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Global food security is being threatened by the reduction of high‐quality cropland, extreme weather events, and the uncertainty of food supply chains. The globalization of agricultural trade has elevated the diversification of non‐grain production (NGP) on cultivated land to a prominent strategy for poverty alleviation in numerous developing nations. Its rapid expansion has engendered a multitude of deleterious consequences on both food security and ecological stability. NGP in China is becoming very common in the process of rapid urbanization, threatening national food security. To better understand the causal mechanisms and enable governments to balance food security and rural development, it is crucial to have a clear understanding of the spatiotemporal dynamics of NGP using remote sensing. Yet knowledge gaps remain concerning how to use remote sensing to track human‐dominated or ‐induced long‐term cultivated land changes. Our study proposed a method for detecting the spatiotemporal evolution of NGP based on Landsat time‐series data under the Google Earth Engine platform. This approach was proposed by (1) obtaining the union of cultivated lands from multiple landcover products to minimize the cultivated land omission, (2) constructing multi‐index dynamic trend rules for 3 representative types of NGP and obtaining results at the pixel level, while adopting the continuous change detection and classification algorithm to Landsat time series (1986–2022) to determine when the most recent change occurred, (3) minimizing the noise by object‐oriented land use–land cover classification and mode filter approaches, and (4) mapping the spatiotemporal distribution of NGP. The proposed methodology was tested in Jiashan, located in Zhejiang Province (eastern China), where NGP is widespread. We achieved a high overall accuracy of 95.67% for NGP type detection and an overall accuracy of 85.26% for change detection of time. The results indicated a continued increasing pattern of NGP in Jiashan from 1986 to 2022, with the cumulative percentage of NGP increasing from 0.02% to 20.69%. This study highlights the utilization of time‐series data to document essential NGP information for evaluating food security in China and the method is well‐suited for large‐scale mapping due to its automatic manner. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date built-up area mapping is of great importance to the science community, decision-makers, and society. Therefore, satellite-based, built-up area (BUA) extraction at medium resolution with supervised classification has been widely carried out. However, the spectral confusion between BUA and bare land (BL) is the primary hindering factor for accurate BUA mapping over large regions. Here we propose a new methodology for the efficient BUA extraction using multi-sensor data under Google Earth Engine cloud computing platform. The proposed method mainly employs intra-annual satellite imagery for water and vegetation masks, and a random-forest machine learning classifier combined with auxiliary data to discriminate between BUA and BL. First, a vegetation mask and water mask are generated using NDVI (normalized differenced vegetation index) max in vegetation growth periods and the annual water-occurrence frequency. Second, to accurately extract BUA from unmasked pixels, consisting of BUA and BL, random-forest-based classification is conducted using multi-sensor features, including temperature, night-time light, backscattering, topography, optical spectra, and NDVI time-series metrics. This approach is applied in Zhejiang Province, China, and an overall accuracy of 92.5% is obtained, which is 3.4% higher than classification with spectral data only. For large-scale BUA mapping, it is feasible to enhance the performance of BUA mapping with multi-temporal and multi-sensor data, which takes full advantage of datasets available in Google Earth Engine. [ABSTRACT FROM AUTHOR]