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Remote Sensing
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Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting II
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Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting II

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: 15 March 2025 | Viewed by 4971

Special Issue Editor

Prof. Dr. Yuei-An Liou

E-Mail Website
Guest Editor
Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli District, Taoyuan City 320317, Taiwan
Interests: remoet sensing; hydrometeorological extremes; vulnerability assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Droughts are very common phenomena which impose serious challenges on ecosystems and human societies. They occur in all types of climate circumstances. However, their characteristics vary considerably from one region to another.

To properly plan and manage water resources, it is important to accurately and timely forecast drought events. Hence, this Special Issue welcomes presentations of significant advancements in drought monitoring and prediction capabilities on regional and global scales. The studies can be based on known drought indicators or new ones. Particularly, the incorporation of machine learning tools and approaches that can improve existing drought forecasts is encouraged.

We also welcome research on exploring the link from multiple information sources, including satellite-based vegetation conditions and evapotranspiration, to investigate current or future drought impacts on water resources.

Prof. Dr. Yuei-An Liou
Guest Editor

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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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

30 pages, 20031 KiB  
Article
Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye
by Eyyup Ensar Başakın, Paul C. Stoy, Mehmet Cüneyd Demirel, Mutlu Ozdogan and Jason A. Otkin
Remote Sens. 2024, 16(20), 3799; https://doi.org/10.3390/rs16203799 - 12 Oct 2024
Cited by 1 | Viewed by 1655
Abstract
We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used [...] Read more.
We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems. Full article
(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting II)
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20 pages, 13807 KiB  
Article
Desertification Mitigation in Northern China Was Promoted by Climate Drivers after 2000
by Haohui Li, Kai Yang, Yang Cui, Lingyun Ai, Chenghai Wang, Zhenting Wang and Caixia Zhang
Remote Sens. 2024, 16(19), 3706; https://doi.org/10.3390/rs16193706 - 5 Oct 2024
Viewed by 1298
Abstract
Desertification greatly threatens the ecological environment and sustainable development over approximately 30% of global land. In this study, the contributions of climate drivers and human activity in shaping the desertification process from 1984 to 2014 were quantified in the desertification-prone region (DPR) in [...] Read more.
Desertification greatly threatens the ecological environment and sustainable development over approximately 30% of global land. In this study, the contributions of climate drivers and human activity in shaping the desertification process from 1984 to 2014 were quantified in the desertification-prone region (DPR) in Northern China (NC) by employing net primary productivity (NPP) as a proxy. The results reveal that 72.74% of the DPR experienced desertification mitigation and 27.26% experienced exacerbation. Climate drivers acted as primary drivers, contributing to both the mitigation (47.2%) and exacerbation (48.5%) of desertification, while human activity also played a crucial role, with contributions of 39.6% to mitigation and 41.0% to exacerbation of desertification. Furthermore, a shift in desertification dynamics emerged around 2000, with climate drivers promoting the mitigation process (66.8%), and precipitation was a dominant climatic factor for the mitigation of desertification after 2000, which was related to internal atmospheric variability. This study highlights changes in the contributions of different factors to desertification, underscoring the need for policy adjustment to attain sustainable land management in NC. Full article
(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting II)
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Graphical abstract

21 pages, 15098 KiB  
Article
An Evaluation of Ecosystem Quality and Its Response to Aridity on the Qinghai–Tibet Plateau
by Yimeng Yan, Jiaxi Cao, Yufan Gu, Xuening Huang, Xiaoxian Liu, Yue Hu and Shuhong Wu
Remote Sens. 2024, 16(18), 3461; https://doi.org/10.3390/rs16183461 - 18 Sep 2024
Viewed by 1059
Abstract
Exploring the response of spatial and temporal characteristics of ecological quality change to aridity on the Qinghai–Tibet Plateau (QTP) can provide valuable information for regional ecological protection, water resource management, and climate change adaptation. In this study, we constructed the Remote Sensing Ecological [...] Read more.
Exploring the response of spatial and temporal characteristics of ecological quality change to aridity on the Qinghai–Tibet Plateau (QTP) can provide valuable information for regional ecological protection, water resource management, and climate change adaptation. In this study, we constructed the Remote Sensing Ecological Index (RSEI) and Standardized Precipitation Evapotranspiration Index (SPEI) based on the Google Earth Engine (GEE) platform with regional characteristics and completely analyzed the spatial and temporal variations of aridity and ecological quality on the QTP in the years 2000, 2005, 2010, 2015, and 2020. Additionally, we explored the responses of ecological quality to aridity indices at six different time scales. The Mann–Kendall test, correlation analysis, and significance test were used to study the spatial and temporal distribution characteristics of meteorological aridity at different time scales on the QTP and their impacts on the quality of the ecological environment. The results show that the ecological environmental quality of the QTP has a clear spatial distribution pattern. The ecological environment quality is significantly better in the south-east, while the Qaidam Basin and the west have lower ecological environment quality indices, but the overall trend of environmental quality is getting better. The Aridity Index of the QTP shows a differentiated spatial and temporal distribution pattern, with higher Aridity Indexes in the north-eastern and south-western parts of the plateau and lower Aridity Indexes in the central part of the plateau at shorter time scales. Monthly, seasonal, and annual-scale SPEI values showed an increasing trend. There is a correlation between aridity conditions and ecological quality on the QTP. The areas with significant positive correlation between the RSEI and SPEI in the study area were mainly concentrated in the south-eastern, south-western, and northern parts of the QTP, where the ecological quality of the environment is more seriously affected by meteorological aridity. Full article
(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting II)
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