Rice lodging disaster monitoring method based on multi-source remote sensing data

Xuemei Dai; Shuisen Chen; Hao Jiang; Chandrasekar Nainarpandian

doi:10.1117/12.2667454

10 February 2023 Rice lodging disaster monitoring method based on multi-source remote sensing data

Xuemei Dai, Shuisen Chen, Hao Jiang, Chandrasekar Nainarpandian

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 1255224 (2023) https://doi.org/10.1117/12.2667454
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

Lodging is a major yield-limiting factor in rice. Accurate assessment of rice lodging is essential for yield damage estimation, agricultural insurance claims settlement and subsequent management decisions. This study aims to explore the effect of lodging on backscatter/coherence and spectral reflectance derived from Sentinel-1 and Sentinel-2 data. Based on this, an extraction method of lodging rice distribution using multi-source remote sensing data was proposed. The results showed that: (a) The lodging area of rice could be effectively extracted from dual-polarization Sentinel-1 image data with an accuracy of 87.87%; (b) The vegetation indices (NDVI, DVI and LSWI) extracted by Sentinel-2 are sensitive to lodging rice. Based on a certain threshold, lodging rice can be effectively extracted with an accuracy of 87.5%. Our findings demonstrate the potential of Sentinel data for near real-time detection of the rice lodging.

Citation Download Citation

Xuemei Dai, Shuisen Chen, Hao Jiang, and Chandrasekar Nainarpandian "Rice lodging disaster monitoring method based on multi-source remote sensing data", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 1255224 (10 February 2023); https://doi.org/10.1117/12.2667454

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KEYWORDS

Remote sensing

Radar

Satellites

Vegetation

Polarization

Scattering

Reflectivity

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