<b>A</b><b> </b><b>G</b><b>lobal</b><b> </b><b>R</b><b>eview of Monitoring Cropland Abandonment</b><b> </b><b>U</b><b>sing Remote Sensing:</b><b> </b><b>Temporal</b><b>-spatial</b><b> </b><b>P</b><b>atterns, Causes, Ecological Effects, and Future Prospects</b>
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Using remote sensing methodologies to uncover the temporal-spatial patterns of cropland abandonment (CA) offers significant advantages at bothmacro scales and in real time. However, the current literature lacks a systematic review of specific typologies and methods regarding the application of remote sensing technology to CA monitoring. To address this knowledge gap, we systematically review remote sensing-based methods for monitoring CA, its causes, and ecological effects. Our results show thatthe methods for monitoring abandoned cropland can be classified into two major categories: those based on image spectral featuresand thosebased on land covertemporal trajectories and vegetation phenologydynamics. Among the eight subcategories, vegetation phenology and dynamic methods exhibit the highest average overall accuracy at 89.33±3.37%, compared to the other methods. It is crucial to assess the causes of CA through remote sensing observations, such as road density, spatial information of agricultural infrastructure, and the ecological effects resulting from abandonment, including food loss risks, carbon sequestration, wildfire risk, evapotranspiration, wilderness quality, biodiversity, and climate change. Through this systematic review, we argue that remote sensing has greater potential for monitoring CA in the future, with room for further progress in the classification of abandoned cropland types, the observation of fragmented and temporally unstable parcels, and the ecological effects in different scenarios. More importantly, we presenta trinity CA monitoring framework based on the cause-pattern-effect pillars, which offers a novel perspective for comprehensive research on CA. Overall, our work provides a systematic and insightful perspective for advancing remote sensing research on CA
