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Remote Sensing of Drought Recovery

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 4620

Special Issue Editors

1. Department of Geography, University of California, Los Angeles, CA 90095, USA
2. Institute for Environmental and Spatial Analysis, University of North Georgia, Dahlonega, GA 30597, USA
Interests: hydrology; GIS; precipitation; climate variability; numerical modeling; climate change; civil engineering; meteorology; remote sensing; environmental engineering
Department of Geography, University of California Los Angeles, Los Angeles, CA 90095, USA
Interests: snow hydrology and river hydraulics; streamflow forecasting and hydrologic hazard prediction; water sustainability and hydrologic extreme assessments; hydro-informatics and land data assimilation; system development and application; remote sensing of land surface processes; land–atmosphere processes and radiative transfer modeling

Special Issue Information

Dear Colleagues, 

This Special Issue aims to cover topics on remote sensing applications to quantify drought recovery. Drought, as a frequent, costly natural disaster, is the most widespread climatic extreme in both human and natural systems. In general, droughts are classified into four groups: meteorological (deficit in precipitation), agricultural (deficit in soil moisture), hydrological (deficit in runoff and groundwater), and socioeconomic (considering water supply, demand, and social response), which can all be associated with a sustained precipitation deficit. Since droughts create significant water and food security concerns, many methods and indices have been developed to detect and quantify the impacts. 

Remote sensing products have been widely used to monitor drought-related variables and quantify impacts from an ecosystem perspective. New unprecedented remote sensing datasets for precipitation, snow, soil moisture, temperature, evaporation, total water storage, vegetation, and land cover have resulted in new global drought monitoring from different perspectives (e.g., meteorological, agricultural, hydrological, and ecological). Satellite observations have also been directly used to detect drought impacts on ecosystems (vegetation growth) by assessing the photosynthesis capacity of plants. 

Recovery time (metric to measure drought impact), is defined as the time period an ecosystem requires to revisit its pre-drought state. Drought recovery is defined in different spatiotemporal scales using the area of ecosystems recovering and time to recovery. 

The focus of this Special Issue is to publish research papers and short reviews addressing recent progress in the area of drought recovery using remote sensing datasets. Original research papers related to the above topics, comprising innovative methods using new remote sensing datasets, statistical indices addressing drought recovery, and its spatiotemporal patterns, are highly encouraged. 

Ali Mehran
Dr. Dongyue Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (1 paper)

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Research

21 pages, 4343 KiB  
Article
Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas
by Xiangzhen Wang, Baofu Li, Yaning Chen, Hao Guo, Yunqian Wang and Lishu Lian
Remote Sens. 2020, 12(18), 2886; https://doi.org/10.3390/rs12182886 - 6 Sep 2020
Cited by 15 | Viewed by 4016
Abstract
Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability [...] Read more.
Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability in hydrological research in arid mountainous areas is not clear. Based on precipitation and runoff data, this study evaluated the applicability of each product to hydrological research in a typical mountainous basin (the Qaraqash River basin) in an arid region by using two methods: a statistical index and a hydrological model (Soil and Water Assessment Tool, SWAT). Simulation results were evaluated by Nash efficiency coefficient (NS), relative error (PBIAS) and determination coefficient (R2). The results show that: (1) The spatial distributions of precipitation estimated by these four products in the Qaraqash River basin are significantly different, and the multi-year average annual precipitation of GSMaP is 97.11 mm, which is the closest to the weather station interpolation results. (2) On the annual and monthly scales, GSMaP has the highest correlation (R ≥ 0.82) with the observed precipitation and the smallest relative error (BIAS < 6%). On the seasonal scale, the inversion accuracy of GSMaP in spring, summer and autumn is significantly higher than other products. In winter, all four sets of products perform poorly in estimating the actual precipitation. (3) Monthly runoff simulations based on SM2RAIN and GSMaP show good fitting (R2 > 0.6). In daily runoff simulation, GSMaP has the greatest ability to reproduce runoff changes. The study provides a reference for the optimization of precipitation image data and hydrological simulation in data-scarce areas. Full article
(This article belongs to the Special Issue Remote Sensing of Drought Recovery)
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