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引用本文:

DOI:

10.11834/jrs.20241349

收稿日期:

2021-05-25

修改日期:

2021-11-08

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时空协同的农业种植结构遥感精细制图方法
张冬韵1, 吴田军2,2, 骆剑承1, 董文1, 孙营伟3, 杨颖频4, 海云瑞5, 孟博文6, 刘巍6
1.中国科学院空天信息创新研究院 遥感科学国家重点实验室;2.长安大学 理学院;3.中国农业科学院农业资源与农业区划研究所;4.广州大学地理科学与遥感学院;5.宁夏农林科学院农业经济与信息技术研究所;6.中国科学院大学
摘要:

农业是国民经济的重要组成成分,精准地掌握农作物种植结构信息是精细化农业应用的基础。本文挖掘不同源遥感数据的时空特征互补优势,设计了一种时空协同的农业种植结构遥感制图方法,考虑将高空间分辨率遥感影像提取的耕地地块作为基本单元,再结合高时间分辨率遥感影像的光谱时序信息,在深度学习技术支持下实现地块尺度的作物分类识别和种植结构精准制图,进而可分析主要作物的空间分布特征。宁夏引黄灌区的试验案例结果表明:(1)本实验共获取研究区耕地地块149万个,总面积约54万公顷,总体分类精度为0.80;(2)相比于利用传统的制图单元和机器学习方法,基于RCF网络从高分影像上获取的耕地地块形态信息更准确,与实际农业耕作管理单元更匹配,基于Bi-LSTM网络进行作物分类能够将时间序列特征的上下文信息考虑在内,且能保证更高的分类识别精度;(3)玉米、水稻、小麦和蔬菜是研究区的主要作物,其中玉米是种植面积最大的优势作物,空间分布最为广泛,菜地主要集中分布在永宁县和青铜峡市,水稻集中分布在灌溉便利的区域,而小麦大规模种植面积较少,其中小麦夏季收割后种植其他作物的情况主要集中于青铜峡灌区,且复种指数呈现由南向北逐渐降低的趋势。

A Precise Crop Planting Structure Mapping Method based on Spatial-temporal Collaboration of Remote Sensing
Abstract:

Agriculture is an important component of the national economy. Obtaining the spatial distribution information of crops accurately is the basis of precision agricultural application. In this paper, we explore the complementary advantages of remote sensing data from different sources in terms of spatial and temporal characteristics, and design a remote sensing mapping method of agricultural planting structure based on spatial and temporal collaboration. The cropland-parcel extracted from high spatial resolution remote sensing images was taken as the basic unit, and the spectral time sequence information of multi-temporal remote sensing images was combined. With the support of deep learning, crop classification and identification at cropland-parcel scale and precise mapping of planting structure can be realized, and the spatial distribution characteristics of main crops can be analyzed. The experimental results in Yellow River Irrigation Area of Ningxia (YRIA-NX) show that : (1) A total of 1.49 million cropland-parcel were obtained in the study area, with a total area of about 540,000 hm2, and the overall classification accuracy was 0.80; (2) compared with the traditional mapping unit and machine learning method, the crop planting structure information obtained by the Bi-LSTM network based on the cropland-parcel scale is more consistent with the actual agricultural tillage management unit, and the classification accuracy can be guaranteed higher; (3) the maize, rice, wheat and vegetables are the main crop of the study area. Maize is the most dominant crop with the largest planting area and the most extensive spatial distribution. The vegetable fields are mainly concentrated distribution in Yongning Town and Qingtongxia Town. Rice is concentrated in areas with convenient irrigation while wheat planting area on a large scale is less. The planting of other crops after wheat harvest in summer is mainly concentrated in the Qingtongxia and the multiple cropping index decreased gradually from south to north.

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