Knowledge about the spatial-temporal pattern of cropland abandonment is the premise for the management of abandoned croplands. Traditional mapping approaches of abandoned croplands usually utilize a multi-date classification-based land cover change trajectory. It requires quality training samples for land cover classification at each epoch, which is challenging in regions of smallholder agriculture in the absence of high-resolution imagery. Facing these challenges, a theoretical model is proposed to recognize abandoned croplands based on post-abandonment secondary succession. It applies the continuous change detection and classification (CCDC) temporal segmentation algorithm to Landsat time series (1986~2021) to obtain disjoint segments, representing croplands’ status. The post-abandonment secondary succession showing a greening trend is recognized using NDVI-based harmonic analysis, so as to capture its preceding abandonment. This algorithm is applied to a mountainous area in southwest China, where cropland abandonments are widespread. Validation based on stratified random samples referenced by a vegetation index time series and satellite images shows that the detected abandoned croplands have user accuracy, producer accuracy and an F1 score ranging from 43% to 71%, with variation among abandonment year. The study area has a potential cropland extent of 22,294 km², within which 9252 km² of the cropland was abandoned. The three peak years of abandonment were 1994, 2000, and 2011. The algorithm is suitable to be applied to large-scale mapping due to its automatic manner.

Accurate and timely information on farmland size is crucial for agricultural development, resource management, and other related fields. However, there is currently no mature method for estimating farmland size in smallholder farming areas. This is due to the small size of farmland plots in these areas, which have unclear boundaries in medium and high-resolution satellite imagery, and irregular shapes that make it difficult to extract complete boundaries using morphological rules. Automatic farmland mapping algorithms using remote sensing data also perform poorly in small-scale farming areas. To address this issue, this study proposes a farmland size evaluation index based on edge frequency (ECR). The algorithm utilizes the high temporal resolution of Sentinel-2 satellite imagery to compensate for its spatial resolution limitations. First, all Sentinel-2 images from one year are used to calculate edge frequencies, which can divide farmland areas into low-value farmland interior regions, medium-value non-permanent edges, and high-value permanent edges (PE). Next, the Otsu’s thresholding algorithm is iteratively applied twice to the edge frequencies to first extract edges and then permanent edges. The ratio of PE to cropland (ECR) is then calculated. Using the North China Plain and Northeast China Plain as study areas, and comparing with existing farmland size datasets, the appropriate estimation radius for ECR was determined to be 1600 m. The study found that the peak ECR value for the Northeast China Plain was 0.085, and the peak value for the North China Plain was 0.105. The overall distribution was consistent with the reference dataset.

The increasing global population is leading to a decline in cropland per person, aggravating food security challenges. The global implementation of land consolidation (LC) has created new farmland and increased productivity. LC is a critical strategy in China for ensuring food security and gaining significant government support. This article investigates the impact of LC on farmland productivity in Sichuan Province in 2020. We utilize time series remote sensing data to analyze LC’s impact on farmland capacity. This study uses Sentinel and Landsat satellite data to calculate CumVI and assesses the LC project’s spatiotemporal evolution. To evaluate LC’s effectiveness, we create indexes for yield level and stability and employ Getis-Ord Gi* to identify spatial differentiation in LC’s impact. GeoDetector and GWR examine the impact of natural factors like elevation, slope, soil organic carbon, and rainfall on the effectiveness of LC. The research results show that: (1) After the implementation of LC, 55.51% of the project areas experienced significant improvements in agricultural productivity; the average increase rate of yield level is 7.74%; and the average increase rate of yield stability is 12.40%. Overall, LC is significant for improving farmland capacity. (2) The effectiveness of LC exhibits spatial differences and correlations in different areas. The main location for high-value agglomeration of yield levels is Nanchong City, while the northern part of Guangyuan City primarily hosts low-value agglomeration areas. (3) Natural conditions influence LC’s effectiveness. In terms of affecting the yield level of LC, the driving factors from high to low are SOC, elevation, slope, and rainfall. In terms of affecting the yield stability of LC, the driving factors, from high to low, are elevation, SOC, slope, and rainfall. LC’s effectiveness is influenced by different natural conditions that have different effects.