for a large area at ∼30-m spatial resolution.

Solving the problem of building extraction from remote sensing images, which have high spatial resolution, is considered to be one of the most challenging issues in the field of photogrammetry science and remote sensing. The purpose of this study is to present an innovative algorithm, named adaptive bilateral filter (ABF) + segment-based neural network, which is based on the fusion of deep convolutional neural networks (DCNNs) and adaptive ABF and has resulted in improvements in the accuracy of building extraction from remote sensing images with high spatial resolution. The building extraction process in this study includes the following steps: applying the ABF to the research data set and optimizing its parameters in order to improve the building outlines, designing, and training the DCNN, SegNet, based on the improved data set and optimizing it using an adaptive moment estimation algorithm and assessing the impact of applying the ABF + SegNet algorithm to automatic building outline extraction. The proposed algorithm in this study is tested on three sets of remote sensing data from the cities of Potsdam, Indianapolis, and Tehran. The results indicate that the ABF + SegNet algorithm is able to extract the buildings from remote sensing color images with suitable accuracy.

Accurate measurements of retreat and progressive lowering of ice shelves are critical for estimating the mass balance of the Antarctic ice sheet under projected warming. Ice shelf extent was extracted from declassified aerial photographs and modern satellite images (Landsat and Moderate Resolution Imaging Spectroradiometer imagery), and a 48-year time series of the areal extents of the Larsen A ice shelf (LAIS) and the Larsen B ice shelf (LBIS) was compiled. In addition, we characterize the surface elevation changes of the LAIS and LBIS over the last two decades by combining the Ocean Topography Experiment/Poseidon and the Envisat Radar Altimeter-2 using the collinear analysis method. The Larsen ice shelf displayed no significant changes until the late 1980s, whereas the LAIS has retreated rapidly since 1986, and the LBIS has followed a similar pattern since the early 1990s. The LAIS and LBIS have already diminished by ∼14,000  km² in total since 1968. As the ice shelves retreated, the surface elevations of the LAIS and LBIS exhibited progressive lowering from 1992 to 2010, with the lowering rate of the LAIS significantly greater than that of the LBIS. The remaining ice of the LAIS was lowered at a rate of 0.45  m a^−  1 from 1992 to 2001, and the remnant LBIS was lowered at a rate of 0.07  m a^−  1 from 1992 to 2010. In response to the continuous retreat of the LAIS and LBIS, their surface elevations are very sensitive to the collapse and retreat of the ice shelves.

Mapping landcover in cities is essential for urban ecology and landuse management, yet urban landcover is often highly heterogeneous at fine spatial scales. Pixel-based approaches are shown to be less successful for effectively mapping urban landcover due to high heterogeneity, with relatively low accuracies reported despite the use of high spatial resolution optical imagery. Alternatively, geographic object-based image analysis (GEOBIA) has yielded higher accuracies across a range of urban applications. We combine three-dimensional (3-D) information from airborne laser scanning (ALS) data with RapidEye high-spatial-resolution imagery in a GEOBIA approach to classify urban landcover in a large metropolitan region in Vancouver, Canada. Results indicate that 12 urban classes could be accurately mapped at 2-m spatial resolution across 150,000 ha with an overall accuracy of 88% (kappa 0.87). Though 5-m RapidEye multispectral pixels were often mixed in heterogeneous urban areas, the additional insight provided by the 3-D ALS information enabled accurate classification of fine spatial objects such as street trees and single-family dwellings.

Liquid water content (LWC) and cloud droplet effective size (CDES) are important factors affecting atmospheric radiative transfer, and measurement of these parameters in clouds is essential. For homogeneous liquid cloud (constant extinction coefficient) with a gamma size distribution of cloud droplets, we find that LWC and CDES can be retrieved from two parameters obtained from a multiple-field-of-view (MFOV) Lidar: the intercept of the range-corrected Lidar signal (IRCLS) and the slope of the range-corrected Lidar signal (SRCLS) at different sizes of FOV. Monte Carlo simulations reveal that IRCLS at different sizes of FOV varies with both extinction coefficient and CDES while SRCLS varies only with the extinction coefficient, which depends on both LWC and CDES. This means that, after extracting the extinction coefficient using SRCLS, we can easily obtain CDES from IRCLS, and LWC can then be determined using the extinction coefficient. An innovative MFOV Lidar system is constructed to measure the LWC and CDES. A series of experiments is conducted in the northern suburb of Nanjing, China, and the LWC and CDES of homogeneous liquid cloud are obtained. Comparisons among results from the MFOV Lidar, theoretical calculation, and the global precipitation measurement satellite verify our proposed method.

Very high resolution canopy cover maps of spekboom are required to assist with the restoration of degraded habitat in the Little Karoo, a large semiarid region in South Africa. Variations in habitat and level of degradation, in addition to radiometric variations in the imagery, make spekboom mapping at a regional scale a challenging problem. We present a per-pixel classification approach for canopy cover mapping of spekboom using multispectral 0.5-m resolution aerial imagery. The imagery is radiometrically homogenized with a technique that uses satellite data to convert digital numbers to estimated surface reflectance values. A feature selection procedure that is robust to redundancy is applied in order to select an informative feature subset from a typical set of spectral, textural, and vegetation index features. Support vector machine, random forest, decision tree, k-nearest neighbor, and Bayes normal classifiers are evaluated against labeled pixel data and canopy cover ground truth acquired at 20 field sites. The results show that all the classifiers, except the Bayes normal classifier, perform well. The decision tree produces the best results (mean absolute canopy cover error of 5.85% with a standard deviation of 4.65%).

The detailed area and spatial distribution of irrigated and rainfed wheat can help forecast wheat yield and study water use efficiency. However, the similar spectral characteristics of irrigated and rainfed wheat make it difficult to separate them with low-spatial resolution or several high-spatial resolution images on the high heterogeneity of the southern Loess Plateau. To solve this challenge, this study used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced STARFM (ESTARFM) to generate time series of the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) at a 30-m resolution by fusing Moderate Resolution Imaging Spectroradiometer and Landsat data. Then, the phenological feature extracted from the predicted NDVI is combined with an auxiliary dataset to classify irrigated and rainfed wheat using the support vector machine classifier. An overall classification accuracy of 93.7% and a Kappa coefficient of 0.91 are achieved. Compared with corresponding high-resolution Google Earth images, the spatial distribution of the classification was consistent with actual land cover. This study demonstrates that the classification approach could classify irrigated and rainfed wheat in high heterogeneity regions and crops with smaller spectral characteristic differences. Moreover, it could be implemented across larger geographic regions.

Agricultural land is strongly involved in the provision of ecosystem services given its spatial extent and impacts of agriculture on ecosystem functioning. Therefore, mapping ecosystem services require spatially explicit data on agricultural land uses. However, available data on agricultural land use usually have coarse spatial and temporal resolution, thus contributing significantly to uncertainties in ecosystem services quantification. Our goal is to develop an approach that produces high-resolution data on agricultural land uses. We propose a remote sensing approach using freely available MODIS images coupled with RapidEye images. Based on the phenological specificities of the different crops, we produced annual maps of agricultural land uses. We tested this approach over the spatially heterogeneous and mountainous region of Grenoble, France, where we mapped agricultural successions over 5 years. Maps produced at the scale of the agricultural parcel describe agricultural land uses through 17 classes. Our results suggest that coupling MODIS and RapidEye data can be used to map agricultural land use at high temporal and spatial resolution even in complex mountain landscapes. The comparison of the agricultural production service estimated by our method and from the Corine Land Cover database highlights the value of incorporating interannual variability in agricultural land use. This will also be facilitated in the future by the availability of new products from Sentinel 2.

Driven by the great success of deep convolutional neural networks (CNNs) that are currently used by quite a few computer vision applications, we extend the usability of visual-based CNNs into the synthetic aperture radar (SAR) data domain without employing transfer learning. Our SAR automatic target recognition (ATR) architecture efficiently extends the pretrained Visual Geometry Group CNN from the visual domain into the X-band SAR data domain by clustering its neuron layers, bridging the visual—SAR modality gap by fusing the features extracted from the hidden layers, and by employing a local feature matching scheme. Trials on the moving and stationary target acquisition dataset under various setups and nuisances demonstrate a highly appealing ATR performance gaining 100% and 99.79% in the 3-class and 10-class ATR problem, respectively. We also confirm the validity, robustness, and conceptual coherence of the proposed method by extending it to several state-of-the-art CNNs and commonly used local feature similarity/match metrics.

Accurate estimates of forest biomass are essential for several purposes, ranging from carbon accounting and ecological applications to sustainable forest management. There are, however, critical steps for mapping aboveground forest biomass (AGB) based on optical satellite data with an acceptable degree of accuracy, such as selecting the proper statistical modeling method and deriving spectral information from imagery, at known field locations. We compare nine modeling techniques including parametric, semiparametric, and nonparametric methods for remotely estimating AGB based on various spectral variables derived from Landsat 8 Operational Land Imager (OLI). We conduct this research in Zagros oak forests on two sites under different human disturbance levels: an undegraded (UD) forest site and a highly degraded (HD) forest site. Based on cross-validation statistics, the UD site exhibited better results than the HD site. Support vector machine (SVM) and Cubist regression (CR) were more precise in terms of coefficient of determination (R²), root-mean-square error (RMSE), and mean absolute error (MAE), though these approaches also result in more biased estimates compared to the other methods. Our findings reveal that if the degree of under- or over-estimation is not problematic, then SVM and CR are good modeling options (R²  =  0.73; RMSE  =  31.5  %   of the mean, and MAE  =  3.93  ton  /  ha), otherwise, the other modeling methods such as linear model, k-nearest neighbor, boosted regression trees, generalized additive model, and random forest may be better choices. Overall, our work indicates that the use of freely available Landsat 8 OLI and proper statistical modeling methods is a time- and cost-effective approach for accurate AGB estimates in Zagros oak forests.

Although many studies exist on the estimation and monitoring of above-ground biomass (AGB) of forest ecosystems by methods of remote sensing, very little research has been carried out for ecosystems of low primary production, such as grasslands, steppes, or savannas. Our study intends to approach this gap and investigates the correlation between space-borne radar information and AGB at the scale of 10 tons per hectare and below. Additionally, we introduce the integration of passive brightness temperature as an additional covariate for biomass estimation, based on the hypothesis that it contains information complementary to microwave backscatter of the active sensors. Our findings show that large-scale estimates of AGB can be conducted for grasslands and savannas at high accuracy (R² up to 0.52). Additionally, we found that the integration of passive radar can increase the quality of AGB estimates in terms of explained variance for selected cases. We hope that these indications are a starting point for more integrated approaches toward biomass estimations based on Earth observation methods.

The remote estimation of foliar nitrogen (N) is key in mapping and monitoring ecosystem health globally. Hyperspectral remote estimation of foliar N has mainly been via its relationship with chlorophyll content. However, the application of broadband remote sensing in foliar N estimation has been receiving increasing attention. The broadband estimation of N provides opportunities for landscape level estimation of N due to the wider spatial coverage. To this end, several studies have tested the potential applications of broadband sensors in estimating N. These were mainly focused on the near-infrared region. Our study was carried out in miombo woodlands, one of the most extensive woodland types in southern Africa. A bootstrapped random forest regression technique, in the R environment, was used for testing for significance in differences in accuracies of chlorophyll and structural indices when estimating foliar N. Our results showed a significant difference between the root mean square error of prediction obtained when chlorophyll indices (mean  =  0.52  %  ) and structural indices (mean  =  0.57  %  ) are used for predicting foliar N. We conclude that the chlorophyll indices predict foliar N significantly better than structural indices. This finding is significant in the estimation of foliar N at the landscape level.

Quantitative estimation of wetland aboveground biomass (AGB) is an essential aspect in evaluating the health and conservation of this valuable ecosystem. We combine AGB field measurements and remote sensing data to establish a suitable model for estimating wetland AGB in the Poyang Lake National Nature Reserve (PLNNR), which is included in the Ramsar Convention’s List of Wetlands of International Importance. All field sampling points cover four dominant vegetation communities (Carex cinerascen, Phalaris arundinacea, Artemisia selengensis, and Miscanthus sacchariflorus) in the PLNNR. Wetland AGB is retrieved from the Landsat-8 OLI image. To improve the accuracy of wetland AGB estimation, we compare the performances of three machine learning algorithms, namely, random forest (RF), back-propagation neural network (BPNN), and support vector regression (SVR), with linear regression (LR) in estimating the AGB in the PLNNR. Results are as follows: (1) the RF model with a root-mean-square error of 0.25  kg m^−  2 performs better than BPNN (0.29  kg m^−  2), SVR (0.27  kg m^−  2), and LR (0.31  kg m^−  2) in our testing dataset, and AGB density in the PLNNR is between 0 and 1.973  kg m^−  2. (2) The four most important features for AGB modeling are near-infrared, short-wave infrared 1 band, enhanced vegetation index, and red band. Our study presents an effective and operational RF model that estimates wetland AGB from Landsat data, providing a scientific basis for floodplain wetland carbon accounting and possible future studies, such as the linkage between wetland AGB and the great water level fluctuations.

Severe mountain pine beetle (MPB) epidemics can degrade ecosystem services and socioeconomic assets. Mapping outbreak progression provides tools to mitigate damages and analyze MPB attack processes. Current time-series methods for mapping disturbance focus on extent rather than severity. Infestation severity, defined by within-pixel percentage, is more robust for answering a variety of ecologic questions. We develop a time-series regression approach to map infestation severity from 2005 to 2015 in the U.S. Central Rocky Mountains. Covariates include spectral data from all available dates of Landsat imagery, topographic data, and US Forest Service aerial detection survey (ADS) polygons. We collect model reference data by interpreting National Agricultural Imagery Program images. Validation against a randomly selected subset of the data results in no statistical difference between predicted and observed severity. The mean absolute deviation is 7.7% with a root-mean-square error of 9.9%. Average (maximum) severity increased from 9.4% (49.7%) in 2005 to 17.6% (58.8%) in 2015. Our raster maps identify widespread, lower severity infestation absent from the ADS. Our maps can improve mitigation efforts by allowing managers to: address low-severity infestations before they intensify, monitor intensifying infestations within previously identified outbreak extents, and combine infestation severity with other forest metrics.