Farmland-parcel-based crop remote sensing classification method in complex mountainous areas via coupling spatial distribution patterns

doi:10.11947/j.AGCS.2025.20240440

Abstract

Abstract:

Parcel-wise crop spatial distribution maps are currently in urgent need for precision agriculture applications. However, in mountainous areas with undulating topography, fragmented farmland structure, diverse crop types, and rainy climates, existing data-driven models may not fully satisfy the precision demands. Fundamentally, the causes may lie in the cognitive limits of the complicated agricultural systems and the uncertainty in remote sensing imaging, as well as the ignorance of spatial-temporal effect within the calculation. In response, this research focuses on the uncertainty reduction of remote sensing crop mapping, conducting researches on the introduction of spatial patterns to both the object-level decomposition of the targeted planting area, the reconstruction of the parcel-wise temporal spectral signature, and the crop classification process. The spatial and temporal features get adequately collaborated with land parcels received as basic analysis units. The comprehensive experiment of typical mountainous areas in Southwest China reveals the positive effect of introducing spatial distribution patterns on uncertainty reduction. It clarifies the significance of combining measures such as precise extraction of land parcels, prior knowledge constraints, feature recombination and expansion, classifier reinforcement, etc. to enhance crop identification in mountainous areas. In general, this study deepens the theoretical exploration of the remote sensing interpretation in crop mapping under complex terrain conditions and, in practice, provides a practical framework with higher accuracy and greater interpretability for the scenarios such as agricultural insurance and disaster assessment.

Key words: farmland-parcel, crop remote sensing classification, mountain area, Tu-Pu collaboration, spatial distribution pattern

CLC Number:

P237

Tianjun WU, Manjia LI, Jiancheng LUO, Ziqi LI, Xiaodong HU, Lijing GAO, Zhanfeng SHEN. Farmland-parcel-based crop remote sensing classification method in complex mountainous areas via coupling spatial distribution patterns[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(7): 1215-1229.

Figures/Tables 12

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作物类型	播种期	苗期	分蘖期	拔节期	抽穗期	灌浆成熟期
水稻	2月下旬至	3月中旬至	4月下旬至	5月下旬至	6月下旬至	7月中旬至
水稻	3月上旬	4月中旬	5月中旬	6月中旬	7月上旬	8月中旬
玉米	2月下旬至	3月下旬至	—	5月中旬至	6月中旬至	7月上旬至
玉米	3月中旬	5月上旬	—	6月上旬	6月下旬	8月中旬

作物类别	OA	正确率	召回率	F₁值	信息熵
水稻	—	0.926	0.907	0.917	－0.634
玉米	—	0.724	0.712	0.718	－0.976
柠檬	—	0.891	0.919	0.905	－0.560
其他	—	0.795	0.816	0.805	－0.895
总体	0.883	0.890	0.885	0.887	－0.757

特征类型	OA	正确率	召回率	F₁值	信息熵
像元级	0.808	0.816	0.812	0.814	－0.885
像元级-地块约束	0.851	0.834	0.838	0.836	－0.733
地块级	0.883	0.890	0.885	0.887	－0.757

地块ID	其他隶属度	水稻隶属度	不确定性	其他隶属度	水稻隶属度	不确定性
目标地块	0.53	0.47	0.997	0.39	0.61	0.967
邻域单元1	0.25	0.75	0.808	0.19	0.81	0.701
邻域单元2	0.61	0.39	0.964	0.58	0.42	0.981
邻域单元3	0.34	0.66	0.925	0.38	0.62	0.958
邻域单元4	0.55	0.45	0.992	0.24	0.76	0.793