Calibration of the SMAP soil moisture retrieval algorithm to reduce bias over the Amazon rainforest

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(2024)

引用 0|浏览9
暂无评分
摘要
Soil moisture (SM) is crucial for the Earth's ecosystem, impacting climate and vegetation health. Obtaining in situ observations of SM is labor-intensive and complex, particularly in remote and densely vegetated regions like the Amazon rainforest. NASA's Soil Moisture Active and Passive (SMAP) mission, utilizing an L-band radiometer, aims to monitor global SM. While it has been validated in areas with low Vegetation Water Content (VWC) (< 5 kgm -2 ), its efficiency in the Amazon, with dense canopies and high VWC (> 10 kgm -2 ), is limitedly investigated due to scarce in situ measurements. This study assessed and analyzed the SMAP SM retrievals in the Amazon, employing the single-channel algorithm (SCA) and adjusting vegetation optical depth (τ) and single scattering albedo (ω), two key vegetation parameters. It incorporated in situ SM observations from three old-growth rainforest locations: Tambopata (Southwest Amazon), Manaus (Central Amazon), and Caxiuana (Eastern Amazon). The SMAP SM deviated substantially from the in situ SM. However, calibrating τ and ω values, characterized by a lower τ, resulted in better agreement with the in situ measurements. The study emphasizes the pressing need for innovative methodologies to accurately retrieve SM in high-VWC regions like the Amazon rainforest using SMAP data.
更多
查看译文
关键词
Remote sensing,soil moisture,Vegetation optical depth,Amazon rainforest,Soil Moisture Active/Passive (SMAP)
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要