Soil Drought and Vegetation Response during 2001–2015 in North China Based on GLDAS and MODIS Data

Yang, Siyao; Meng, Dan; Gong, Huili; Li, Xiaojuan; Wu, Xinling

doi:10.1155/2018/1818727

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…MA (max)j −MMA j × 100 , ifMA j > MMA j MA (max)j , MA (min)j , and MMA j are long term maximum, minimum, and median soil moisture at month j) [26,27] Remote Sens. 2021, 13, 1715 7 of 28…”

Section: Model Input Parametermentioning

confidence: 99%

“…For agricultural drought, considering the seasonal variability and its cumulative impact on vegetation, we selected the SMDI drought index. The SMDI has the ability to reflect the short-term dry conditions and it is also spatially comparable irrespective of weather and climatic zone [26,27]. A deep forwarded neural network (DFNN) was used for the deep learning method and the SMDI drought index was considered as a model response variable to monitor drought over South Asia.…”

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: Model Input Parametermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%