Aerosols are one of the key components of climate systems. They absorb and scatter both solar and terrestrial radiation and produce strong surface as well as atmospheric radiative forcing effects. Aerosol climatology includes the measurement of light extinction by aerosol scattering and absorption, by procedures such as aerosol optical depth (AOD), angstrom exponent (α), single-scattering albedo (ω), and size distribution. This article analyses the dynamics of seasonal AOD over the Indian subcontinent from 2001 to 2009 using the MODIS level 2 data set. The analysis carried out for winter, pre-monsoon, monsoon, and post-monsoon seasons is based on 8 days’ composite AOD data for selected months representative of each season. The spatial variability of AOD has been shown to be 0.47 μm and 0.66 μm for fine- and coarse-mode aerosols, respectively, which illustrates the principle of relative difference. The dynamics of seasonally averaged AOD over the period under study represent an increasing tendency from 0.20 to 0.37 at 0.47 μm and from 0.16 to 0.26 at 0.66 μm during winter (2003–2009), whereas AOD in the pre-monsoon season ranged from 0.24 to 0.16 at 0.47 μm and from 0.24 to 0.16 at 0.66 μm (2005–2009). The monsoon season yielded an AOD of less than 0.15 throughout the study period, and the post-monsoon season recorded an increasing tendency from 0.18 to 0.29 at 0.47 μm and from 0.16 to 0.19 at 0.66 μm (2005–2009), reflecting a similar trend to that of the winter AOD curve. The spatial distribution of AOD shows that the northern part of India – especially the Indo-Gangetic plain – remains most affected by high AOD throughout the year. Such high AOD can be attributed to increasing anthropogenic emission due to an ever-increasing population, and urban, industrial, and other economic activities causing high concentrations of fine-mode organic and inorganic aerosol particles, along with coarse soil and mineral dust over the Indo-Gangetic plain.

The frequent occurrence of a forest fire can be detrimental to the functioning of ecologically fragile regions such as the Western Himalaya. This study assesses the spatio-temporal variability and trends of fire occurrence in forests, shrubland, and grassland in relation to anthropogenic activities and climate oscillations over Uttarakhand and Himachal Pradesh; the most fire-affected states in the Western Himalaya. The Pre-monsoon (March to June) temporal distribution of fire occurrence is bimodal with the first and second fire peaks occurring in the last week of April and late May/early June, respectively. The length of fire season was found to have reduced by ~ 10 days in 2010s compared to 2000s with the increasing burned area concentration over a shorter period (~110 days). The Mann-Kendall Tau (τ) and Sen’s-slope suggested an increasing trend (τ = 0.3, Sen’s slope = 46.78 km² year⁻¹, p = 0.02) of burn area over Uttarakhand where most fire activity occurs. The Getis-Ords (Gi*) statistic was derived by using Fire Radiative Power (FRP) data to infer the spatial distribution of hot and cold clusters which were found to be greatest (Gi* = 1–10) in evergreen needleleaf forest which also accounted for a marginally higher mean per-pixel FRP than other land cover types. Analyses of the role of climatic variables on fire activity revealed moderate positive and negative correspondence between the burned area and average maximum-temperature (R = 0.57, p = 0.01) and total precipitation (R = – 0.53, p = 0.02), respectively. Assessment of the variation of fire activity with global climatic oscillation indices found the Niño-3.4 index to have the strongest, albeit modest, relationship (R = 0.51) with burned area anomalies. This study highlights the increase in fire intensity in the fragile forests of the Western Himalayan region and the requirement for further research into the role of climate and anthropogenic activities on fire occurrence in order to best preserve regional biodiversity and develop an effective forest management strategy.

Climate change poses a significant threat to agroecosystems, especially in the dry areas, characterized by abrupt precipitation pattern and frequent drought events. Ideal crops, tolerant to these events, such as cactus, can perform well under such changing climatic conditions. This study spatially maps land suitability for cactus (Opuntia ficus-indica) cultivation in India using the analytical hierarchical process (AHP). Nine essential growth factors that include the climate and edaphic components were considered for the period 2000 to 2007. About 32% of the total geographic area of the country is in the high to moderate suitable category. Remaining 46% falls under the marginally suitable and 22% under the low to very low suitable category. The suitability analysis, based on the precipitation anomaly (2008–2017), suggests a high probability of cactus growth in the western and east-central part of India. The relationship with aridity index shows a decreasing rate of suitability with the increase of aridity in the western and east-central provinces (β~−1 to −2). We conclude that integrating cactus into dryland farming systems and rangelands under changing climate can be one plausible solution to build resilient agro-ecosystems that provide food and fodder while enhancing the availability of ecosystem services.

We proposed a modification of the existing approach for mapping active paddy rice fields in monsoon-dominated areas. In the existing PPPM approach, LSWI higher than EVI at the transplantation stage enables the identification of rice fields. However, it fails to recognize the fields submerged later due to monsoon floods. In the proposed approach (IPPPM), the submerged fields, at the maximum greenness time, were excluded for better estimation. Sentinel–2A/2B time-series images were used for the year 2018 to map paddy rice over the Lower Gangetic Plain (LGP) using Google earth engine (GEE). The overall accuracy (OA) obtained from IPPPM was 85%. Further comparison with the statistical data reveals the IPPPM underestimates (slope (β₁) = 0.77) the total reported paddy rice area, though R² remains close to 0.9. The findings provide a basis for near real-time mapping of active paddy rice areas for addressing the issues of production and food security.