Accurate and continuous monitoring of soil moisture (SM) is crucial for a wide range of applications in agriculture, hydrology, and climate modelling. In this study, we present a novel machine learnin
Soil Moisture data (version 05.2) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets, a
This data set contains meteorological data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.
This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. asc
Context and methodology This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN...
