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Remote Sensing of Drought Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 99354

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Special Issue Editors

Prof. Dr. Tsegaye Tadesse

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Guest Editor

National Drought Mitigation Center, School of Natural Resources, University of Nebraska-Lincoln /815 Hardin Hall, Lincoln, NE 68583-0988, USA
Interests: drought and vegetation monitoring; remote sensing; agricultural development; food security, and climate change/variability at national and international levels
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Brian Wardlow

E-Mail Website
Guest Editor

Center for Advanced Land Management Information Technologies (CALMIT), School of Natural Resources, University of Nebraska-Lincoln, 1400 R Street, Lincoln, NE 68588, USA
Interests: drought monitoring and early warning; land use/land cver characterization; land surface phenology; ecological and natural resource applications

Special Issue Information

Dear Colleagues,

Drought is a complex and recurring natural disaster that occurs throughout the world and often has negative impacts on many sectors of society. Drought monitoring is challenging given the complex spatio-temporal dimensions of drought and its severity. Traditionally, drought monitoring has relied mainly upon climate-based indicators and indices such as the Standardized Precipitation Index and the Palmer Drought Severity Index. These climate-based indicators have proven useful for many applications. However, the spatial variability in drought conditions depicted in the associated maps are at a relatively broad scale, and often contain limited information about local-scale variations in drought severity across the landscape. In addition, climate-based drought indices maps may have a limited value because they provide a generalized spatial view of drought conditions and variations across large areas. Thus, improved and effective drought monitoring approaches are critical for supporting early warning systems and pro-active drought planning.

In the past few decades, satellite-based remote sensing has provided relatively high spatial resolution (i.e., local to synoptic scale) and high temporal resolution (i.e., hours to days) observations of the Earth. Remotely sensed imagery provides spatial continuous spectral measures across large areas that reflect both atmospheric and land surface characteristics. As a result, remote sensing data has been increasingly used for large-area drought monitoring. For example, several satellite-derived vegetation indices have been developed to monitor drought from local to global scales. Researchers are making progress in developing better drought monitoring tools to assess drought-related vegetation stress and evaluating with ground observations. In recent years, hybrid drought indices that integrate climate, satellite, and environmental data have been developed. In addition, remote sensing data collected by several recent satellite-based instruments have also been used to estimate several key variables related to drought that include land surface temperature, evapotranspiration, soil moisture, and precipitation. Satellite-based microwave and radar instruments are also increasingly being used for soil moisture and precipitation estimation.

Currently, an increasing number of new and/or more sophisticated remote sensing techniques have been used for estimating vegetation drought stress, evapotranspiration, soil moisture, ground water fluxes, and precipitation. As a result, the demand for the development of operational drought monitoring and early warning system (EWS) using these new technologies is growing in many parts of the world. Improved operational EWS may need more sophisticated analysis and modeling techniques, as well as improved scientific knowledge from the basic research. This Special Issue of Remote Sensing discusses recent advances in drought monitoring and prediction, presenting case studies conducted all over the world. Among the topics to be discussed are:

New and improved remote sensing-based drought indices that could help in identifying, classifying, and communicating drought conditions
Earth observations that include satellite, climate, oceanic, and biophysical data for efficient drought analysis and improved seasonal prediction
Improved modelling techniques to combine or integrate drought indices based on various drought indicators
Satellite-based soil moisture and evapotranspiration estimation
Remote sensing-based precipitation estimation and evaluation
Data mining and GIS applications to drought monitoring and prediction
Building Drought Early Warning Systems (DEWSs) integrating remote sensing data
Use of remote sensing data and applications for food security

Original research on these topics will be welcome for this Special Issue.

Dr. Tsegaye Tadesse
Prof. Brian D Wardlow
Guest Editors

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Keywords

Drought monitoring and prediction
Hybrid drought indices
Satellite-derived Climate data
Vegetation monitoring
Satellite-derived Evapotranspiration
Soil moisture and groundwater estimation
Drought impact and Food security

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Research

21 pages, 11036 KiB

Open AccessArticle

Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa

by Willibroad Gabila Buma and Sang-Il Lee

Remote Sens. 2019, 11(21), 2534; https://doi.org/10.3390/rs11212534 - 29 Oct 2019

Cited by 21 | Viewed by 5912

Abstract

As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data. Full article

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

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25 pages, 8575 KiB

Open AccessArticle

Monitoring Drought Impact on Annual Forage Production in Semi-Arid Grasslands: A Case Study of Nebraska Sandhills

by Markéta Poděbradská, Bruce K. Wylie, Michael J. Hayes, Brian D. Wardlow, Deborah J. Bathke, Norman B. Bliss and Devendra Dahal

Remote Sens. 2019, 11(18), 2106; https://doi.org/10.3390/rs11182106 - 9 Sep 2019

Cited by 15 | Viewed by 5822

Abstract

Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha⁻¹*yr⁻¹, was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R² = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses. Full article

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

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24 pages, 9282 KiB

Open AccessArticle

Assessing the Remotely Sensed Evaporative Drought Index for Drought Monitoring over Northeast China

by Lilin Zhang, Yunjun Yao, Xiangyi Bei, Kun Jia, Xiaotong Zhang, Xianhong Xie, Bo Jiang, Ke Shang, Jia Xu and Xiaowei Chen

Remote Sens. 2019, 11(17), 1960; https://doi.org/10.3390/rs11171960 - 21 Aug 2019

Cited by 14 | Viewed by 4903

Abstract

Many existing satellite evapotranspiration (ET)-based drought indices have characterized regional drought condition successfully, but the relatively short time span of ET products limits their use in long-term climatological drought assessment. In this study, we assess Evaporative Drought Index (EDI) as a drought monitoring indicator over Northeast China through a retrospective comparison with drought-related indicators. After verifying its utility for detecting documented regional drought events and impacts of drought on crop production, we apply it to improve our understanding of the variation in dryness over Northeast China from 1982 to 2015. Our results illustrate that EDI is generally effective for characterizing terrestrial moisture condition and its standardized formula, namely, Standardized Evaporative Drought Index (sEDI) corresponds well with historical drought events and inter-annual grain crop yields over Northeast China. Although the calculation of sEDI does not directly incorporate precipitation and soil moisture, statistical analyses indicate sEDI can detect drought in accordance with the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI), with the highest correlations found in the west part of Northeast China (R < −0.7). Further analysis illustrates sEDI is more related to commonly-used drought metrics over areas with short canopy vegetation (R < −0.5) than woodland (R < −0.2), which suggests precipitation may not be a good representative of drought condition over areas with deep-rooted vegetation. Then, we find 56.5% of Northeast China shows an upward dry trend from 1982 to 2015, which mainly concentrates in the west part of the study area. Conversely, 14.4% of Northeast China shows a significant wetted trend and most of them locate at cropland areas, due to the improved water management. This study suggests that EDI is a feasible method to monitor spatially distributed drought condition and can provide unique drought information not reflected by rainfall deficits, which also can be used to evaluate traditional precipitation-based indicators. Full article

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

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22 pages, 5291 KiB

Open AccessArticle

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

by Li Hua, Huidong Wang, Haigang Sui, Brian Wardlow, Michael J. Hayes and Jianxun Wang

Remote Sens. 2019, 11(16), 1873; https://doi.org/10.3390/rs11161873 - 10 Aug 2019

Cited by 43 | Viewed by 6603

Abstract

Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days. Full article

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

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21 pages, 7139 KiB

Open AccessArticle

The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics

by Sophie Reinermann, Ursula Gessner, Sarah Asam, Claudia Kuenzer and Stefan Dech

Remote Sens. 2019, 11(15), 1783; https://doi.org/10.3390/rs11151783 - 30 Jul 2019

Cited by 54 | Viewed by 9034

Abstract

Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter- and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000–2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ. Full article

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

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21 pages, 6846 KiB

Open AccessArticle

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

by Jae-Hyun Ryu, Kyung-Soo Han, Yang-Won Lee, No-Wook Park, Sungwook Hong, Chu-Yong Chung and Jaeil Cho

Remote Sens. 2019, 11(15), 1773; https://doi.org/10.3390/rs11151773 - 27 Jul 2019

Cited by 15 | Viewed by 5353

Abstract

Satellite-based remote sensing techniques have been widely used to monitor droughts spanning large areas. Various agricultural drought indices have been developed to assess the intensity of agricultural drought and to detect damaged crop areas. However, to better understand the responses of agricultural drought to meteorological drought, agricultural management practices should be taken into consideration. This study aims to evaluate the responses to drought under different forms of agricultural management for the extreme drought that occurred on the Korean Peninsula in 2014 and 2015. The 3-month standardized precipitation index (SPI3) and the 3-month vegetation health index (VHI3) were selected as a meteorological drought index and an agricultural drought index, respectively. VHI3, which comprises the 3-month temperature condition index (TCI3) and the 3-month vegetation condition index (VCI3), differed significantly in the study area during the extreme drought. VCI3 had a different response to the lack of precipitation in South and North Korea because it was affected by irrigation. However, the time series of TCI3 were similar in South and North Korea. These results meant that each drought index has different characteristics and should be utilized with caution. Our results are expected to help comprehend the responses of the agricultural drought index on meteorological drought depending on agricultural management. Full article

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

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30 pages, 7351 KiB

Open AccessArticle

A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring

by Chrisgone Adede, Robert Oboko, Peter Waiganjo Wagacha and Clement Atzberger

Remote Sens. 2019, 11(9), 1099; https://doi.org/10.3390/rs11091099 - 8 May 2019

Cited by 38 | Viewed by 6542

Abstract

Droughts, with their increasing frequency of occurrence, especially in the Greater Horn of Africa (GHA), continue to negatively affect lives and livelihoods. For example, the 2011 drought in East Africa caused massive losses, documented to have cost the Kenyan economy over 12 billion US dollars. Consequently, the demand is ever-increasing for ex-ante drought early warning systems with the ability to offer drought forecasts with sufficient lead times The study uses 10 precipitation and vegetation condition indices that are lagged over 1, 2 and 3-month time-steps to predict future values of vegetation condition index aggregated over a 3-month time period (VCI3M) that is a proxy variable for drought monitoring. The study used data covering the period 2001–2015 at a monthly frequency for four arid northern Kenya counties for model training, with data for 2016–2017 used as out-of-sample data for model testing. The study adopted a model space search approach to obtain the most predictive artificial neural network (ANN) model as opposed to the traditional greedy search approach that is based on optimal variable selection at each model building step. The initial large model-space was reduced using the general additive model (GAM) technique together with a set of assumptions. Even though we built a total of 102 GAM models, only 20 had R² ≥ 0.7, and together with the model with lag of the predicted variable, were subjected to the ANN modelling process. The ANN process itself uses the brute-force approach that automatically partitions the training data into 10 sub-samples, builds the ANN models in these samples and evaluates their performance using multiple metrics. The results show the superiority of 1-month lag of the variables as compared to longer time lags of 2 and 3 months. The best ANN model recorded an R² of 0.78 between actual and predicted vegetation conditions 1-month ahead using the out-of-sample data. Investigated as a classifier distinguishing five vegetation deficit classes, the best ANN model had a modest accuracy of 67% and a multi-class area under the receiver operating characteristic curve (AUROC) of 89.99%. Full article

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

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19 pages, 4559 KiB

Open AccessEditor’s ChoiceArticle

Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions

by Catherine Champagne, Jenelle White, Aaron Berg, Stephane Belair and Marco Carrera

Remote Sens. 2019, 11(4), 372; https://doi.org/10.3390/rs11040372 - 13 Feb 2019

Cited by 23 | Viewed by 7048

Abstract

Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)’s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set’s sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X² = 170 for Day of Year 145), followed by the ESA-CCI (peak X² = 89 on Day of Year 129) and then the RDPS (peak X² = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18–20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10–15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data. Full article

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

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24 pages, 4941 KiB

Open AccessArticle

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

by Getachew Ayehu, Tsegaye Tadesse, Berhan Gessesse and Yibeltal Yigrem

Remote Sens. 2019, 11(2), 125; https://doi.org/10.3390/rs11020125 - 10 Jan 2019

Cited by 13 | Viewed by 6969

Abstract

In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e.,

α = 0.01

α = 0.05,

and

α = 0.1

). Thus, the AWS based SCA model with

α = 0.05

was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m³/m³ during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when

α

value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m³/m³ using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions. Full article

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

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18 pages, 33560 KiB

Open AccessArticle

Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia

by Seonyoung Park, Eunkyo Seo, Daehyun Kang, Jungho Im and Myong-In Lee

Remote Sens. 2018, 10(11), 1811; https://doi.org/10.3390/rs10111811 - 15 Nov 2018

Cited by 50 | Viewed by 8186

Abstract

Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is still challenging. This study has developed drought prediction models for a short-period of time (one pentad) using remote-sensing data and climate variability indices over East Asia (20°–50°N, 90°–150°E) through random forest machine learning. Satellite-based drought indices were calculated using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture, Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and normalized difference vegetation index (NDVI). The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions. Full article

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

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20 pages, 4487 KiB

Open AccessArticle

Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8

by Marcel Urban, Christian Berger, Tami E. Mudau, Kai Heckel, John Truckenbrodt, Victor Onyango Odipo, Izak P. J. Smit and Christiane Schmullius

Remote Sens. 2018, 10(9), 1482; https://doi.org/10.3390/rs10091482 - 17 Sep 2018

Cited by 53 | Viewed by 11130

Abstract

During the southern summer season of 2015 and 2016, South Africa experienced one of the most severe meteorological droughts since the start of climate recording, due to an exceptionally strong El Niño event. To investigate spatiotemporal dynamics of surface moisture and vegetation structure, data from ESA’s Copernicus Sentinel-1/-2 and NASA’s Landsat-8 for the period between March 2015 and November 2017 were utilized. In combination, these radar and optical satellite systems provide promising data with high spatial and temporal resolution. Sentinel-1 C-band data was exploited to derive surface moisture based on a hyper-temporal co-polarized (vertical-vertical—VV) radar backscatter change detection approach, describing dynamics between dry and wet seasons. Vegetation information from a TLS (Terrestrial Laser Scanner)-derived canopy height model (CHM), as well as the normalized difference vegetation index (NDVI) from Sentinel-2 and Landsat-8, were utilized to analyze vegetation structure types and dynamics with respect to the surface moisture index (SurfMI). Our results indicate that our combined radar–optical approach allows for a separation and retrieval of surface moisture conditions suitable for drought monitoring. Moreover, we conclude that it is crucial for the development of a drought monitoring system for savanna ecosystems to integrate land cover and vegetation information for analyzing surface moisture dynamics derived from Earth observation time series. Full article

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

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21 pages, 6245 KiB

Open AccessArticle

Monitoring and Assessment of Drought Focused on Its Impact on Sorghum Yield over Sudan by Using Meteorological Drought Indices for the Period 2001–2011

by Khalid. M. Elhag and Wanchang Zhang

Remote Sens. 2018, 10(8), 1231; https://doi.org/10.3390/rs10081231 - 6 Aug 2018

Cited by 30 | Viewed by 7050

Abstract

Currently, the high-resolution satellite images in near real-time have gained more popularity for natural disaster detection due to the unavailability and difficulty of acquiring frequent ground observation data over a wide region. In Sudan, the occurrence of drought events is a predominant natural disaster that causes substantial damages to crop production. Therefore, monitoring drought and measuring its impact on the agricultural sector remain major concerns of policymakers. The current study focused on assessing and analyzing drought characteristics based on two meteorological drought indices, namely the Standardized Precipitation Index (SPI) and the Drought Severity Index (DSI), and inferred the impact of drought on sorghum productivity in Sudan from 2001 to 2011. To identify the wet and dry areas, the deviations of tropical rainfall measuring mission (TRMM) precipitation products from the long-term mean from 2001 to 2011 were computed and mapped at a seasonal scale (July–October). Our findings indicated that the dry condition fluctuated over the whole of Sudan at various temporal and spatial scales. The DSI results showed that both the Kordofan and Darfur regions were affected by drought in the period 2001–2005, whereas most regions were affected by drought from 2008 to 2011. The spatial correlation between DSI, SPI-3, and TRMM precipitation products illustrated a significant positive correlation in agricultural lands and negative correlation in mountainous areas. The relationship between DSI and the Standardized variable of crop yield (St. Y) for sorghum yield was also investigated over two main agricultural regions (Central and Eastern regions) for the period 2001–2011, which revealed a good agreement between them, and a huge drop of sorghum yield also occurred in 2008–2011, corresponding to extreme drought indicated by DSI. The present study indicated that DSI can be used for agricultural drought monitoring and served as an alternative indicator for the estimation of crop yield over Sudan in some levels. Full article

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

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20 pages, 11944 KiB

Open AccessArticle

The Temporal-Spatial Characteristics of Drought in the Loess Plateau Using the Remote-Sensed TRMM Precipitation Data from 1998 to 2014

by Qi Zhao, Qianyun Chen, Mengyan Jiao, Pute Wu, Xuerui Gao, Meihong Ma and Yang Hong

Remote Sens. 2018, 10(6), 838; https://doi.org/10.3390/rs10060838 - 27 May 2018

Cited by 49 | Viewed by 5594

Abstract

Rainfall gauges are always sparse in the arid and semi-arid areas of Northwest China, which makes it difficult to precisely study the characteristics of drought at a large scale in this region and similar areas. This study used the TRMM (The Tropical Rainfall Measuring Mission) multi-satellite precipitation data to study the spatial-temporal evolution of drought in the Loess Plateau based on the SPI (Standardized Precipitation Index) drought index for the period of 1998–2014. The results indicate that the monthly TRMM precipitation data are well matched with the observed precipitation, indicating that this remotely sensed data set can be reliably used to calculate the SPI drought index. Based on the study findings, the average precipitation in the Loess Plateau is showing a significant increasing trend at the rate of 4.46 mm/year. From the spatial perspective, the average annual precipitation in the Southeast is generally greater than that in the Northwest. However, the annual precipitation in the Southeast area is showing a decreasing trend, whereas, the annual precipitation in the northwest areas is showing an increasing trend. Through the SPI analysis, the 3-month SPI and 12-month SPI were both showing an increasing trend, which indicates that the drought severity in the Loess Plateau was a generally declining trend at a seasonal to annual time scale. From the spatial perspective, the SPI values in the Central and Northwest of the Loess Plateau were significantly increasing, whereas, the SPI values in the southern area of the Loess Plateau were slightly decreasing. From the seasonal characteristics, the high-risk area for drought in the spring was concentrated in the northeast and southwest part, and in the summer and autumn, the high-risk area was transferred to the south part. Through this study, it is concluded that the Loess Plateau was likely getting wetter during the time period since the Grain-for-Green Project (1999–2012) was implemented, which replaced much farmland with forestry. This is a positive signal for vegetation recovery and ecological restoration in the near future. Full article

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

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