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Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting

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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 2022) | Viewed by 38180

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Prof. Dr. Yuei-An Liou

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1. Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli District, Taoyuan City 32001, Taiwan
2. Taiwan Group on Earth Observations, Hsinchu, Taiwan
Interests: remote sensing; atmospheric science; environmental monitoring; disaster analysis
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Special Issue Information

Dear Colleagues,

Droughts are very common phenomena which impose serious challenges to 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.

You may choose our Joint Special Issue in Water.

Prof. Dr. Yuei-An Liou
Guest Editor

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Keywords

drought indicators
drought forecasting
drought monitoring
drought risk assessment
evapotranspiration
machine learning

Published Papers (10 papers)

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Research

22 pages, 3894 KiB

Open AccessArticle

Ecological Drought Condition Index to Monitor Vegetation Response to Meteorological Drought in Korean Peninsula

by Jeongeun Won and Sangdan Kim

Remote Sens. 2023, 15(2), 337; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020337 - 06 Jan 2023

Cited by 10 | Viewed by 2447

Abstract

Droughts caused by meteorological factors such as a long-term lack of precipitation can propagate into several types of drought through the hydrological cycle. Among them, a phenomenon in which drought has a significant impact on the ecosystem can be defined as an ecological drought. In this study, the Ecological Drought Condition Index-Vegetation (EDCI-veg) was newly proposed to quantitatively evaluate and monitor the effects of meteorological drought on vegetation. A copula-based bivariate joint probability distribution between vegetation information and drought information was constructed, and EDCI-veg was derived from the joint probability model. Through the proposed EDCI-veg, it was possible to quantitatively estimate how much the vegetation condition was affected by the drought, and to identify the timing of the occurrence of the vegetation drought and the severity of the vegetation drought. In addition, as a result of examining the applicability of the proposed EDCI-veg by comparing past meteorological drought events with the corresponding vegetation conditions, it was found that EDCI-veg can reasonably monitor vegetation drought. It has been shown that the newly proposed EDCI-veg in this study can provide useful information on the ecological drought condition that changes with time. On the other hand, the ecological drought analysis based on the type of land cover showed that the response of vegetation to meteorological drought was different depending on the land cover. In particular, it was revealed that the vegetation inhabiting the forest has a relatively high resistance to meteorological drought. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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21 pages, 4938 KiB

Open AccessArticle

Drought Monitoring and Performance Evaluation Based on Machine Learning Fusion of Multi-Source Remote Sensing Drought Factors

by Yangyang Zhao, Jiahua Zhang, Yun Bai, Sha Zhang, Shanshan Yang, Malak Henchiri, Ayalkibet Mekonnen Seka and Lkhagvadorj Nanzad

Remote Sens. 2022, 14(24), 6398; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246398 - 19 Dec 2022

Cited by 10 | Viewed by 4269

Abstract

Drought is an extremely dangerous natural hazard that causes water crises, crop yield reduction, and ecosystem fires. Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; while the remote sensing drought indices cover larger areas but have poor accuracy. Applying data-driven models to fuse multi-source remote sensing data for reproducing composite drought index may help fill this gap and better monitor drought in terms of spatial resolution. Machine learning methods can effectively analyze the hierarchical and non-linear relationships between the independent and dependent variables, resulting in better performance compared with traditional linear regression models. In this study, seven drought impact factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) were used to reproduce the standard precipitation evapotranspiration index (SPEI) for Shandong province, China, from 2002 to 2020. Three machine learning methods, namely bias-corrected random forest (BRF), extreme gradient boosting (XGBoost), and support vector machines (SVM) were applied as regression models. Then, the best model was used to construct the spatial distribution of SPEI. The results show that the BRF outperforms XGBoost and SVM in SPEI estimation. The BRF model can effectively monitor drought conditions in areas without ground observation data. The BRF model provides comprehensive drought information by producing a spatial distribution of SPEI, which provides reliability for the BRF model to be applied in drought monitoring. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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17 pages, 3502 KiB

Open AccessArticle

Evaluation of Hybrid Wavelet Models for Regional Drought Forecasting

by Gilbert Hinge, Jay Piplodiya, Ashutosh Sharma, Mohamed A. Hamouda and Mohamed M. Mohamed

Remote Sens. 2022, 14(24), 6381; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246381 - 16 Dec 2022

Cited by 8 | Viewed by 1745

Abstract

Drought forecasting is essential for risk management and preparedness of drought mitigation measures. The present study aims to evaluate the effectiveness of the proposed hybrid technique for regional drought forecasting. Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and two wavelet techniques, namely, Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT), were evaluated in drought forecasting up to a lead time of six months. Standard error metrics were used to select optimal model parameters, such as number of inputs, number of hidden neurons, level of decomposition, and number of mother wavelets. Additionally, the performance of various mother wavelets, including the Haar wavelet (db1) and 19 Daubechies wavelets (db1 to db20), were evaluated. The results indicated that the ANN model produced better forecasts than the MLR model, whereas the hybrid models outperformed both ANN and MLR models, which failed to predict the SPI values for a lead time greater than two months. The performance of all the models was found to improve as the timescale increased from 3 to 12 months. However, all the models’ performances deteriorated as the lead time increased. The hybrid WPT-MLR was the best model for the study area. The findings indicated that a hybrid WPT-MLR model could be used for drought early warning systems in the study area. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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21 pages, 11512 KiB

Open AccessArticle

Analysis of Agronomic Drought in a Highly Anthropogenic Context Based on Satellite Monitoring of Vegetation and Soil Moisture

by Mehrez Zribi, Simon Nativel and Michel Le Page

Remote Sens. 2021, 13(14), 2698; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142698 - 08 Jul 2021

Cited by 7 | Viewed by 2345

Abstract

This paper aims to analyze agronomic drought in a highly anthropogenic, semiarid region, the western Mediterranean region. The proposed study is based on Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced SCATterometer (ASCAT) satellite data describing the dynamics of vegetation cover and soil water content through the Normalized Difference Vegetation Index (NDVI) and Soil Water Index (SWI). Two drought indices were analyzed: the Vegetation Anomaly Index (VAI) and the Moisture Anomaly Index (MAI). The dynamics of the VAI were analyzed as a function of land cover deduced from the Copernicus land cover map. The effect of land cover and anthropogenic agricultural activities such as irrigation on the estimation of the drought index VAI was analyzed. The VAI dynamics were very similar for the shrub and forest classes. The contribution of vegetation cover (VAI) was combined with the effect of soil water content (MAI) through a new drought index called the global drought index (GDI) to conduct a global analysis of drought conditions. The implementation of this combination on different test areas in the study region is discussed. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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21 pages, 5584 KiB

Open AccessArticle

Regional GNSS-Derived SPCI: Verification and Improvement in Yunnan, China

by Xiongwei Ma, Yibin Yao and Qingzhi Zhao

Remote Sens. 2021, 13(10), 1918; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101918 - 14 May 2021

Cited by 11 | Viewed by 2101

Abstract

From the aspect of global drought monitoring, improving the regional drought monitoring method is becoming increasingly important for the sustainable development of regional agriculture and the economy. The standardized precipitation conversion index (SPCI) calculated by the Global Navigation Satellite System (GNSS) observation is a new means for drought monitoring that has the advantages of simple calculation and real-time monitoring. However, only SPCI with a 12-month scale has been verified on a global scale, while its capability and applicability for monitoring drought at a short time scale in regional areas have never been investigated. Therefore, this study aims to evaluate the performance of SPCI at other time scales in Yunnan, China, and propose an improved method for SPCI. The data of six GNSS stations were selected to calculate SPCI; the standardized precipitation evapotranspiration index (SPEI) and composite meteorological drought index (CI) are introduced to evaluate the SPCI at a short time scale in Yunnan Province. In addition, a modified CI (MCI) was proposed to calibrate the SPCI because of its large bias in Yunnan. Experimental results show that (1) SPCI exhibits better agreement with CI in Yunnan Province when compared to SPEI; (2) the capability of SPCI for drought monitoring is superior to that of SPEI in Yunnan; and (3) the improved SPCI is more suitable for drought monitoring in Yunnan, with a relative bias of 5.43% when compared to the MCI. These results provide a new means for regional drought monitoring in Yunnan, which is significant for dealing with drought disasters and formulating related disaster prevention and mitigation policies. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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22 pages, 10872 KiB

Open AccessArticle

Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring

by Soo-Jin Lee, Nari Kim and Yangwon Lee

Remote Sens. 2021, 13(9), 1778; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091778 - 02 May 2021

Cited by 19 | Viewed by 4254

Abstract

Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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28 pages, 8997 KiB

Open AccessArticle

Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

by Foyez Ahmed Prodhan, Jiahua Zhang, Fengmei Yao, Lamei Shi, Til Prasad Pangali Sharma, Da Zhang, Dan Cao, Minxuan Zheng, Naveed Ahmed and Hasiba Pervin Mohana

Remote Sens. 2021, 13(9), 1715; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091715 - 28 Apr 2021

Cited by 36 | Viewed by 6031

Abstract

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R² ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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15 pages, 3233 KiB

Open AccessArticle

Spatio-Temporal Assessment of Surface Moisture and Evapotranspiration Variability Using Remote Sensing Techniques

by Mai Son Le and Yuei-An Liou

Remote Sens. 2021, 13(9), 1667; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091667 - 24 Apr 2021

Cited by 11 | Viewed by 2538

Abstract

The relationship between the physic features of the Earth’s surface and its temperature has been significantly investigated for further soil moisture assessment. In this study, the spatiotemporal impacts of surface properties on land surface temperature (LST) were examined by using Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) and meteorological data. The significant distinctions were observed during a crop growing season through the contrast in the correlation between different multi-spectral satellite indices and LST, in which the highest correlation of −0.65 was found when the Normalized Difference Latent heat Index (NDLI) was used. A new index, named as Temperature-soil Moisture Dryness Index (TMDI), is accordingly proposed to assess surface moisture and evapotranspiration (ET) variability. It is based on a triangle space where NDLI is set as a reference basis for examining surface water availability and the variation of LST is an indicator as a consequence of the cooling effect by ET. TMDI was evaluated against ET derived from the commonly-used model, namely Surface Energy Balance Algorithm for Land (SEBAL), as well as compared to the performance of Temperature Vegetation Dryness Index (TVDI). This study was conducted over five-time points for the 2014 winter crop growing season in southern Taiwan. Results indicated that TMDI exhibits significant sensitivity to surface moisture fluctuation by showing a strong correlation with SEBAL-derived ET with the highest correlation of −0.89 was found on 19 October. Moreover, TMDI revealed its superiority over TVDI in the response to a rapidly changing surface moisture due to water supply before the investigated time. It is suggested that TMDI is a proper and sensitive indicator to characterize the surface moisture and ET rate. Further exploitation of the usefulness of the TMDI in a variety of applications would be interesting. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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17 pages, 6829 KiB

Open AccessArticle

A Practical Satellite-Derived Vegetation Drought Index for Arid and Semi-Arid Grassland Drought Monitoring

by Sheng Chang, Hong Chen, Bingfang Wu, Elbegjargal Nasanbat, Nana Yan and Bulgan Davdai

Remote Sens. 2021, 13(3), 414; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030414 - 25 Jan 2021

Cited by 20 | Viewed by 3217

Abstract

In semi-arid pasture areas, drought may directly influence livestock production, cause economic losses, and accelerate the processes of desertification along with destructive human activities (i.e., overgrazing). The aim of this article is to analyze the disadvantages of several drought indices derived from remote sensing data and develop a new vegetation drought index (VDI) for monitoring of grassland drought with high temporal frequency (dekad) and fine spatial resolution (1 km). The site-based soil moisture data from the field campaign in 2014 and the fenced biomass values at nine sites from 2000 to 2015 were adopted for validation. The results indicate that the proposed VDI would better reflect the extent, severity, and changes of drought compared with single drought indices or the vegetation health index (VHI); specifically, the VDI is more closely related to site-based soil moisture, with R human increasing to approximately 0.07 compared with the VHI; and with normalized fenced biomass (NFB) values, with average R human increasing to approximately 0.11 compared with the VHI. However, the correlations between VHI and VDI with NFB values are relatively lower in desert steppe regions. Furthermore, regional drought-affected data (RDA) are used to ensure spatial consistency of the evaluation; the VDI map is in good agreement with the RDA map based on field measurements. The presented VDI shows reliable and stable drought monitoring ability, which will play an important role in the future drought monitoring of inland grassland. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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21 pages, 4849 KiB

Open AccessArticle

Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan

by Iman Rousta, Haraldur Olafsson, Md Moniruzzaman, Hao Zhang, Yuei-An Liou, Terence Darlington Mushore and Amitesh Gupta

Remote Sens. 2020, 12(15), 2433; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152433 - 29 Jul 2020

Cited by 63 | Viewed by 6893

Abstract

Drought has severe impacts on human society and ecosystems. In this study, we used data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) sensors to examine the drought effects on vegetation in Afghanistan from 2001 to 2018. The MODIS data included the 16-day 250-m composites of the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI) with Land Surface Temperature (LST) images with 1 km resolution. The TRMM data were monthly rainfalls with 0.1-degree resolution. The relationship between drought and index-defined vegetation variation was examined by using time series, regression analysis, and anomaly calculation. The results showed that the vegetation coverage for the whole country, reaching the lowest levels of 6.2% and 5.5% were observed in drought years 2001 and 2008, respectively. However, there is a huge inter-regional variation in vegetation coverage in the study period with a significant rising trend in Helmand Watershed with R = 0.66 (p value = 0.05). Based on VCI for the same two years (2001 and 2008), 84% and 72% of the country were subject to drought conditions, respectively. Coherently, TRMM data confirm that 2001 and 2008 were the least rainfall years of 108 and 251 mm, respectively. On the other hand, years 2009 and 2010 were registered with the largest vegetation coverage of 16.3% mainly due to lower annual LST than average LST of 14 degrees and partially due to their slightly higher annual rainfalls of 378 and 425 mm, respectively, than the historical average of 327 mm. Based on the derived VCI, 28% and 21% of the study area experienced drought conditions in 2009 and 2010, respectively. It is also found that correlations are relatively high between NDVI and VCI (r = 0.77, p = 0.0002), but slightly lower between NDVI and precipitation (r = 0.51, p = 0.03). In addition, LST played a key role in influencing the value of NDVI. However, both LST and precipitation must be considered together in order to properly capture the correlation between drought and NDVI. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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