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Drought Monitoring Using Satellite Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 50812

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Special Issue Editor

Prof. Dr. Won-Ho Nam

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Guest Editor

School of Social Safety and Systems Engineering, Hankyong National University, Anseong, Gyeonggi 17579, Korea
Interests: irrigation and drainage engineering; agricultural drought and water resources management; drought monitoring, mitigation, planning, and policy; risk and vulnerability management; remote sensing for drought monitoring and management; soil moisture and hydrologic/watershed modeling
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Special Issue Information

Dear Colleagues,

Drought has had large impacts on economies, societies, and the environment, and could become even more disruptive given the context of climate change characterized by increasing temperatures and more variable and extreme precipitation.

Understanding how droughts develop, evolve, and affect us is vital to the preparation and planning for droughts and to mitigating their impacts. Traditionally, climate-based drought indices calculated from station-based meteorological observations (e.g., precipitation and air temperature) have been used to characterize drought conditions throughout the world. Because these indices are based on ground-based point observations, they are typically spatially interpolated using spatial statistical techniques. As a result, traditional climate-based drought index maps often depict broad-scale drought patterns that do not depict local-scale spatial variations in drought conditions.

Satellite remote sensing has provided us with an alternative means of acquiring spatially detailed and more localized information about drought severity patterns because of the spectral observations that are collected across the entire landscape. Drought monitoring using satellite remote sensing can be used by agricultural producers, decision-makers relying on early warning information, policymakers, and other stakeholders to improve management decisions.

This Special Issue will focus on “Drought Monitoring using Satellite Remote Sensing”. We welcome novel research, reviews, and opinion pieces covering all related topics, including drought monitoring, drought planning and policy, drought forecasting, risk and vulnerability management, remote sensing for drought monitoring, soil moisture, vegetation monitoring, evapotranspiration, case-studies from the field, and policy positions.

Assist. Prof. Dr. Won-Ho Nam
Guest Editor

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Keywords

drought monitoring
drought planning and policy
drought forecasting
risk and vulnerability management
remote sensing for drought monitoring
soil moisture
vegetation monitoring
evapotranspiration
NDVI (Normalized Difference Vegetation Index)
crop yield

Published Papers (12 papers)

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Research

Jump to: Review

24 pages, 5484 KiB

Open AccessArticle

Agricultural Drought Detection with MODIS Based Vegetation Health Indices in Southeast Germany

by Simon Kloos, Ye Yuan, Mariapina Castelli and Annette Menzel

Remote Sens. 2021, 13(19), 3907; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193907 - 30 Sep 2021

Cited by 25 | Viewed by 4934

Abstract

Droughts during the growing season are projected to increase in frequency and severity in Central Europe in the future. Thus, area-wide monitoring of agricultural drought in this region is becoming more and more important. In this context, it is essential to know where and when vegetation growth is primarily water-limited and whether remote sensing-based drought indices can detect agricultural drought in these areas. To answer these questions, we conducted a correlation analysis between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) within the growing season from 2001 to 2020 in Bavaria (Germany) and investigated the relationship with land cover and altitude. In the second step, we applied the drought indices Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI) to primarily water-limited areas and evaluated them with soil moisture and agricultural yield anomalies. We found that, especially in the summer months (July and August), on agricultural land and grassland and below 800 m, NDVI and LST are negatively correlated and thus, water is the primary limiting factor for vegetation growth here. Within these areas and periods, the TCI and VHI correlate strongly with soil moisture and agricultural yield anomalies, suggesting that both indices have the potential to detect agricultural drought in Bavaria. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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14 pages, 3224 KiB

Open AccessArticle

Time Varying Spatial Downscaling of Satellite-Based Drought Index

by Hone-Jay Chu, Regita Faridatunisa Wijayanti, Lalu Muhamad Jaelani and Hui-Ping Tsai

Remote Sens. 2021, 13(18), 3693; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183693 - 15 Sep 2021

Cited by 6 | Viewed by 1976

Abstract

Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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15 pages, 1068 KiB

Open AccessArticle

Ambiguous Agricultural Drought: Characterising Soil Moisture and Vegetation Droughts in Europe from Earth Observation

by Theresa C. van Hateren, Marco Chini, Patrick Matgen and Adriaan J. Teuling

Remote Sens. 2021, 13(10), 1990; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101990 - 19 May 2021

Cited by 24 | Viewed by 4187

Abstract

Long-lasting precipitation deficits or heat waves can induce agricultural droughts, which are generally defined as soil moisture deficits that are severe enough to negatively impact vegetation. However, during short soil moisture drought events, the vegetation is not always negatively affected and sometimes even thrives. Due to this duality in agricultural drought impacts, the term “agricultural drought” is ambiguous. Using the ESA’s remotely sensed CCI surface soil moisture estimates and MODIS NDVI vegetation greenness data, we show that, in major European droughts over the past two decades, asynchronies and discrepancies occurred between the surface soil moisture and vegetation droughts. A clear delay is visible between the onset of soil moisture drought and vegetation drought, with correlations generally peaking at the end of the growing season. At lower latitudes, correlations peaked earlier in the season, likely due to an earlier onset of water limited conditions. In certain cases, the vegetation showed a positive anomaly, even during soil moisture drought events. As a result, using the term agricultural drought instead of soil moisture or vegetation drought, could lead to the misclassification of drought events and false drought alarms. We argue that soil moisture and vegetation drought should be considered separately. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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35 pages, 8655 KiB

Open AccessArticle

Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

by El houssaine Bouras, Lionel Jarlan, Salah Er-Raki, Clément Albergel, Bastien Richard, Riad Balaghi and Saïd Khabba

Remote Sens. 2020, 12(24), 4018; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244018 - 08 Dec 2020

Cited by 28 | Viewed by 4246

Abstract

In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000–2017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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20 pages, 4426 KiB

Open AccessArticle

Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

by Ankit Shekhar, Jia Chen, Shrutilipi Bhattacharjee, Allan Buras, Antony Oswaldo Castro, Christian S. Zang and Anja Rammig

Remote Sens. 2020, 12(19), 3249; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193249 - 06 Oct 2020

Cited by 27 | Viewed by 4207

Abstract

The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent, resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on the terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory-2 (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly in the spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean conditions (i.e., those in 2015–2017), we found a change in the intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring, followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about a 31% decrease in SIF values during July and August as compared to the mean over the previous three years. Furthermore, our MODIS (Moderate Resolution Imaging Spectroradiometer) and OCO-2 comparative results indicate that especially for coniferous and mixed forests, OCO-2 SIF has a quicker response and a possible higher sensitivity to drought in comparison to MODIS’s fPAR (fraction of absorbed photosynthetically active radiation) and the Normalized Difference Vegetation Index (NDVI) when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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22 pages, 12263 KiB

Open AccessArticle

Preliminary Utility of the Retrospective IMERG Precipitation Product for Large-Scale Drought Monitoring over Mainland China

by Linyong Wei, Shanhu Jiang, Liliang Ren, Linqi Zhang, Menghao Wang and Zheng Duan

Remote Sens. 2020, 12(18), 2993; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182993 - 15 Sep 2020

Cited by 19 | Viewed by 3902

Abstract

This study evaluated the suitability of the latest retrospective Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 (IMERG) Final Run product with a relatively long period (beginning from June 2000) for drought monitoring over mainland China. First, the accuracy of IMERG was evaluated by using observed precipitation data from 807 meteorological stations at multiple temporal (daily, monthly, and yearly) and spatial (pointed and regional) scales. Second, the IMERG-based standardized precipitation index (SPI) was validated and analyzed through statistical indicators. Third, a light–extreme–light drought-event process was adopted as the case study to dissect the latent performance of IMERG-based SPI in capturing the spatiotemporal variation of drought events. Our results demonstrated a sufficient consistency and small error of the IMERG precipitation data against the gauge observations with the regional mean correlation coefficient (CC) at the daily (0.7), monthly (0.93), and annual (0.86) scales for mainland China. The IMERG possessed a strong capacity for estimating intra-annual precipitation changes; especially, it performed well at the monthly scale. There was a strong agreement between the IMERG-based SPI values and gauge-based SPI values for drought monitoring in most regions in China (with CCs above 0.8). In contrast, there was a comparatively poorer capability and notably higher heterogeneity in the Xinjiang and Qinghai-Tibet Plateau regions with more widely varying statistical metrics. The IMERG featured the advantage of satisfactory spatiotemporal accuracy in terms of depicting the onset and extinction of representative drought disasters for specific consecutive months. Furthermore, the IMERG has obvious drought monitoring abilities, which was also complemented when compared with the Precipitation Estimation from the Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The outcomes of this study demonstrate that the retrospective IMERG can provide a more competent data source and potential opportunity for better drought monitoring utility across mainland China, particularly for eastern China. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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18 pages, 3817 KiB

Open AccessArticle

Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China

by Yunqian Wang, Jing Yang, Yaning Chen, Zhicheng Su, Baofu Li, Hao Guo and Philippe De Maeyer

Remote Sens. 2020, 12(14), 2298; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142298 - 17 Jul 2020

Cited by 12 | Viewed by 3639

Abstract

Droughts are one of the costliest natural disasters. Reliable drought monitoring and prediction are valuable for drought relief management. This study monitors and predicts droughts in Xinjiang, an arid area in China, based on the three drought indicators, i.e., the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the Multivariate Standardized Drought Index (MSDI). Results indicate that although these three indicators could capture severe historical drought events in the study area, the spatial coverage, persistence and severity of the droughts would vary regarding different indicators. The MSDI could best describe the overall drought conditions by incorporating the characteristics of the SPI and SSMI. For the drought prediction, the predictive skill of all indicators gradually decayed with the increasing lead time. Specifically, the SPI only showed the predictive skill at a 1-month lead time, the MSDI performed best in capturing droughts at 1- to 2-month lead times and the SSMI was accurate up to a 3-month lead time owing to its high persistence. These findings might provide scientific support for the local drought management. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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23 pages, 10968 KiB

Open AccessArticle

Different Drought Legacies of Rain-Fed and Irrigated Croplands in a Typical Russian Agricultural Region

by Yuanhuizi He, Fang Chen, Huicong Jia, Lei Wang and Valery G. Bondur

Remote Sens. 2020, 12(11), 1700; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111700 - 26 May 2020

Cited by 12 | Viewed by 2907

Abstract

Droughts are one of the primary natural disasters that affect agricultural economies, as well as the fire hazards of territories. Monitoring and researching droughts is of great importance for agricultural disaster prevention and reduction. The research significance of investigating the hysteresis of agricultural to meteorological droughts is to provide an important reference for agricultural drought monitoring and early warnings. Remote sensing drought monitoring indices can be employed for rapid and accurate drought monitoring at regional scales. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and the surface temperature product are used as the data sources. Calculating the temperature vegetation drought index (TVDI) and constructing a comprehensive drought disaster index (CDDI) based on the crop growth period allowed drought conditions and spatiotemporal evolution patterns in the Volgograd region in 2010 and 2012 to be effectively monitored. The causes of the drought were then analyzed based on the sensitivity of a drought to meteorological factors in rain-fed and irrigated lands. Finally, the lag time of agricultural to meteorological droughts and the hysteresis in different growth periods were analyzed using statistical analyses. The research shows that (1) the main drought patterns in 2010 were spring droughts from April to May and summer droughts from June to August, and the primary drought patterns in 2012 were spring droughts from April to June, with an affected area that reached 3.33% during the growth period; (2) local drought conditions are dominated by the average surface temperature factor. Rain-fed lands are sensitive to the temperature and are therefore prone to summer droughts. Irrigated lands are more sensitive to water shortages in the spring and less sensitive to extremely high temperature conditions; (3) there is a certain lag between meteorological and agricultural droughts during the different growth stages. The strongest lag relationship was found in the planting stage and the weakest one was found in the dormancy stage. Therefore, the meteorological drought index in the growth period has a better predictive ability for agricultural droughts during the appropriately selected growth stages. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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15 pages, 6011 KiB

Open AccessArticle

Soil Moisture–Vegetation–Carbon Flux Relationship under Agricultural Drought Condition using Optical Multispectral Sensor

by Chanyang Sur, Do-Hyuk Kang, Kyoung Jae Lim, Jae E. Yang, Yongchul Shin and Younghun Jung

Remote Sens. 2020, 12(9), 1359; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091359 - 25 Apr 2020

Cited by 6 | Viewed by 3360

Abstract

Agricultural drought is triggered by a depletion of moisture content in the soil, which hinders photosynthesis and thus increases carbon dioxide (CO₂) concentrations in the atmosphere. The aim of this study is to analyze the relationship between soil moisture (SM) and vegetation activity toward quantifying CO₂ concentration in the atmosphere. To this end, the MODerate resolution imaging spectroradiometer (MODIS), an optical multispectral sensor, was used to evaluate two regions in South Korea for validation. Vegetation activity was analyzed through MOD13A1 vegetation indices products, and MODIS gross primary productivity (GPP) product was used to calculate the CO₂ flux based on its relationship with respiration. In the case of SM, it was calculated through the method of applying apparent thermal inertia (ATI) in combination with land surface temperature and albedo. To validate the SM and CO₂ flux, flux tower data was used which are the observed measurement values for the extreme drought period of 2014 and 2015 in South Korea. These two variables were analyzed for temporal variation on flux tower data as daily time scale, and the relationship with vegetation index (VI) was synthesized and analyzed on a monthly scale. The highest correlation between SM and VI (correlation coefficient (r) = 0.82) was observed at a time lag of one month, and that between VI and CO₂ (r = 0.81) at half month. This regional study suggests a potential capability of MODIS-based SM, VI, and CO₂ flux, which can be applied to an assessment of the global view of the agricultural drought by using available satellite remote sensing products. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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Review

Jump to: Research

34 pages, 3542 KiB

Open AccessFeature PaperReview

Optical and Thermal Remote Sensing for Monitoring Agricultural Drought

by Qiming Qin, Zihua Wu, Tianyuan Zhang, Vasit Sagan, Zhaoxu Zhang, Yao Zhang, Chengye Zhang, Huazhong Ren, Yuanheng Sun, Wei Xu and Cong Zhao

Remote Sens. 2021, 13(24), 5092; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245092 - 15 Dec 2021

Cited by 15 | Viewed by 5032

Abstract

By effectively observing the land surface and obtaining farmland conditions, satellite remote sensing has played an essential role in agricultural drought monitoring over past decades. Among all remote sensing techniques, optical and thermal remote sensing have the most extended history of being utilized in drought monitoring. The primary goal of this paper is to illustrate how optical and thermal remote sensing have been and will be applied in the monitoring, assessment, and prediction of agricultural drought. We group the methods into four categories: optical, thermal, optical and thermal, and multi-source. For each category, a concise explanation is given to show the inherent mechanisms. We pay special attention to solar-induced chlorophyll fluorescence, which has great potential in early drought detection. Finally, we look at the future directions of agricultural drought monitoring, including (1) early detection; (2) spatio-temporal resolution; (3) organic combination of multi-source data; and (4) smart prediction and assessment based on deep learning and cloud computing. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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14 pages, 2779 KiB

Open AccessReview

Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward

by Gilbert Hinge, Mohamed M. Mohamed, Di Long and Mohamed A. Hamouda

Remote Sens. 2021, 13(21), 4353; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214353 - 29 Oct 2021

Cited by 16 | Viewed by 2960

Abstract

In recent years, satellite precipitation products (SPPs) have emerged as an essential source of data and information. This work intends to summarize lessons learnt on using SPPs for drought monitoring and to propose ways forward in this field of research. A thorough literature review was conducted to review three aspects: effects of climate type, data record length, and time scale on SPPs performance. The conducted meta-analysis showed that the performance of SPPs for drought monitoring largely depends upon the climate type of the location and length of the data record. SPPs drought monitoring performance was shown to be higher in temperate and tropical climates than in dry and continental ones. SPPs were found to perform better with an increase in data record length. From a general standpoint, SPPs offer great potential for drought monitoring, but the performance of SPPs needs to be improved for operational purposes. The present study discusses blending SPPs with in situ data and other lessons learned, as well as future directions of using SPPs for drought applications. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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24 pages, 2113 KiB

Open AccessReview

Reviewing the Potential of Sentinel-2 in Assessing the Drought

by Dani Varghese, Mirjana Radulović, Stefanija Stojković and Vladimir Crnojević

Remote Sens. 2021, 13(17), 3355; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173355 - 24 Aug 2021

Cited by 23 | Viewed by 6185

Abstract

This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated. Full article

(This article belongs to the Special Issue Drought Monitoring Using Satellite Remote Sensing)

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