Drought disaster monitoring using MODIS derived index for drought years: A space-based information for ecosystems and environmental conservation

Orimoloye, Israel R.; Belle, Johanes A.; Ololade, Olusola O.

doi:10.1016/j.jenvman.2021.112028

Cited by 46 publications

(17 citation statements)

References 47 publications

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“…Drought is a complex phenomenon and one of the greatest recurring natural disasters. It causes substantial losses in agriculture sectors [1][2][3][4][5][6], natural ecosystems [7][8][9], and forestry [10][11][12]. It is also called a creeping phenomenon with its influences gradually take in an area over a period and may persist for a long period.…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression Analysis for Spatial Patterns of Drought Persistence

et al. 2021

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Drought is one of the natural hazards with potentially significant impacts on society, economy, and other natural resources over the globe. However, the understanding of drought characteristics and its persistence can significantly help to reduce the potential impacts of drought. Moreover, the knowledge about the spatiotemporal pattern of seasonal drought frequency and drought persistence is important for water resource management, agricultural development, energy consumption, and crop yields. Therefore, the present study is employed to examine the seasonal drought frequency and drought persistence in the region. In this regard, the standardized precipitation index (SPI) at the three-month time scale was used to determine meteorological drought. Furthermore, the logistic regression model is used to calculate the odds and probability of drought persistence from one season to the next for the selected stations by identifying the spatial pattern of seasonal drought frequency and persistence. The potential of the current analysis is validated on six selected stations of the northern area of Pakistan. The outcomes related to the current analysis provide the basis for taking more considerations on early warning systems and help to make the valuable decision for water resource management and agriculture sectors in Pakistan.

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Section: Introductionmentioning

confidence: 99%

Logistic Regression Analysis for Spatial Patterns of Drought Persistence

et al. 2021

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“…The results indicated that different drought indices have specific performance for drought monitoring in different land use types in China, and this research provided an effective way to explore the potential of different drought indices for monitoring droughts. Orimoloye et al [62] utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) EVI products to calculate the Vegetation Condition Monitoring Index (VCI), which was proven to be suited for monitoring drought disasters.…”

Section: Evaluation Of Nir V For Drought Monitoringmentioning

confidence: 99%

Assessing the Potential of Downscaled Far Red Solar-Induced Chlorophyll Fluorescence from the Canopy to Leaf Level for Drought Monitoring in Winter Wheat

Lin

Shen

et al. 2022

Remote Sensing

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Solar-induced chlorophyll fluorescence (SIF) from ground, airborne, and satellite-based observations has been increasingly used in drought monitoring recently due to its close relationship with photosynthesis. SIF emissions respond rapidly to droughts, relative to the widely used vegetation indices (VIs), thus indicating their potential for early drought monitoring. The response of SIF to droughts can be attributed to the confounding effects of both the physiology and canopy structure. In order to reduce the reabsorption and scattering effects, the total emitted SIF (SIFtot) was proposed and served as a better tool to estimate GPP compared with the top-of-canopy SIF (SIFtoc). However, the response time and response magnitude of SIFtot to droughts and its relationships with the environmental parameters and soil moisture (SM) (i.e., the knowledge of drought monitoring using SIFtot) remains unclear. Here, the continuous ground data of F760toc (SIFtoc at 760 nm) from a nadir view that was downscaled to F760tot (SIFtot at 760 nm), NIRv, and the NDVI, SM, meteorological, and crop growth parameters were measured from four winter wheat plots with different intensities of drought (well-watered, moderate drought, severe drought, and extreme drought) over 2 months. The results indicated that F760tot was more closely correlated with the SM than the VIs at short time lags but weaker at longer time lags. The daily mean values of F760tot and NIRv were able to distinguish the differences between different drought levels, and F760tot responded quickly to the onset of drought, especially for the moderate drought intensity. These findings demonstrated that F760tot has potential for early drought monitoring and may contribute to mitigating the risk of agricultural drought.

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“…The global fine-resolution SPI maps will be accomplished in the future. We will effectively evaluate how the environmental effects of drought events evolved [45]. Furthermore, the multiple drought indices or composite drought index will be applied in the future study [46,47].…”

Section: Strength Of Time-varying Spatial Downscaling and Future Workmentioning

confidence: 99%

Time Varying Spatial Downscaling of Satellite-Based Drought Index

et al. 2021

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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.

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Drought disaster monitoring using MODIS derived index for drought years: A space-based information for ecosystems and environmental conservation

Cited by 46 publications

References 47 publications

Logistic Regression Analysis for Spatial Patterns of Drought Persistence

Logistic Regression Analysis for Spatial Patterns of Drought Persistence

Assessing the Potential of Downscaled Far Red Solar-Induced Chlorophyll Fluorescence from the Canopy to Leaf Level for Drought Monitoring in Winter Wheat

Time Varying Spatial Downscaling of Satellite-Based Drought Index

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