Remote sensing and land surface models promote the understanding of soil moisture dynamics by means multiple products. These products differ in data sources, algorithms, model structures forcing datasets, complicating selection optimal products, especially regions with complex covers. This study compared different algorithms flagging strategies based on situ observations Anhui province, China, ...

An algorithmic approach, based on satellite-derived sea-surface (“skin”) salinities (SSS), is proposed to correct for errors in SSS retrievals and convert these skin into comparable in-situ (“bulk”) the top-5 m of subpolar Arctic Oceans. In preparation routine assimilation operational ocean forecast models, Soil Moisture Active Passive (SMAP) satellite Level-2 observations are transformed using...

Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture vegetation water content, which are related to the Earth carbon cycles. Only two space-borne sensors currently operating: SMOS, launched end of 2009 thus providing now a 10-year data set SMAP, beginning 2015. This study provides state-of-the-art scientific overview SMOS-IC retriev...

Currently, soil-moisture data extracted from microwave suffer poor spatial resolution. To overcome this problem, study proposes a method to downscale the soil moisture The proposed establishes statistical relationship between low-spatial-resolution input and land-surface model based on neural network (NN). This is then applied high-spatial-resolution obtain data. include passive (SMAP, AMSR2), ...

Large rivers are key components of the land-ocean branch global water and biogeochemical cycles. River discharges can have important influences on physical, biological, optical, chemical processes in coastal oceans. It is, therefore, importance to routinely monitor time-varying dispersal patterns river plumes. The European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) NASA Active Passi...

Soil moisture (SM) has significant impacts on the Earth’s energy and water cycle system. Remote sensing, such as Moisture Active Passive (SMAP) mission, delivered valuable estimations of global surface soil moisture. However, it a 2~3 days revisit time leading to gaps between SMAP areas. To achieve accurate comprehensive real-time forecast SM, we propose spatial–temporal deep learning model bas...