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.

Using 14 years (2007–2020) of data from passive (MODIS/Aqua) and active (CALIOP/CALIPSO) satellite measurements over China, we investigate (1) the temporal and spatial variation of aerosol properties over the Beijing–Tianjin–Hebei (BTH) region, the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) and (2) the vertical distribution of aerosol types and extinction coefficients for different aerosol optical depth (AOD) and meteorological conditions. The results show the different spatial patterns and seasonal variations of the AOD over the three regions. Annual time series reveal the occurrence of AOD maxima in 2011 over the YRD and in 2012 over the BTH and PRD; thereafter the AOD decreases steadily. Using the CALIOP vertical feature mask, the relative frequency of occurrence (rFO) of each aerosol type in the atmospheric column is analyzed: rFOs of dust and polluted dust decrease from north to south; rFOs of clean ocean, polluted continental, clean continental and elevated smoke aerosol increase from north to south. In the vertical, the peak frequency of occurrence (FO) for each aerosol type depends on region and season and varies with AOD and meteorological conditions. In general, three distinct altitude ranges are observed with the peak FO at the surface (clean continental and clean marine aerosol), at ∼1 km (polluted dust and polluted continental aerosol) and at ∼3 km (elevated smoke aerosol), whereas dust aerosol may occur over the whole altitude range considered in this study (from the surface up to 8 km). The designation of the aerosol type in different height ranges may to some extent reflect the CALIOP aerosol type classification approach. Air mass trajectories indicate the different source regions for the three study areas and for the three different altitude ranges over each area. In this study nighttime CALIOP profiles are used. The comparison with daytime profiles shows substantial differences in the FO profiles with altitude, which suggest effects of boundary layer dynamics and aerosol transport on the vertical distribution of aerosol types, although differences due to day–night CALIOP performance cannot be ruled out.

Heat-health risk is a growing concern in many regions of China due to the more frequent occurrence of extremely hot weather. Spatial indexes based on various heat assessment frameworks can be used for the assessment of heat risks. In this study, we adopted two approaches—Crichton’s risk triangle and heat vulnerability index (HVI) to identify heat-health risks in the Northern Jiangxi Province of China, by using remote sensing and socio-economic data. The Geographical Information System (GIS) overlay and principal component analysis (PCA) were separately used in two frameworks to integrate parameters. The results show that the most densely populated community in the suburbs, instead of city centers, are exposed to the highest heat risk. A comparison of two heat assessment mapping indicates that the distribution of HVI highlights the vulnerability differences between census tracts. In contrast, the heat risk index of Crichton’s risk triangle has a prominent representation for regions with high risks. The stepwise multiple linear regression zero-order correlation coefficient between HVI and outdoor workers is 0.715, highlighting the vulnerability of this particular group. Spearman’s rho nonparametric correlation and the mean test reveals that heat risk index is strongly correlated with HVI in most of the main urban regions in the study area, with a significantly lower value than the latter. The analysis of variance shows that the distribution of HVI exhibits greater variety across urban regions than that of heat risk index. Our research provides new insight into heat risk assessment for further study of heat health risk in developing countries.

The Himalayan region, a major source of fresh water, is recognized as a water tower of the world. Many perennial rivers originate from Nepal Himalaya, located in the central part of the Himalayan region. Snowmelt water is essential freshwater for living, whereas it poses flood disaster potential, which is a major challenge for sustainable development. Climate change also largely affects snowmelt hydrology. Therefore, river discharge measurement requires crucial attention in the face of climate change, particularly in the Himalayan region. The snowmelt runoff model (SRM) is a frequently used method to measure river discharge in snow-fed mountain river basins. This study attempts to investigate snowmelt contribution in the overall discharge of the Budhi Gandaki River Basin (BGRB) using satellite remote sensing data products through the application of the SRM model. The model outputs were validated based on station measured river discharge data. The results show that SRM performed well in the study basin with a coefficient of determination (R²) >0.880. Moreover, this study found that the moderate resolution imaging spectroradiometer (MODIS) snow cover data and European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological datasets are highly applicable to the SRM in the Himalayan region. The study also shows that snow days have slightly decreased in the last three years, hence snowmelt contribution in overall discharge has decreased slightly in the study area. Finally, this study concludes that MOD10A2 and ECMWF precipitation and two-meter temperature products are highly applicable to measure snowmelt and associated discharge through SRM in the BGRB. Moreover, it also helps with proper freshwater planning, efficient use of winter water flow, and mitigating and preventive measures for the flood disaster.

Aerosol–cloud interaction (ACI) is examined using 10 years of data from the MODIS/Terra (morning orbit) and MODIS/Aqua (afternoon orbit) satellites. Aerosol optical depth (AOD) and cloud properties retrieved from both sensors are used to explore in a statistical sense the morning-to-afternoon variation of cloud properties in conditions with low and high AOD, over both land and ocean. The results show that the interaction between aerosol particles and clouds is more complex and of greater uncertainty over land than over ocean. The variation in d(Cloud_X), defined as the mean change in cloud property Cloud_X between the morning and afternoon overpasses in high-AOD conditions minus that in low-AOD conditions, is different over land and ocean. This applies to cloud droplet effective radius (CDR), cloud fraction (CF) and cloud top pressure (CTP), but not to cloud optical thickness (COT) and cloud liquid water path (CWP). Both COT and CWP increase over land and ocean after the time step, irrespective of the AOD. However, the initial AOD conditions can affect the amplitude of variation of COT and CWP. The effects of initial cloud fraction and meteorological conditions on the change in CF under low- and high-AOD conditions after the 3 h time step over land are also explored. Two cases are considered: (1) when the cloud cover increases and (2) when the cloud cover decreases. For both cases, we find that almost all values of d(CF) are positive, indicating that the variations of CF are larger in high AOD than that in low AOD after the 3 h time step. The results also show that a large increase in cloud fraction occurs when scenes experience large AOD and stronger upward motion of air parcels. Furthermore, the increase rate of cloud cover is larger for high AOD with increasing relative humidity (RH) when RH is larger than 20 %. We also find that a smaller increase in cloud fraction occurs when scenes experience larger AOD and larger initial cloud cover. Overall, the analysis of the diurnal variation of cloud properties provides a better understanding of aerosol–cloud interaction over land and ocean.