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Remote Sensing
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Using Satellite Images for Drought Monitoring
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Using Satellite Images for Drought Monitoring

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 13559

Special Issue Editor

Dr. Sang-Il Lee

E-Mail Website
Guest Editor
Professor, Dept. of Civil & Environmental Engineering, Dongguk University, 1-30 Pildong-ro, Jung-gu, Seoul 04620, Korea
Interests: hydrology; remote sensing; groundwater; water resources; water reuse

Special Issue Information

Dear Colleagues,

Climate change and its variability have made drought a recurrent phenomenon in many parts of the world. Frequent and severe drought often results in serious economic, social, and environmental crises. Producing reliable and timely information for decision makers is of the utmost importance.

Traditionally, drought assessment and monitoring efforts have been made with conventional methods that are based on point data. At the present, however, the frequency of using data from satellite sensors is ever-increasing in many aspects of practice related to drought.

Due to the fact that satellite imageries can provide continuous datasets that can be used to detect the onset of a drought as well as its duration and magnitude, remote sensing is considered to be far superior to conventional methods for drought monitoring and early warning applications. Of course, many difficult challenges are waiting for research attention in applying satellite data for drought monitoring.

This Special issue is aimed at archiving recent achievements in extracting knowledge from satellite imageries and its use for near real-time drought monitoring. We invite original papers on recent advances in drought monitoring technologies based on satellite images as well as review articles that summarize the current state of understanding in this field of study.

Dr.  Sang-Il Lee
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.

Keywords

  • Use of satellite images for drought monitoring and forecasting
  • Assessment and management of risk and vulnerability due to drought
  • Drought indices
  • Soil moisture and evapotranspiration
  • Early warning
  • Utilization of AI for drought monitoring
  • Validation and verification of drought forecasting

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Published Papers (3 papers)

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Research

19 pages, 8689 KiB  
Article
Spatiotemporal Characteristics of Drought and Driving Factors Based on the GRACE-Derived Total Storage Deficit Index: A Case Study in Southwest China
by Tingtao Wu, Wei Zheng, Wenjie Yin and Hanwei Zhang
Remote Sens. 2021, 13(1), 79; https://doi.org/10.3390/rs13010079 - 28 Dec 2020
Cited by 21 | Viewed by 3252
Abstract
Drought monitoring is useful to minimize the impact of drought on human production and the natural environment. Gravity Recovery and Climate Experiment (GRACE) satellites can directly capture terrestrial water storage anomalies (TWSA) in the large basin, which represents a new source of hydrological [...] Read more.
Drought monitoring is useful to minimize the impact of drought on human production and the natural environment. Gravity Recovery and Climate Experiment (GRACE) satellites can directly capture terrestrial water storage anomalies (TWSA) in the large basin, which represents a new source of hydrological information. In this study, the GRACE-based total storage deficit index (TSDI) is employed to investigate the temporal evolution and spatial distribution of drought in Southwest China from 2003 to 2016. The comparison results of TSDI with the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrating Palmer drought severity index (SC-PDSI) show that TSDI has significant consistency with them, which verifies the reliability of TSDI. The spatial distribution of TSDI was more consistent with the governmental drought reports than SC-PDSI in the most severe drought event from September 2009 to April 2010. Finally, the links between drought and climate indicators are investigated using the partial least square regression (PLSR) model. The results show that insufficient precipitation has the most significant impact on drought in Southwest China, followed by excessive evaporation. Although Southwest China is selected as a case study in this paper, the method can be applied in other regions as well. Full article
(This article belongs to the Special Issue Using Satellite Images for Drought Monitoring)
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21 pages, 4988 KiB  
Article
Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output
by Sumin Park, Jungho Im, Daehyeon Han and Jinyoung Rhee
Remote Sens. 2020, 12(21), 3499; https://doi.org/10.3390/rs12213499 - 24 Oct 2020
Cited by 27 | Viewed by 4077
Abstract
Drought forecasting is essential for effectively managing drought-related damage and providing relevant drought information to decision-makers so they can make appropriate decisions in response to drought. Although there have been great efforts in drought-forecasting research, drought forecasting on a short-term scale (up to [...] Read more.
Drought forecasting is essential for effectively managing drought-related damage and providing relevant drought information to decision-makers so they can make appropriate decisions in response to drought. Although there have been great efforts in drought-forecasting research, drought forecasting on a short-term scale (up to two weeks) is still difficult. In this research, drought-forecasting models on a short-term scale (8 days) were developed considering the temporal patterns of satellite-based drought indices and numerical model outputs through the synergistic use of convolutional long short term memory (ConvLSTM) and random forest (RF) approaches over a part of East Asia. Two widely used drought indices—Scaled Drought Condition Index (SDCI) and Standardized Precipitation Index (SPI)—were used as target variables. Through the combination of temporal patterns and the upcoming weather conditions (numerical model outputs), the overall performances of drought-forecasting models (ConvLSTM and RF combined) produced competitive results in terms of r (0.90 and 0.93 for validation SDCI and SPI, respectively) and nRMSE (0.11 and 0.08 for validation of SDCI and SPI, respectively). Furthermore, our short-term drought-forecasting model can be effective regardless of drought intensification or alleviation. The proposed drought-forecasting model can be operationally used, providing useful information on upcoming drought conditions with high resolution (0.05°). Full article
(This article belongs to the Special Issue Using Satellite Images for Drought Monitoring)
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18 pages, 3181 KiB  
Article
Agricultural Drought Monitoring by MODIS Potential Evapotranspiration Remote Sensing Data Application
by Kamil Szewczak, Helena Łoś, Rafał Pudełko, Andrzej Doroszewski, Łukasz Gluba, Mateusz Łukowski, Anna Rafalska-Przysucha, Jan Słomiński and Bogusław Usowicz
Remote Sens. 2020, 12(20), 3411; https://doi.org/10.3390/rs12203411 - 17 Oct 2020
Cited by 17 | Viewed by 4105
Abstract
The current Polish Agricultural Drought Monitoring System (ADMS) adopted Climatic Water Balance (CWB) as the main indicator of crop losses caused by drought conditions. All meteorological data needed for CWB assessment are provided by the ground meteorological stations network. In 2018, [...] Read more.
The current Polish Agricultural Drought Monitoring System (ADMS) adopted Climatic Water Balance (CWB) as the main indicator of crop losses caused by drought conditions. All meteorological data needed for CWB assessment are provided by the ground meteorological stations network. In 2018, the network consisted of 665 stations, among which in only 58 stations full weather parameters were registered. Therefore, only these stations offered a possibility to estimate the exact values of potential evapotranspiration, which is a component of the CWB algorithm. This limitation affects the quality of CWB raster maps, interpolated on the basis of the meteorological stations network for the entire country. However, the interpolation process itself may introduce errors; therefore, the adaptation of satellite data (that are spatially continuous) should be taken into account, even if the lack of data due to cloudiness remains a serious problem. In this paper, we involved the remote sensing data from MODIS instrument and considered the ability to integrate those data with values determined by using ground measurements. The paper presents results of comparisons for the CWB index assessed using ground station data and those obtained from potential evapotranspiration as the product from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing instrument. The comparisons of results were performed for specific points (locations of ground stations) and were expressed by differences in means values. Analysis of Pearson’s correlation coefficient (r), Mann–Kendal trend test (Z-index), mean absolute error (MAE) and root mean square error (RMSE) for ten years’ series were evaluated and are presented. In addition, the basic spatial interpretation of results has been proposed. The correlation test revealed the r coefficient in the range from 0.06 to 0.68. The results show good trend agreement in time between two types of CWB with constantly higher values of this index, which is estimated using ground measurement data. In results for 34 (from 43 analyzed) stations the Mann–Kendal test provide the consistent trend, and only nine trends were inconsistent. Analyses revealed that the disagreement between the two considered indices (determined in different ways) increased significantly in the warmer period with a significant break point between R7 and R8 that falls at the end of May for each examined year. The value of MAE varied from 80 mm to 135 mm. Full article
(This article belongs to the Special Issue Using Satellite Images for Drought Monitoring)
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