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In South Asia, annual land use and land cover (LULC) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vitally essential to obtain correct information on the LULC 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 Moderate Resolution Imaging Spectroradiometer (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 LULC map of South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using the annual map of the LULC time series, and the space–time dynamics of the LULC 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 show the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrublands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions. [ABSTRACT FROM AUTHOR]

Changes in large-scale circulation, especially El Niño–Southern Oscillation (ENSO), have significant impacts on dust activities over the dust source and downwind regions. However, these impacts present an interdecadal pattern, and it remains less clear which factors lead to the interdecadal variability of the ENSO impact on dust activities over northwestern South Asia, although previous studies have discussed the response of interannual dust activities over northwestern South Asia to the ENSO circle. Based on the linear regression model and MERRA-2 atmospheric aerosol reanalysis data, this study investigated the interdecadal variability of the ENSO impact on dust activities as well as the associated possible atmospheric drivers under two different warming phases over northwestern South Asia. Results indicated that the relationship between ENSO and dust column mass density (DUCMASS) experienced an obvious shift from the accelerated global warming period (1982–1996) to the warming hiatus period (2000–2014). The change in Atlantic and Indian Ocean sea surface temperature anomaly (SSTA) patterns weakened the impact of ENSO on dust activities over northwestern South Asia during 1982–1996, while the change in Pacific Decadal Oscillation (PDO) strengthened ENSO's effect when it was in phase with ENSO. Both the Atlantic and Indian Ocean SSTA patterns were modulated by the duration of ENSO events (i.e., continuing and emerging ENSO). This study provides new insights into numerical simulation involving the influence of atmospheric teleconnections on the variability of dust activities and their influence mechanisms. [ABSTRACT FROM AUTHOR]

In South Asia, key differences in annual land use and land cover (LULC) take place due to climate change, global warming, human activity, biodiversity, and hydrology. So, it is very important to get accurate land cover information for this region. An annual LULC map that covers a comprehensive period is a major dataset for climatologically study. While yearly worldwide maps of LULC are produced from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset, in 2001, the first LULC map of MODIS is generated which restrictions the perspective climatologically analysis. This research work generated a time series of yearly LULC maps of South Asia from 2001 to 2015 by using random forest classification from AVHRR GIMMS NDVI3g data. The MODIS land cover product such as (MCD12Q1) was used as a reference data for the trained classifier. The result was validated by using time series of annual LULC maps, and the spatiotemporal dynamic of LULC maps was illustrated in the last 15 years from 2001 to 2015. The simplified sixteen class versions of our 15-year overall accuracy of a land cover map are 86.70%, and 1.23% higher than that of MODIS maps. The change detection indicated that, for the last 15 years, the class of closed shrublands, savannas, croplands, urban and built-up land, barren, and cropland per natural vegetation mosaics increase notably during the 2001 to 2015, and in contrast, the class of woody savannas, evergreen needleleaf forests, open shrublands, grasslands, mixed forests, permanent wetlands, permanent snow and ice, evergreen broadleaf forests, and water bodies decrease notably during 2001 to 2015. These yearly land cover maps will be an essential dataset for the upcoming climate study, where time series of LULC maps accessibility is restricted. [ABSTRACT FROM AUTHOR]

Drought is a severe climate fact that mainly results from low rainfall leading to serious threat of water shortages an ecological system of South Asia. Due to the current drought conditions and vegetation dynamics, the situation could further be intensified over South Asia. Thus, we study the drought impacts on vegetation dynamics over South Asia, aimed to find out the spatiotemporal differences in vegetation dynamics and seasons at which vegetation is determined by drought. Our approach is based on the using of advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) data and (NASA's MERRA) air temperature and rainfall data (1990–2015). Due to the low vegetation and dryness in South Asia, the NDVI is more helpful in describing the drought condition. From April to October, there were fast improvements in NDVI, VHI, and VCI. During September, the monthly VHI and VCI were stabilized and enhanced in October once more and in December again indicated a declining trend. The PCI, TCI, VCI, and VHI monthly values confirmed that in 2001, an extreme drought year, and continuous up to 2003, which lead the maximum drought in the South Asia regions. A considerably significantly correlation value in summer (JJA) and autumn (SON) seasons are showed between precipitation and NDVI. While the relationship between NVSWI and NDVI presented considerably high relationship in DJF, JJA, and SON, which specify an excellent indication for monitoring water stress. From 1990 to 2015, the difference of vegetation trend was obvious showed among various regions. The drought frequency was reducing trends from 1990 to 2015 over South Asia. [ABSTRACT FROM AUTHOR]

South Asia is susceptible to drought due to high variation in monthly precipitation. The drought indices deriving from remote sensing data have been used to monitor drought events. To secure agricultural land in South Asia, timely and effective drought monitoring is very important. In this study, TRMM data was utilized along with remote sensing techniques for reliable drought monitoring. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), NDVI, and Normalized Vegetation Supply Water Index (NVSWI) are more helpful in describing the drought events in South Asia due to the dryness and low vegetation. To categorize drought-affected areas, the spatial maps of TRMM were used to confirm MODIS-derived TVDI, DSI, and NVSWI. The DSI, TVDI, NVSWI, and Normalized Monthly Precipitation Anomaly Percentage (NAP) indices with an integrated use of MODIS-derived ET/PET and NDVI were selected as a tool for monitoring drought in South Asia. The seasonal DSI, TVDI, NVSWI, NAP, and NDVI values confirmed that South Asia suffered an extreme drought in 2001, which continued up to 2003. The correlation was generated among DSI, NAP, NVWSI, NDVI, TVDI, and TCI on a seasonal basis. The significantly positive correlation values of DSI, TVDI, and NVSWI were in DJF, MAM, and SON seasons, which were described as good drought monitoring indices during these seasons. During summer, the distribution values of drought indicated that more droughts occurred in the southwest regions as compared to the northeast region of South Asia. From 2001 to 2017, the change trend of drought was characterized; the difference of drought trend was obviously indicated among different regions. In South Asia, generally, the frequency of drought showed declining trends from 2001 to 2017. It was verified that these drought indices are a comprehensive drought monitoring indicator and would reduce drought risk in South Asia. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]