Evaluation of SMAP and CYGNSS Soil Moistures in Drought Prediction Using Multiple Linear Regression and GLDAS Product
Drought is a devastating natural hazard and exerts profound effects on both the environment and society. Predicting drought occurrences is significant in aiding decision-making and implementing effective mitigation strategies. In regions characterized by limited data availability, such
as Southern Africa, the use of satellite remote sensing data promises an excellent opportunity for achieving this predictive goal. In this article, we assess the effectiveness of Soil Moisture Active Passive (SMAP) and Cyclone Global Navigation Satellite System (CYGNSS) soil moisture data
in predicting drought conditions using multiple linear regression-predicted data and Global Land Data Assimilation System (GLDAS) soil moisture data.
Document Type: Research Article
Affiliations: 1: e School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China 2: Department of Physics and Mathematics, University of Acala, Nanjing 210044, China 3: School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Publication date: May 1, 2024
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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