Advanced geospatial analytics for evapotranspiration dynamics: Integration of Sentinel-1A and FAO Penman-Monteith method

Selvaprakash Ramalingam *

Visiting Research Scholar, Agricultural and Biological Engineering, Purdue University, USA.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 3481-3492.
Article DOI: 10.30574/ijsra.2024.13.2.2581
Publication history: 
Received on 13 November 2024; revised on 22 December 2024; accepted on 24 December 2024
 
Abstract: 
Evapotranspiration (ET0) is vital for agriculture and environmental management, facing challenges from climate change. Optical remote sensing overcomes reliance on weather station data. The modeled ET0 using the FAO Penman-Monteith method and Partial Least Squares Regression on Sentinel-1A data with 2016-2017 meteorological archives. Comparative analyses revealed stability in transportation areas within deciduous forests and wetlands, contrasting temporal variations. ET0 was significantly influenced by relative humidity (RH) (70.80% to 89.89%), with temperature (T) playing a crucial role. Urban vegetated areas maintained stable T values (29.37°C), while forests exhibited dynamic T variations (24.24°C to 28.94°C). VH polarization captured diverse climatic influences, resulting in a broader range of dynamic ET0 values (7.38 to 10.76 mm/day) compared to VV polarization (6.74 to 9.34 mm/day). VH sensor performance varied; in October 2016 showed moderate accuracy R2 was 0.50 with slight underestimation Bias -0.08, while exceptional accuracy was seen in December 2017 R2 was 1.00 with positive bias (0.57) and excellent agreement KGE was 0.92. VV sensors in October 2016 had a firm fit R2 was 0.55, with moderate underestimation Bias -0.87, and in December 2017 displayed a good fit the R2 was 0.57, with slight overestimation Bias 0.44, and good agreement KGE 0.44. Integrating machine learning and satellite imagery enhances ET0 accuracy for real-time monitoring in adaptive management, addressing climate change, and showcasing sensor-specific variations. Future research should integrate multi-source synthetic aperture radar satellite data and machine learning for precise ET0 estimation in adaptive environmental management.
 
Keywords: 
Evapotranspiration; Temperature; Relative Humidity; Sentinel 1A
 
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