摘要
稻渔种养在保障粮食安全、减少环境污染、提高土壤肥力、降低CH4 排放方面具有显著的社会、经济和生态效益.准确获取稻鱼田、稻虾田等信息,对服务现代农业数字化管理及提升资源利用效率具有重要意义.以成都平原的典型稻虾田为研究对象,使用2019～2021年Sentinel-1后向散射系数构建时序数据集;分析稻虾田与常规稻田、莲藕田、传统水产养殖等地物后向散射系数的时序特征;通过随机森林分类器提取稻虾田、常规稻田、藕田等信息,结果显示:①稻虾田年内后向散射系数具有典型的时序变化特征,其系数变化范围、曲线波峰明显区别于常规稻田、藕田.②随机森林分类的总体精度、Kappa系数分别为:94.32%和0.91,表明Sentinel-1时序数据可准确识别稻虾种养,能将稻田和藕田等地物进行区分,是多云雾地区稻渔遥感监测的理想数据源.该研究结果可为多云雾地区稻渔种养的遥感识别提供参考.
Abstract
Rice-fish co-culture,as a model of modern ecological cycle agricultural,with significant social,eco-nomic,and ecological benefits on ensuring stable food production,reducing pollution,improving soil fertility,and lowering CH4 emissions.Therefore,obtaining information on distribution and area of rice-fish fields by us-ing remote sensing technology,is helpful in enhancing the level of agricultural digital management and improv-ing the efficiency of resource utilization efficiency.In this study,we selected the typical rice-crayfish model in the Chengdu Plain for remote sensing identification.First,the time-series data of Sentinel-1 VH polarization backscatter coefficients from 2019～2021 were collected and preprocessed in the Google Earth Engine,to re-duce the noise of SAR time-series data.Then the time-series characteristics of typical ground objects were ana-lyzed,including rice-crayfish fields,paddy fields,lotus root fields,orchards,traditional aquaculture,etc,and the characteristic parameters statistical of the backscatter coefficients time-series were statistically analyzed.Fi-nally,the information of rice-crayfish fields,rice fields and lotus root fields were extracted by the classification method of random forest.The results showed that the backscattering coefficients of rice-crayfish fields exhibited typical time-series variation characteristics.Specifically,the annual variation trend of backscattering coefficients began with a smooth transition at low value,then increased rapidly,and finally decreased sharply to low value,due to the state of rice-crayfish fields changed from water body to vegetation and then back to water body.More-over,the range of coefficient variation and the time of curve peak were significantly different from paddy fields and lotus root fields,respectively.The overall accuracy and Kappa coefficient based on random forest classifica-tion were 94.32%and 0.91,respectively.This suggested that time-series data of Sentinel-1 can effectively identify rice-crayfish fields in cloudy regions.The results can provide a reference for remote sensing identifica-tion of rice-crayfish fields in cloudy areas.
基金项目
国家重点研发计划子课题(2022YFD2001105)
四川省财政自主创新专项(2022ZZZCX031)
四川省科技计划重点研发计划(2023YFG0124)
四川省农科院"1+9"科技攻关任务项目(1+9KJGG008)
NETL
NSTL
万方数据