Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort,
funded by the European Space Agency, to serve as a centralised data hosting facility for …

Evapotranspiration (ET) is an essential ecohydrological process linking the land surface
energy, water and carbon cycles, and plays a critical role in the earth system. ET remains …

Global soil moisture estimates from current satellite missions are suffering from inherent
discontinuous observations and coarse spatial resolution, which limit applications especially …

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to
monitor the photosynthetic activity of terrestrial ecosystems. However, several issues …

Soil moisture plays a critical role in improving the weather and climate forecast and
understanding terrestrial ecosystem processes. It is a key hydrologic variable in agricultural …

Deep learning (DL) models trained on hydrologic observations can perform extraordinarily
well, but they can inherit deficiencies of the training data, such as limited coverage of in situ …

Seasonal and interannual variations in global wetland area are a strong driver of
fluctuations in global methane (CH 4) emissions. Current maps of global wetland extent vary …

Spatial downscaling has recently become a crucial process in the regional application of
coarse-resolution passive microwave surface soil moisture (SSM) products. Extensive gaps …

While ensuring food security worldwide, irrigation is altering the water cycle and generating
numerous environmental side effects. As detailed knowledge about the timing and the …