DESTIN: A new method for delineating the boundaries of crop fields by fusing spatial and temporal information from WorldView and Planet satellite imagery

The digital boundaries of crop fields represent a prerequisite for designing parcel-based crop management platforms, implementing online site-specific agronomic practices and monitoring crop growth per field. Previous approaches on field boundary delineation were mostly developed with medium resolution imagery (e.g., Landsat) for the regions or countries with intensive agriculture and large-sized crop fields. However, suitable delineation methods are scarce for the regions in developing countries where the majority of arable land is cultivated by smallholder farmers and distributed in small and fragmented crop fields. This study proposed a comprehensive method, delineation by fusing spatial and temporal information (DESTIN), to derive the boundaries of crop fields from sub-meter WorldView-2/3 and 3-m Planet imagery. After extraction of spatial objects from very high resolution (VHR) WorldView imagery, this method performed recognition of crop field objects using high resolution (HR) Planet-derived temporal features specifically concerning soil preparation and harvesting stages for summer crops. The performance of DESTIN in crop field boundary delineation was evaluated with the reference polygons (0.4-1.0 ha in area on average) over four subset areas in eastern China's Jiangsu province, and further compared with a benchmark objection extraction approach. The results demonstrated that the integration of WorldView and Planet imagery as demanded by DESTIN yielded accurate recognition of crop fields with the classification overall accuracy (OA) ranging from 94.98% to 98.84%, which was remarkably improved over the use of WorldView or Planet imagery alone with increases in OA from 12% to 17%. The majority of crop field boudaries were successfully delineated with both methods, but DESTIN produced cleaner polygons than the benchmark appraoch and closer matches of field boundaries to the reference. DESTIN also yielded better one-to-one matches between delineations and reference (77% as opposed to 54%) and fewer one-to-many matches (1% as opposed to 33%) as a reflection of being less prone to over segmentation. The DESTIN method does not need subjective parameterization for image segmentation, and could be applicable to the areas with availability of bi-temporal VHR imagery over the soil preparation and harvesting stages and HR imagery over the peak growth stage of summer crops. It has great potential for delineating the crop field boundaries in smallholder farming systems with VHR imagery acquired from satellite, airborne or unmanned aerial vehicle platforms.