A Review of Agricultural Film Mapping: Current Status, Challenges, and Future Directions

Agricultural film plays a vital role in enhancing land productivity. However, concerns have arisen regarding its impact on ecology and soil environment. Accurate and timely agricultural film maps are critical for supporting strategic agricultural planning and predicting environmental impacts. In this paper, we summarized the current status of agricultural film mapping, including plastic greenhouses (PGs) and plastic-mulched farmland (PMF), from the evolution of remote sensing data, sample sources, spectral-temporal-spatial features, and advantages and disadvantages of classification algorithms. The findings revealed that medium- and low-resolution images were used for large-scale PGs and PMF mapping, while high-resolution images were combined with deep learning to extract local deep information. The synergy between the spectral, temporal, and spatial features can definitely improve classification accuracy, especially through object-based classification methods. Deep learning has apparent advantages than traditional machine learning algorithms in extracting PGs details, rarely used for mapping PMF. There are some problems, i.e., the diversity of film types, the difference of coverage time, and the variation of spectral properties, which lead to the scarcity of large-scale PGs and PMF maps despite numerous efforts in agricultural film mapping. To advance the field, future directions should focus on combining multi-source data, collaborating spectral-temporal-spatial features to extract types, start-end dates, and durations of mulching, and expanding from local to national or global scales. The accurate and timely agricultural film maps are expected to support effective land management, rationalize human land use behavior, and inform policy formulation for environmental sustainability.