Water is an essential natural resource, but increasingly water also forms a threat to the human population, with floods being the most common natural disaster worldwide. Earth Observation has the potential for developing cost-effective methods to monitor risk, with free and open data available at the global scale. In this study, we present the application of remote sensing observations to map flooded areas, using the Vembanad-Kol-Wetland system in the southwest of India as a case study. In August 2018, this region experienced an extremely heavy monsoon season, which caused once-in-a-century floods that led to nearly 500 deaths and the displacement of over a million people. We review the use of existing algorithms to map flooded areas in the Lake Vembanad region using the spectral reflectances of the green, red and near-infrared bands from the MSI sensor on board Sentinel-2. Although the MSI sensor has no cloud-penetrating capability, we show that the Modified Normalised Difference Water Index and the Automated Water Extraction Index can be used to generate flood maps from multi-spectral visible remote sensing observations to complement commonly used SAR-based techniques to enhance temporal coverage (from 12 to 5 days). We also show that local knowledge of paddy cultivation practices can be used to map the manoeuvring of water levels and exclude inundated paddy fields to improve the accuracy of flood maps in the study region. The flood mapping addressed here has the potential to become part of a solution package based on multi-spectral visible remote sensing with capabilities to simultaneously monitor water quality and risk of human pathogens in the environment, providing additional important services during natural disasters.

Safeguarding food security requires sustained air and crop monitoring, crop-specific ozone tolerance research, and integrated strategies aligning agricultural and air quality policies amid climate change and rising air pollution.

Surface ozone (SurfO3) pollution poses significant challenges to air quality, public health and agriculture worldwide. In a scenario of rising anthropogenic emissions, increasing temperature and altered atmospheric compositions exacerbate SurfO3 production in hotspots of pollution, leading to an ozone-climate penalty that could offset gains from emission reduction measures. This study explores projected changes in SurfO3 under the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) simulations of the Coupled Model Intercomparison Project phase-six (CMIP6), focusing on their implications for agriculture in India. We analyse the simulations from the United Kingdom Earth System Model version1 at low (-LL) resolution (UKESM 1-0-LL) and Geophysical Fluid Dynamics Laboratory Earth System Model version 4 (GFDL-ESM 4) models, following the anthropogenic emission trajectory of the shared socio-economic pathway (SSP)3–7.0 high emission scenario with pre-defined climate (SSP370pdSST) and changing climate (SSP370SST). Climate-induced increases in SurfO3 is observed in the rabi (December–February) and late-kharif (October–November) seasons, whereas early to mid-kharif (June–September) season show a reduction in SurfO3 in India. Increasing trend in climate change-induced SurfO3(0.03–0.08 ppbv yr-1) is evident in the Indo-Gangetic Plain (IGP), the ‛breadbasket of India’, during most seasons, and in central India during post-kharif and rabi seasons. This suggests that, even if the precursor emission remains at the current level, climate change alone could contribute to the increase in SurfO3 in the highly polluted regions of south Asia by 2050. Furthermore, the IGP region, one of the most fertile agricultural regions in South Asia, is likely to face significant challenges due to increasing SurfO3, which exacerbates crop yield losses, particularly for rabi wheat. This situation is concerning, as IGP comprises of about 27% of the total cultivated area and contributes significantly (about 50% of the total food consumed in the country) to the national agricultural production in India. Therefore, the study emphasise the need for targeted mitigation strategies, particularly for CH4, VOCs and NOx, to mitigate the dual challenges of climate change and SurfO3 pollution.Keywords: Surface ozone, Climate change, AerChemMIP, Ozone-climate penalty, Agriculture, IGP

Tropospheric nitrogen dioxide (NO2) is an indicator of anthropogenic activity and a key precursor of the phytotoxic surface ozone (O3), with potential implications for agricultural ecosystems. Recent satellite-based studies have reported negative association between NO2 and vegetation greenness over agricultural regions, but the persistence of these relationships over longer timescales and their relevance for crop photosynthetic functioning remain unclear. Here, we assess co-variability between NO2 and photosynthetic activity over Indian croplands (rice and wheat dominated regions) over a decadal scale (2007–2022) using High Spatial-Temporal Coverage Merged tropospheric NO2 (HSTCM-NO2) dataset and Solar Induced Fluorescence (SIF) from the Global Solar-induced Chlorophyll Fluorescence (GOSIF) product. Analyses conducted separately for the kharif (July–September) and rabi (January–March) seasons to account for contrasting agro-climatic and photochemical conditions. We find widespread increases in SIF for rabi season croplands (0.0032 W/m²/μm/sr/yr) including wheat-dominated regions, despite spatially heterogeneous NO2 trends (0.0096×1015 molec./cm2/yr) that increase across much of the Indo-Gangetic Plain (IGP). However, analysis of detrended interannual variability reveals a significant negative association between NO2 and SIF during the rabi season (Pearson correlation, r=-0.5, p=0.048), indicating reduced photosynthetic activity in years with elevated pollution. A similar but weaker relationship is observed for kharif season croplands in rice-dominated regions (r=-0.1, p=0.704). The results indicate that variations in NO2 pollution may modulate interannual crop performance by influencing photosynthetic activity, even in systems where long-term productivity trends are primarily driven by management and technology.Keywords: Tropospheric NO2; Pollution; Croplands; Photosynthetic activity; SIF; India