Impact of temporal variations in vegetation optical depth and vegetation temperature on L-band passive soil moisture retrievals over a tropical forest using in-situ information

Assessment of NASA SMAP Soil Moisture Products for Agricultural Regions in Central Mexico: An Analysis Based on the THEXMEX Dataset

“…In general, these fields presented lower uncertainty in correlation length compared to corn fields. The areas covered by forest presented a mean h RM S of 1.46 cm and a mean correlation length of 14.5 cm, similar to other forested areas such as [34].…”

“…3) Voronoï diagram: A Voronoï diagram approach [49] is also used as an upscaling function and applied to the in situ surface SM information collected during the field campaigns similar to previous works [30], [31], [34]. Input parameters used to generate the Voronoï diagram include the bounding area of the SMAP pixel and the geographical location of the in situ stations.…”

“…As a response to this need, Terrestrial Hydrology Experiments in Mexico (THEXMEXs) have been conducted over different biomes, including agricultural lands, to monitor the dynamics of soil and vegetation [33], [34], [35]. During 2018 and 2019 (THEXMEX-18 and -19), an agricultural region was characterized over two complete growing seasons of corn in the region of Huamantla, Central Mexico.…”

SMAP Detects Soil Moisture Under Temperate Forest Canopies

“…ESA (European Space Agency) launched a satellite carrying an L‐band radiometer in 2009 called Soil Moisture Ocean Salinity (SMOS) (Mecklenburg et al, 2016) and NASA (National Aeronautics and Space Administration) in 2015 called Soil Moisture Active Passive (SMAP) (Entekhabi et al, 2010). These missions opened the door for wide scale retrieving of soil moisture in forested area from space (e.g., Djamai et al, 2015; Kang et al, 2016, 2019; Monsiváis‐Huertero et al, 2020; Vittucci et al, 2016). However, the lack of appropriate reference soil moisture sources has been a major obstacle for quantifying, developing, testing, and validating soil moisture retrieval algorithms from satellite observations (e.g., Vittucci et al, 2019).…”