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

Abstract

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.

Flash Drought: Review of Concept, Prediction and the Potential for Machine Learning, Deep Learning Methods

“…Besides SESR, several operational tools such as USDM (Ford et al., 2015; Ford & Labosier, 2017; Otkin et al., 2013, 2016, 2018; Pendergrass et al., 2020), ESI (Hoffmann et al., 2021; Mo & Lettenmaier, 2016), EDDI (Hoffmann et al., 2021; Pendergrass et al., 2020), Quick Drought Response Index (QuickDRI; Chen et al., 2019), Flash Drought Stress Index (FDSI; Sehgal et al., 2021), National Water Model (NWM; Wang et al., 2020), and Vegetation Health Indices (VHI; Prodhan et al., 2021; Sehgal et al., 2021) have been applied to monitor FD's Spatio‐temporal extent. Additionally, NIDIS (2022), has reported several FD monitoring tools that include‐ High Plains Regional Climate Center (HPRCC) ACIS Maps and Flash Drought Assessment Using SMAP Hydrology (FLASH).…”

“…On the other hand, DL refers to sophisticated ML algorithms with multiple hierarchical layers to fit complex functions. Building on the success of these methods in different fields of earth sciences (Liakos et al., 2018; Huntingford et al., 2019; Reichstein et al., 2019; Yuan et al., 2020), recent drought studies have applied ML and DL methods in a stand‐alone manner and in combination with physical‐based models to achieve better prediction, physical consistency, reduced uncertainty, and reduced computational demands (Dikshit et al., 2021; Park et al., 2016; Prodhan et al., 2021; Rhee & Im, 2017). Notably only a limited discussion about the role of ML and DL methods in FD prediction is available in the research literature thus far.…”

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

“…Yang et al tried to use one-year hyperspectral imagery to train a CNN classification model to estimate corn grain yield [ 39 ]. Prodhan et al monitored drought over South Asia using a deep learning approach with 16 years of remote sensing data [ 40 ]. It can be seen that a large volume of image data, as well as ground truth data in years, were commonly needed to provide a sufficient dataset to train a deep learning network.…”

Collecting and Disseminating Ground Truth Data for Generation of Agriculture Statistics

“…Output from both approaches is a single-layer thematic image with discrete classes representing features from the input raster data. Modern machine learning-based approaches, such as random forest [17][18][19], and deep neural network structures (convolutional neural network [CNN], deep neural network [DNN], recurrent neural network [RNN]) [20][21][22], are widely used for image classification. Besides, the satellite imagery's spatial and temporal resolution should always be considered when performing image classification for agriculture mapping.…”