Nengcheng Chen

TL;DR: In this article, five remotely sensed soil moisture products, namely, the Soil Moisture Active Passive (SMAP), two SoilMoisture and Ocean Salinity (SMOS) products, the Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), and the European Space Agency (ESA) Climate Change Initiative (CCI), were systematically investigated by utilizing in-situ soil moisture observations from global dense and sparse networks.

TL;DR: The results suggest that the LSTM-AdaBoost combination model using the averaging strategy is highly promising for short and mid-term daily SST predictions.

TL;DR: This review is intended to fill this knowledge gap by identifying the key concepts behind urban drought, including the definition, occurrence, characteristics, formation, and impacts, and proposes five action steps for policymakers and city stakeholders to combat and mitigate the impacts of urban droughts.

TL;DR: A spatiotemporal deep learning model is proposed which can capture the correlations of SST across both space and time and is highly promising for short and mid-term daily SST field prediction accurately and conveniently.

TL;DR: In this article, an Evolution Process-based Multi-sensor Collaboration (EPMC) framework was proposed with the realization that effective agricultural drought assessment requires an integrated approach that considers both drought development and crop phenology.