Characterization of drought monitoring events through MODIS- and TRMM-based DSI and TVDI over South Asia during 2001–2017

Ali, Shahzad; Tong, D.; Xu, Zhen Tian; Henchiri, Malak; Kalisa, Wilson; Shi, Siqi; Zhang, Jiahua

doi:10.1007/s11356-019-06500-4

Cited by 38 publications

(32 citation statements)

References 28 publications

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“…India in the south and Sri Lanka in the southwest have an equatorial climate. Bangladesh's climate is largely characterized by a cool winter and hot, humid summer, while Afghanistan is dominated by dry areas [14]. Seasonal rainfall is primarily driven by monsoon wind, which accounts for 80% of the annual precipitation in most parts of South Asia.…”

Section: Study Areamentioning

confidence: 99%

“…Drought severely impacts Bangladeshi agriculture, reducing production by 40% on average and affecting 53% of the population. Sri Lanka has also experienced drought impact, and more than 0.2 million people in the country have suffered severely [13,14]. Therefore, drought monitoring with an accurate and near-real-time identification system has significant practical consequences for agricultural water management.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…The South Asia regions have facing water-related challenges, such as water shortage, low water use e ciency, and degradation of water quality. Furthermore, the South Asia region soon challenged a transition from pastoral-land to farm-land (Schulz et al, 2010;Ali et al, 2019a). The huge impact of land use, land use change and forestry on reducing livestock is another reason, which is related to the restoration of rain-fed farming (Olson et al, 20011).…”

Section: Introductionmentioning

confidence: 99%

“…Changes in land cover and land use (LCLU) have always remained one of the main concerns in the development of sustainable agricultural management strategies that directly affect the hydrology, biodiversity and local climate (Linke et al, 2009). The LCLU information and its changes over time are very important for assessing the in uence on the humans, atmosphere, and world systems (Meyer and Turner, 1992;Di Gregorio, 2005;Ali et al, 2019a). The South Asia is the most affected by land cover and land use change detection (IUCN, 2010;Ali et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

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Monitoring land use changing detection maps with MODIS and AVHRR data from 2001 to 2015 in South Asia regions

Ali

Henchiri

et al. 2021

Preprint

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In South Asia, annual land cover and land use (LCLU) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vital essential to obtain correct information on the LCLU in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the MODIS dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LCLU map of the South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using of the annual map of LCLU time series, and the space-time dynamics of the LCLU map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years shows the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrub-lands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

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Contribution of precipitation and temperature to multiscale drought variations over Asia: Dependence on the time scale

Zhang

Jia

et al. 2022

Intl Journal of Climatology

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Drought variations are determined by precipitation and temperature changes. The present study examines the relative contributions of precipitation and temperature to temporal variations of short-term, medium-term and long-term droughts over Asia. This is accomplished by comparing (a) the variations of Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) that extend over 3, 9 and 24 months (i.e., shortterm, medium-term and long-term droughts, respectively) and (b) the strength

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Characterization of drought monitoring events through MODIS- and TRMM-based DSI and TVDI over South Asia during 2001–2017

Cited by 38 publications

References 28 publications

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

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

Monitoring land use changing detection maps with MODIS and AVHRR data from 2001 to 2015 in South Asia regions

Contribution of precipitation and temperature to multiscale drought variations over Asia: Dependence on the time scale

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