Agroforestry practices that include the integration of multifunctional trees within agricultural lands can generate multiple socioecological benefits, in addition to being a natural climate solution due to the associated carbon sequestration potential. Such agroforestry trees represent a vital part of India’s landscapes. However, despite their importance, a current lack of robust monitoring mechanisms has contributed to an insufficient grasp of their distribution in relation to management practices, as well as their vulnerability to climate change and diseases. Here we map 0.6 billion farmland trees, excluding block plantations, in India and track them over the past decade. We show that around 11 ± 2% of the large trees (about 96 m² crown size) mapped in 2010/2011 had disappeared by 2018. Moreover, during the period 2018–2022, more than 5 million large farmland trees (about 67 m² crown size) have vanished, due partly to altered cultivation practices, where trees within fields are perceived as detrimental to crop yields. These observations are particularly unsettling given the current emphasis on agroforestry as a pivotal natural climate solution, playing a crucial role in both climate change adaptation and mitigation strategies, in addition to being important for supporting agricultural livelihoods and improving biodiversity.

Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (< 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting.

Soil quality is fundamental to nutrient-rich food production and the sustainability of terrestrial ecosystems. However, inappropriate agricultural practices often lead to persistent soil exposure to air and sunlight, which increases soil organic matter losses and erosion risks, particularly under climate extremes. Here, we provide a satellite-based mapping of daily soil exposure occurrence across global croplands from 2001 to 2022 and evaluate the associated degradation risks caused by extreme climate events. We find that while 57% of global croplands experienced a reduction in soil exposure duration in the past two decades, 86% are increasingly subjected to climate extremes. The areas exposed to increasing climate extremes tend to have higher soil organic carbon levels, indicating an intensified degradation risk of global nutrient-rich cropland soils. Our study offers spatio-temporally explicit insights into global cropland soil exposure and its vulnerability to climate extremes, providing evidence to support improvements in sustainable agriculture practices.