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Remote Sensing
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Multi-Sensor Remote Sensing for Drought Characterization and Monitoring
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Multi-Sensor Remote Sensing for Drought Characterization and Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 1166

Special Issue Editors

Dr. Peijuan Wang

E-Mail Website
Guest Editor
Chinese Academy of Meteorological Sciences, Beijing, China
Interests: agricultural drought monitoring based on multi-source data; crop growth condition and yield estimation; agricultural meteorological disasters
Prof. Dr. Liang Sun

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: multi-source remote sensing data fusion algorithm and application; crop classification and yield estimation; surface evapotranspiration and crop drought monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Jianying Yang

E-Mail Website
Guest Editor
Chinese Academy of Meteorological Sciences, Beijing, China
Interests: agricultural meteorological disasters identification; risk evaluation of agricultural meteorological disasters
Dr. Hua Wu

E-Mail Website
Guest Editor
Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China
Interests: thermal infrared remote sensing; scaling and validation of remote sensed products; retrieval of hydrothermal parameters from remote sensing data
Special Issues, Collections and Topics in MDPI journals
Dr. Ehsan Sharifi

E-Mail Website
Guest Editor
Deutsches GeoForschungsZentrum (GFZ), Potsdam, Germany
Interests: satellite-based earth observation; precipitation; groundwater; drought monitoring; data assimilation; downscaling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The special issue “Multi-Sensor Remote Sensing for Drought Characterization and Monitoring” aims to explore the latest advancements and research trends in the use of multi-sensor remote sensing data to understand, characterize, and monitor drought phenomena.

Drought is a complex natural hazard with profound environmental, social, and economic impacts, which will lead to a series of problems, such as agricultural production, food security, and sustainable development of ecosystems. Accurate and timely monitoring of drought is crucial for early warning, mitigation, and adaptation strategies. Remote sensing provides a valuable tool for drought characterization and monitoring, offering a wide range of data sources from various sensors and platforms, which enable the large-scale and continuous observation of key drought-related variables.

This special issue invites original research articles, review papers, and technical notes that focus on the following topics:

  • Development and evaluation of novel algorithms and techniques for drought detection, characterization, and monitoring using multi-sensor remote sensing data.
  • Integration of data from various remote sensing platforms, including optical, thermal, and microwave sensors, for drought assessment and monitoring.
  • The application of advanced machine learning and artificial intelligence techniques to improve the accuracy and efficiency of drought detection and monitoring.
  • Validation and comparison of remote sensing-based drought indicators and indices with ground-based observations and other drought monitoring tools.
  • Assessment of the impacts of climate change and variability on drought occurrence, intensity, and duration using remote sensing data.
  • Case studies demonstrating the operational implementation of multi-sensor remote sensing data for drought management and decision-making.

We encourage submissions from researchers and professionals working in the fields of remote sensing, geography, meteorology, hydrology, environmental science, and related disciplines. By contributing to this special issue, authors will have the opportunity to showcase their cutting-edge research and promote the exchange of ideas and knowledge among the scientific community.

Dr. Peijuan Wang
Dr. Liang Sun
Dr. Jianying Yang
Dr. Hua Wu
Dr. Ehsan Sharifi
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.

Keywords

  • multi-sensor remote sensing
  • drought monitoring
  • drought evolution characterization
  • drought risk evaluating
  • drought early warning
  • machine learning
  • climate change
  • drought indices
  • data integration
  • optical, thermal, and microwave sensors
  • ground-based validation

Published Papers (2 papers)

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Research

22 pages, 7371 KiB  
Article
Investigating the Response of Vegetation to Flash Droughts by Using Cross-Spectral Analysis and an Evapotranspiration-Based Drought Index
by Peng Li, Li Jia, Jing Lu, Min Jiang, Chaolei Zheng and Massimo Menenti
Remote Sens. 2024, 16(9), 1564; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16091564 - 28 Apr 2024
Viewed by 222
Abstract
Flash droughts tend to cause severe damage to agriculture due to their characteristics of sudden onset and rapid intensification. Early detection of the response of vegetation to flash droughts is of utmost importance in mitigating the effects of flash droughts, as it can [...] Read more.
Flash droughts tend to cause severe damage to agriculture due to their characteristics of sudden onset and rapid intensification. Early detection of the response of vegetation to flash droughts is of utmost importance in mitigating the effects of flash droughts, as it can provide a scientific basis for establishing an early warning system. The commonly used method of determining the response time of vegetation to flash drought, based on the response time index or the correlation between the precipitation anomaly and vegetation growth anomaly, leads to the late detection of irreversible drought effects on vegetation, which may not be sufficient for use in analyzing the response of vegetation to flash drought for early earning. The evapotranspiration-based (ET-based) drought indices are an effective indicator for identifying and monitoring flash drought. This study proposes a novel approach that applies cross-spectral analysis to an ET-based drought index, i.e., Evaporative Stress Anomaly Index (ESAI), as the forcing and a vegetation-based drought index, i.e., Normalized Vegetation Anomaly Index (NVAI), as the response, both from medium-resolution remote sensing data, to estimate the time lag of the response of vegetation vitality status to flash drought. An experiment on the novel method was carried out in North China during March–September for the period of 2001–2020 using remote sensing products at 1 km spatial resolution. The results show that the average time lag of the response of vegetation to water availability during flash droughts estimated by the cross-spectral analysis over North China in 2001–2020 was 5.9 days, which is shorter than the results measured by the widely used response time index (26.5 days). The main difference between the phase lag from the cross-spectral analysis method and the response time from the response time index method lies in the fundamental processes behind the definitions of the vegetation response in the two methods, i.e., a subtle and dynamic fluctuation signature in the response signal (vegetation-based drought index) that correlates with the fluctuation in the forcing signal (ET-based drought index) versus an irreversible impact indicated by a negative NDVI anomaly. The time lag of the response of vegetation to flash droughts varied with vegetation types and irrigation conditions. The average time lag for rainfed cropland, irrigated cropland, grassland, and forest in North China was 5.4, 5.8, 6.1, and 6.9 days, respectively. Forests have a longer response time to flash droughts than grasses and crops due to their deeper root systems, and irrigation can mitigate the impacts of flash droughts. Our method, based on cross-spectral analysis and the ET-based drought index, is innovative and can provide an earlier warning of impending drought impacts, rather than waiting for the irreversible impacts to occur. The information detected at an earlier stage of flash droughts can help decision makers in developing more effective and timely strategies to mitigate the impact of flash droughts on ecosystems. Full article
(This article belongs to the Special Issue Multi-Sensor Remote Sensing for Drought Characterization and Monitoring)
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19 pages, 3589 KiB  
Article
Hydrologic Consistency of Multi-Sensor Drought Observations in Forested Environments
by Konstantinos M. Andreadis, Dean Meason, Priscilla Corbett-Lad, Barbara Höck and Narendra Das
Remote Sens. 2024, 16(5), 852; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16050852 - 29 Feb 2024
Viewed by 530
Abstract
Drought can have significant impacts on forests, with long periods of water scarcity leading to water stress in trees and possible damages to their eco-physiological functions. Satellite-based remote sensing offers a valuable tool for monitoring and assessing drought conditions over large and remote [...] Read more.
Drought can have significant impacts on forests, with long periods of water scarcity leading to water stress in trees and possible damages to their eco-physiological functions. Satellite-based remote sensing offers a valuable tool for monitoring and assessing drought conditions over large and remote forested regions. The objective of this study is to evaluate the hydrological consistency in the context of drought of precipitation, soil moisture, evapotranspiration, and land surface temperature observations against in situ measurements in a number of well-monitored sites in New Zealand. Results showed that drought indicators were better captured from soil moisture observations compared to precipitation satellite observations. Nevertheless, we found statistically significant causality relationships between the multi-sensor satellite observations (median p-values ranging from 0.001 to 0.019), with spatial resolution appearing to be an important aspect for the adequate estimation of drought characteristics. Understanding the limitations and capabilities of satellite observations is crucial for improving the accuracy of forest drought monitoring, which, in turn, will aid in sustainable forest management and the development of mitigation and adaptation strategies in the face of changing climate conditions. Full article
(This article belongs to the Special Issue Multi-Sensor Remote Sensing for Drought Characterization and Monitoring)
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