基于Sentinel-2遥感影像的黄土高原覆膜农田识别  

Plastic-mulched Farmland Recognition in Loess Plateau Based on Sentinel-2 Remote-sensing Images

作　　者：赵成 梁盈盈 冯浩[2,3] 王钊 于强[3,4] 何建强 ZHAO Cheng;LIANG Yingying;FENG Hao;WANG Zhao;YU Qiang;HE Jianqiang(Key Laboratory for Agricultural Soil and Water Engineering in Arid Area,Ministry of Education,Northwest A&F University,Yangling,Shaanxi 712100,China;Institute of Water-saving Agriculture in Arid Areas of China,Northwest A&F University,Yangling,Shaanxi 712100,China;State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau,Institute of Water and Soil Conservation,Chinese Academy of Science and Ministry of Water Resource,Yangling,Shaanxi 712100,China;Key Laboratory of Eco-Environment and Meteorology for the Qinling Mountains and Loess Plateau,Shaanxi Provincial Meteorological Bureau,Xi’an 710016,China)

机构地区：[1]西北农林科技大学旱区农业水土工程教育部重点实验室,陕西杨凌712100 [2]西北农林科技大学中国旱区节水农业研究院,陕西杨凌712100 [3]中国科学院水利部水土保持研究所黄土高原土壤侵蚀与旱地农业国家重点实验室,陕西杨凌712100 [4]陕西省气象局秦岭和黄土高原生态环境气象重点实验室,西安710016

出　　处：《农业机械学报》2023年第8期180-192,共13页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(52079115、41961124006);国家重点研发计划项目(2021YFD1900700);陕西省重点研发计划重点产业创新链(群)-农业领域项目(2019ZDLNY07-03);高等学校学科创新引智计划(111计划)项目(B12007)。

摘　　要：及时、准确地获取覆膜农田的空间分布信息是防治地膜微塑料污染的基础。为准确地识别黄土高原地区的覆膜农田,本研究构建了基于Sentinel-2遥感影像和随机森林算法的适用于黄土高原覆膜农田遥感识别的特征集组合与多时相组合方案。以甘肃省临夏县、宁夏回族自治区彭阳县和山西省山阴县作为测试区,陕西省旬邑县作为验证区开展识别研究。首先,基于随机森林算法,针对3个不同的作物生育期(播期、生长旺盛期和收获期),在7种不同的特征集组合方案中优选出各时期识别精度最高的方案。然后,基于不同作物生育期的遥感影像及其对应的最优特征集组合方案,构建不同的多时相组合来进行覆膜农田识别并优选多时相组合。最后,利用旬邑县来验证构建的优选特征集组合与多时相组合识别覆膜农田的有效性,并绘制各研究区的覆膜农田空间分布图。结果表明:相比于其他遥感识别特征因子,Sentinel-2遥感影像光谱特征集中的可见光波段(B2、B3和B4)和短波红外波段(B11和B12),指数特征集中的归一化差值裸地与建筑用地指数(NDBBI)、归一化水体指数(NDWI)、裸土指数(BSI)、归一化建筑物指数(NDBI)和改进的归一化水体指数(MNDWI),纹理特征集中的和平均(savg)和相关性(corr)可以作为覆膜农田识别的优选输入特征变量。在7种特征集组合方案中,光谱+指数方案是播期和收获期识别覆膜农田的优选方案,在这两个时期对4个研究区的覆膜农田进行识别的F1值分别大于87%和57%,而光谱+指数+纹理方案是生长旺盛期识别覆膜农田的优选方案,该方案识别4个研究区覆膜农田的F1值均大于71%。基于多时相遥感影像的覆膜农田识别精度高于仅基于单时相遥感影像的精度,其中播期+生长旺盛期+收获期多时相组合可作为黄土高原覆膜农田识别的优选多时相组合,该组合在4个研究区识别覆�Plastic film mulching has greatly increased crop yields in arid and semi-arid regions of China,but also caused a lot of environmental problems.Thus,timely and accurate mapping of plastic-mulched farmlands through remote sensing technology is helpful for governments to plan agricultural production and deal with micro-plastic pollutions.However,the existing recognition methods based on single-temporal remote-sensing images with low and medium resolutions are unable to accurately recognize the plastic-mulched farmlands in the Loess Plateau due to its complex terrain and fragmented agricultural landscapes.In order to accurately recognize plastic-mulched farmlands in the Loess Plateau,different feature set combination schemes and multi-temporal image combination schemes applicable to recognize plastic-mulched farmlands in the Loess Plateau were constructed based on Sentinel-2 remote-sensing images and random forest algorithm.Three testing areas were selected for constructing recognition schemes mentioned above,including Linxia County in Gansu Province,Pengyang County in Ningxia Hui Autonomous Region,and Shanyin County in Shanxi Province,and one validation area of Xunyi County in Shaanxi Province were chosen as the scheme validation area.Firstly,based on the random forest algorithm,the optimal feature set combination scheme with the highest recognition accuracy was selected from seven different feature set combination schemes for each growth stage(sowing stage,flourishing stage,and harvesting stage).Then,based on the remote-sensing images of the three different crop growth stages and their corresponding optimal feature set combination schemes,different multi-temporal image combination schemes were constructed to recognize the plastic-mulched farmlands,and then the optimal multi-temporal image combination scheme was selected.Finally,the effectiveness of the optimal feature set combination scheme and multi-temporal image combination sheme for recognizing plastic-mulched farmlands was verified in Xunyi County,and the spat

关 键 词：覆膜农田 遥感识别 多时相遥感影像 特征优选 Google Earth Engine 随机森林 

分 类 号：S127[农业科学—农业基础科学] TP79[自动化与计算机技术—检测技术与自动化装置]