This PDF file contains the front matter associated with SPIE Proceedings Volume 10777, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Supraglacial debris essentially hamper the mapping of ice glaciers by remote sensing data. A semi-automatic approach for the mapping of debris covered glaciers in Astor Basin was applied, which combines the inputs from optical satellite data and the digital elevation model (DEM) data. Strong and effective pixel-based band ratios have turned out to be precise for naturally outlining clean glacier ice, however such classifications algorithm exhibit limitations in delineating debriscovered ice because of its spectral resemblance with adjacent landscape. Object based image analysis (OBIA) has risen as another examination strategy inside remote sensing. It gives a system to filter out worthless details and integrate other parts of detail into a single object, although it is also allowing contextual, shape, textural and, hierarchical principles to be used to classify imagery. Supraglacial debris-covered, snow covered glaciers and glaciated ice in Astor Basin were mapped by using Landsat 7,8 imageries gained from 2010 to 2017 and a digital elevation model (DEM) acquired from Advanced Land Observing Satellite (ALOS).The methods offered recognized their usefulness using freely accessible reasonable resolution Landsat OLI and ALOS data. Yet, the increasing availability of high resolution imageries, improved quality and the latest digital terrain data grip the potential of enhanced image segmentation and classification from OBIA approaches.

In this paper, we present a novel framework to characterize the complex spatial structure of the intra-urban heat island. Cities are known to be warmer than its surrounding areas because of the Urban Heat Island (UHI) phenomenon. However, due to the diverse and complex spatial geometries of cities themselves, the temperatures within vary widely. We take advantage of the well-established notion of fractal properties of cities, to characterize the complex structure of these hotspots. As a demonstrative case study, Land Surface Temperatures (LST) for Atlanta, GA, derived from Landsat 8 is used. From clustering analysis at multiple thermal thresholds, we show that the hotspots can be described as a case of percolating clusters. By comparing the area-perimeter fractal dimension at these thresholds, we find these clusters to be statistically self-similar. Furthermore, at the percolation threshold, the cluster size distribution is found to follow a power-law size distribution; and at a higher threshold, deviation from the power law is observed in the form of exponential tempering. We argue that the spatial distribution of the hotspots itself plays a significant role in the overall UHI and fractal analysis techniques lend themselves aptly to the characterization of the same. This has several further applications, such as targeted heat mitigation, assessment of health impacts, and energy load estimation.

Changes in directional (biconical) spectral reflectance with varied illumination and observing angles were monitored for three soil samples under air dry and saturated conditions in the laboratory. The illumination angle was set at -10°, -40°, and -70° (left side of sample in the principal plane), and the observing angle ranged from -60° to +60° (both side of sample in the principal plane) in 5° increments. The samples were chosen to represent various soil properties. The nadir spectral reflectance was relatively stable for all illumination angles, however, the directional reflectance was more variable. When soil samples were dry, the directional reflectance changed obviously with phase angle with a stronger backward reflectance, while the forward reflectance was generally lower. For saturated soil samples, the directional spectral reflectance of dry soil feature was reduced, and the strong backward scattering was weakened. Indeed, the directional spectral reflectance became less sensitive to illumination angle and observation angle changes, especially for dark soils. The added water not only darkened the soil reflectance, but also reduced the directional variation difference of soil. A simple sketch was introduced to suggest an explanation for the difference between directional reflectance between air dry and saturated samples. When illumination was from one direction, the convex soil surface forms a distinct shadow on the opposite side, leading to a low forward reflectance. However, with a water layer coating on the soil surface, the chance of light propagating to the opposite side of illumination was increased, increasing reflectance in the forward direction.

The spacing of Gansu Province from the eastern to western regions is very large and adjacent to the Qinghai–Tibet Plateau. On the one hand, Agriculture in western area is irrigated rather than non-irrigated agriculture in eastern area. On the other hand, elevation where adjacent to the Tibetan Plateau is much higher than other places. In this study, remote sensing drought indices, such as temperature vegetation dryness index (TVDI), vegetation condition index (VCI), temperature condition index (TCI), perpendicular drought index (PDI), and modified perpendicular drought index (MPDI), were calculated using historical MODIS data. The applicability of these remote sensing indices was preliminarily studied by comparing the Relative Soil Moisture (RSM) of the sites. Results showed that:1) In whole area, irrigated areas and high-altitude areas, the remote sensing indices have different degree of indication for the spatial distribution of RSM in the superficial layer in spring, summer, and autumn. Among them, TVDI has the best indication, followed by VCI and TCI, and PDI and MPDI have very limited indication. But TVDI has no indication in May in irrigated areas at all. 2) None of them can indicate the temporal variation characteristics of the RSM in the irrigated areas, and TVDI and TCI based on the surface temperature can indicate the temporal variation of the 10 and 20 cm-deep RSM in the high-altitude areas. In general, TVDI is a good indicator for RSM in the superficial layer in Gansu Province during spring, summer, and autumn.

Land surface albedo is considered to be a fundamental quantity for accurate assessment of the surface energy budget. Whereas satellite products usually provide total surface albedo, land surface models and numerous applications often require a separation of snow-free surface albedos of the vegetation canopy and the underlying bare soil. In this paper, a simplified non-linear spectral mixture model (NSM) was firstly presented to describe the canopy radiative transfer process. The soil background’s contribution on canopy albedo was approximated using an illumination factor, which is the canopy transmittance. Then, a framework was designed to retrieve the soil VIS albedo using the NSM model, MODIS BRDF (MCD43A3), leaf area index (LAI) (MCD15A2H) and clumping index (CI) products. Finally, the global broadband visible albedo of soil background was indirectly validated using the ECOCLIMAP data, with an R² value of 0.889. Therefore, the simplified NSM model is accurate to simulate canopy visible albedo and also to retrieve soil background’s albedo, and the first global snow-free albedo of soil background at 500-m resolution is reliable.

The characteristics of this GPP estimation method correspond to the photosynthesis process. The photosynthetic rate varies from it's capacity by weather conditions, where depression thereof is controlled by stomatal opening and closing. In this study, we used flux data from a dry area and Moderate Resolution Imaging Spectrometer (MODIS) surface temperature products to define a canopy conductance index. First, we studied the contribution ratios of elements of canopy conductance using the big-leaf model with diurnal change flux data averaged over 8 days. Next, the correlations of meteorological and flux elements with surface temperature data from MODIS were studied. The largest contributor to the denominator of canopy conductance was found to be vapor pressure deficit (VPD), and that of the numerator was evapotranspiration. During the period around noon, evapotranspiration did not change dramatically and the canopy conductance index was estimated as the slope of 1/VPD, which changes over time. In the dry area, the surface temperatures around 11 a.m. and 1 p.m. were strongly correlated with VPD at 11 a.m. and 1 p.m., respectively. For dry areas, therefore, the slope of 1/VPD can be estimated using surface temperature data from satellite sensors.

The recent emergence of spaceborne Solar-Induced chlorophyll Fluorescence (SIF) represents a major breakthrough in understanding, monitoring and quantifying global carbon cycle variability and change. However, the existing spaceborne SIF products are typically noisy, coarse and sparse in both time and space coverage, thus are not suitable for regional carbon cycle studies. In this study, we are taking advantage of the complementary characteristics of the Global Ozone Monitoring Experiment (GOME-2) SIF and MODIS observations of terrestrial ecosystems to downscale the coarse-resolution SIF products into finer spatial and temporal resolutions, using an innovative Ensemble Kalman Filter and parameter estimation with Adaptive Spatial Average scheme (ASA-EnKF) data assimilation technique. We used a simplified version of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE), level-2 GOME-2 ungridded SIF product to develop a high-quality/resolution (i.e., 1km, hourly, gap-free) SIF dataset, over the Western United States. This paper demonstrates some preliminary result of our work.

Much attention has been paid nationally and internationally to shifting cultivation in upland Myanmar since Forest Department of Myanmar indicates that it is the main underlying cause for deforestation. However, knowledge of the explicitly spatial pattern of forest lands affected by shifting cultivation, transition characteristics and the impact of shifting cultivation for Reduced emissions from deforestation and forest degradation (REDD+) is scare. In this study, the scale, intensity and duration of shifting cultivation during 2002-2016 were detected by means of MODIS time series imageries using improved vegetation change tracker (VCT) based on Integrated Forest Z-score (IFZ) derived from the spectral–temporal properties of forest lands change processes.

In recent years, climate change and other anthropogenic factors have contributed to increased crop blight and harmful insects in South Korea crop fields. The main objective of this research was to develop an integrated method and procedure that can be used by unmanned aerial vehicle (UAV) to derive reliable, cost-effective, timely, and repeatable farm information on agricultural production of the field crop at regional level prior to the harvesting date. An attempt has been made in this study to investigate the role of geo-informatics to discriminate different crops at various levels of classification and monitoring crop growth. This research focuses on the evaluation of spatial and temporal variations in crop phenology at Chungbuk using the UAV image data. Crop canopy spectral data in the growing seasons were measured. UAV imagery combined with Smart Farm Map (SFM) were suggested as promising for use in a national crop monitoring system. The test bed area which located in Cheongju were observed by four bands of UAV mounted sensors. UAV images were acquired 6 times from May 6 to October 15, 2016. The difference of normalized difference vegetation index (NDVI) was analyzed. Results showed that NDVI of UAV were strongly correlated with vegetation vigor and growth. The spatial and temporal NDVI and land use and Land cover (LULC) distribution of the crop field were mapped based on the 4-band combination of UAV imagery. The results of this study, we found that the spatial and temporal variation and correlation with crop phenology, LULC classification, and NDVI relationship. The developed model in this study shows a promising result, which can be useful for forecasting crop vegetation conditions in regional scales. Also, the results suggest that the necessary classification performance can be obtained in most of the phenology at crop growing cases, therefore the analysis could be cost effective. The investment to achieve this seems to be worthwhile.

The research was implemented in a planted area dominated by winter wheat in the Hebi city of Henan Province driving method and EnKF assimilation method were used to coupling the optimized and reconstructed MODIS LAI time series data and the rigorously calibrated wheat growth model WheatSM(Wheat Growth and Development Simulation Model) developed according to the planting varieties of the winter wheat region in North China respectively, the research on winter wheat yield estimation was conducted at field scale and regional scale from 2013 to 2016.The results show that : (1) The coupling simulation precision was inferior to the pre-coupled simulation precision at field scale simulation.(2)When the grid was used for regional scale simulation, the simulation accuracy of 1 km resolution grid point is higher than 5km resolution.(3)At two kinds of grid resolutions, EnKF assimilation simulation has the highest accuracy, followed by the driving method, and the simulation precision before coupling is the lowest. The RMSE of the total output of the EnKF assimilation simulation area and the actual total output at the 1 km resolution grid is 15.1. Assimilation of remote sensing information at fine grid resolution can improve the precision of regionalization application of the WheatSM model.

Due to the large number of total population and its high population growth, food needs become the most important issue in Indonesia. This study aims to (1) map the agro-ecosystem zones based on the analysis of remote sensing images; (2) estimate the food production (rice and cassava); and (3) analyze the food security in the study area based on the mapping of the agro-ecosystem zones. Gunung Kidul Regency was selected as the study area because it is one of areas with food insecurity in D.I. Yogyakarta. This research used Landsat 8 OLI recorded on 14 April 2014 and 27 June 2013 and assisted by other spatial data such as the RBI map, soil map and slope map using Geographic Information System (GIS). The data of population statistics was also used to calculate the amount of food needs in the study area. Field survey was conducted to determine the productivity of the land in each agroecosystem zone, and to test the accuracy of the results of remote sensing images processing. The results of this study are: (1) Gunung Kidul Regency can be divided into seven agroecosystem zones, each of which has a different productivity for rice and cassava; (2) Gunung Kidul Regency is included in areas experiencing food insecurity when only taking into account the production of rice, with a shortage of 13,134.05 t; and (3) If the production of rice and cassava are taken into account, Gunung Kidul Regency is not categorized as foodinsecure areas because it has a food surplus of 435,192.20 t.

Estimation of surface net radiation (SNR) is essential for understanding the land surface energy transformation, snow melting calculations, modeling crop growth, and addressing water resource management. In this study, two sets of experiments were performed to identify, respectively, the impacts of MODIS land surface temperature (LST) products, ground-based incoming shortwave and long-wave radiation and albedo measurements, as well as the performance of CoLM with respect to modeling SNR in the Tibetan Plateau at three timescales (half-hourly, hourly, daily, and monthly). The results show that the two experiments provide nearly the similar results and are obvious higher than ground measured SNR validations at three different timescales. SNRs obtained at half-hourly and hourly timescales closely match the real data fluctuations, while daily timescale is too large to catch the short-term fluctuations according to the peak values at the three timescales. Moreover, compared with Method 2, Method 1 is more accurate at different timescales.

MERSI data are applied to generate NDVI and RVI change graphs for comparative monitoring of one large-area dry-hot wind disaster in the wheat-growing area of Henan province, analysis of the correlation of the NDVI variation and RVI variation arising from mild and severe dry-hot wind processes to the daily highest temperature, 14:00 ground wind speed of 10m and 14:00 humidity and establishment of a mono-factor and multi-factor regressive forecast model between vegetation index variation and disaster-causing atmospheric elements. The results show that the monitoring results of dry-hot wind disasters on the basis of two vegetation indexes highly conform to each other. In case of severe dry-hot wind process, the two vegetation index variations have a high relevance to the key meteorological elements, with R² in the trinary linear regression model of meteorological elements being 0.706 and 0.708 respectively and the highest daily mean temperature passing the 0.05 significance level check. In case of mild dry-hot wind process, the two vegetation index variations have a very low relevance to the key meteorological elements and modeling is impossible and there is a high degree of difference between the variations of vegetation indexes of different stations, i.e. the lower the level of meteorological disaster is, the more telemetric data are needed to ensure the truthful disaster loss monitoring results and the more important field management measures as a defense against meteorological disasters.

Leaf area index (LAI) as an important vegetation biophysical parameter, is one of input parameters in the land surface model, which has a very close relationship with vegetation photosynthesis, evapotranspiration, precipitation and carbon flux exchange process. Crop LAI is a vital agronomic parameter, which can reflect the status of crop growth and predict crop yield. Accurate crop LAI has an important role in crop identification, growth monitoring, and food production estimation. This paper used the China Environmental monitoring Satellite (HJ-1) CCD data to estimate summer corn LAI based on PROSAIL radiative transfer model in Yucheng county, Shandong province, China. The mechanism and impact of leaf and canopy parameters on the canopy reflectance in different bands of HJ-1 CCD data were analyzed quantitatively using the simulated data. The results showed that the LAI estimates had relatively high accuracy with R=0.73, RMSE=0.32, and the PROSAIL model could be used to estimate LAI effectively from the perspective of radiative transfer principle. It can provide reference data for corn LAI estimation and growth monitoring in the study area.

The combinatorial use of geostationary and polar-orbiting satellites is expected to provide a new approach to Earth observation with a wide range of applications; however, differences in the observation conditions and sensor specifications can introduce biases into the outputs of the various satellites. These differences are also known to depend on the land cover type, and this feature of the data requires thorough investigation. This study compared the solar reflective bands measured by the Advanced Himawari Imager (AHI) and an established sensor MODIS onboard Terra satellite. The comparison was made using data collected over a forested region on the Shikoku Island (30 km by 140 km) located in the south of Japan under similar view zenith angles of approximately 40 degrees. The reflectances of the visible and near-infrared bands (four bands) were processed to correct the molecular scattering and ozone absorption effect. A comparison was made before and after the atmospheric correction. Our results showed that the reflectance differences over the region fell mainly within the relevant standard deviations (reflectance variations within the relevant region), except for the green band. The larger difference between the green band reflectances measured by the two sensors was attributed to differences in the band positions. The band-4 of MODIS (green) covers 545-565 nm, whereas the AHI counterpart (band-2) covers 490-530 nm, providing little overlap with MODIS. These results suggested that special caution is needed when using data collected from these two sensors simultaneously or continuously if the green band is involved in the algorithm.

Remote sensing (RS) and geographic information system (GIS) could be very efficiently used for precise paddy field area estimation and provision of paddy field crop maps. The study on drone RS based paddy field estimation and inventory studies at small town-level was taken up in Chungbuk. The major objective of this study was to attempt small town-level paddy rice inventory during rice growing season using drone mounted sensors. The methodology adopted for small townlevel paddy field crop inventory consisted of: a) geo-referencing of Smart Farm Map data, b) rectification of cadastral maps, c) Ground data collection, d) drone data collection and e) accuracy assessment. The data obtained from RGB and NIR sensors onboard the drone are described. Approaches for preprocessing, transferring, and modeling these data for understanding the relationship between their spatial and temporal behavior and rice growth states are discussed. Finally, techniques for rice identification and area and inventory are briefly described. The results indicate that paddy field discrimination at small town-level is possible using drone with accuracy ranging from 95 to 97 per cent depending upon plot size. In most of the paddy field, an amount of heterogeneity was found due to growing state differences and varying management practices resulting in different vigor conditions. As expected it was observed that the accuracy with drone imagery data was better in comparison to National Statistical Office data since plot sizes are very small in the study area. The drone data of the paddy field was also available due to various reasons, it was observed that, for better rice growing condition discrimination and achieving higher accuracy, therefore, Combination drone imagery with Smart Farm Map data is very important. This study brings out the potentials and limitations of combined GIS based small town-level paddy field inventory using drone imagery data. Thus drone data and the information derived from it, is attractive to agricultural management system in the South Korea. It is concluded that, in addition to the GIS combined technology, the use of many other techniques such as ground observations, GPS and meteorological data is highly appreciable.