Agricultural droughts are extreme events which are often a result of interplays between
multiple hydro-meteorological processes. Therefore, assessing drought occurrence, extent …

Abstract In recent years, Deep Learning Multi-Layer Perceptron (DLMLP) neural networks
have shown remarkable success in addressing crop yield forecast related problems. The …

Researchers and engineers have increasingly used Deep Learning (DL) for a variety of
Remote Sensing (RS) tasks. However, data from local observations or via ground truth is …

In Morocco, cereal production shows high interannual variability due to uncertain rainfall
and recurrent drought periods. Considering the socioeconomic importance of cereal for the …

This study presents an evaluation of the impact of vegetation conditions on a land surface
model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water …

利用卫星遥感生产叶面积指数(Leaf area index: LAI) 产品并进行真实性检验是植被定量遥感的
一项重要研究内容. 过去10 a, 我国研究人员利用MODIS 或AVHRR 观测数据生产了 …

Drought is a challenging problem in agriculture due to its random and nonlinear nature.
Moreover, in bad weather situations, satellites do not capture the precise data of the earth …

Satellite-based soil moisture products allow direct monitoring of agricultural drought,
especially in regions with sparse ground-based observations. In this study, a soil moisture …

The land data assimilation system, LDAS-Monde, developed by the Research Department of
the French Meteorological service (Centre National de Recherches Météorologiques …

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre
National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) …