A precise spatiotemporal fusion crop classification framework based on parcels.

The precise extraction of crop type information on agricultural land supports applications such as agricultural information statistics and planning. It is also a crucial foundation for improving agricultural production efficiency and promoting agricultural informatization. In smallholder agricultural regions, such as the southern agricultural areas of China, a significant number of small parcels exist. These small parcels often exhibit deficiencies and discrepancies in feature representation for time series classification of crop types, leading to considerable classification challenges. To achieve more precise crop type differentiation in smallholder agricultural systems, this study designs a parcel-based classification framework, PITT (Parcel-level Integration of Time series and Texture). The PITT framework categorizes small parcels in smallholder systems by area into small parcels and micro parcels, which are then separately used as inputs for time series classification methods and high-resolution texture classification methods. During the process, the time series classification results guide the high-resolution texture classification method. Finally, the results from the texture classification are fused with the time series classification results, achieving more accurate crop classification outcomes. The study focuses on the Jiang area of Zongyang County, Tongling city, Anhui Province. Experimental validations using Pearson correlation coefficients and TWDTW similarity comparisons reveal that larger parcels have time series features that more strongly represent the features of typical samples. Additionally, when the PITT framework was compared with other time series classification models using real labels, the F1 scores for small parcels of approximately 0.1-0.5 hectares increased for rapeseed and wheat, reaching 0.93 and 0.94, respectively. For micro parcels (less than 0.1 ha), the F1 scores improved by at least 4.11% and 17.05%, respectively. This demonstrates the ability to achieve high crop classification performance with minimal labelling in smallholder systems, advancing the informatization of smallholder agriculture.