中国农业气象

• 论文 • 上一篇    

 内蒙古地区FY-3B/3C微波遥感土壤水分数据产品的融合与评估

 姜少杰,宋海清,李云鹏,潘学标,姜会飞   

  1.  1.内蒙古自治区生态与农业气象中心,呼和浩特 010051;2.中国农业大学资源与环境学院,北京 100193
  • 出版日期:2020-08-20 发布日期:2020-08-19
  • 作者简介:姜少杰,E-mail:jiang470004510@163.com
  • 基金资助:
     国家重点研发计划重大自然灾害监测预警与防范专项(2018YFC1506606);内蒙古自治区科技计划项目(201602103);国家自然科学基金项目(41775156);内蒙古自治区气象局科技创新项目(nmqxkjcx201702;nmqxkjcx201806);内蒙古自治区自然科学基金面上项目(2017MS0410;2018MS04005);内蒙古科技重大专项(2020ZD0005);内蒙古科技计划项目(2019GG016)

 Data Fusion and Evaluation of Soil Moisture Products from FY-3B/3C Microwave Remote Sensing in Inner Mongolia

 JIANG Shao-jie, SONG Hai-qing, LI Yun-peng, PAN Xue-biao, JIANG Hui-fei   

  1.  1.Ecological and Agricultural Meteorology Center of Inner Mongolia Autonomous Region, Hohhot 010051, China; 2.College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193
  • Online:2020-08-20 Published:2020-08-19
  • Supported by:
     

摘要:  土壤水分是陆-气耦合系统的重要组成部分,土壤水分监测在气候、水文、农业等领域具有重要意义。与站点资料相比,遥感数据能够较好地反应区域格点上土壤水分的变化。基于2018年作物生长季(5-10月)观测站点表层(0-10cm)土壤水分逐日观测资料,选用与观测站点资料时空一致的FY-3B升轨/降轨、FY-3C升轨/降轨、AMSR2、SMOS卫星土壤水分产品,对各遥感数据进行检验。首先利用加权平均法对FY-3B升轨/降轨、FY-3C升轨/降轨产品数据进行融合,然后利用随机森林方法融合形成FY-3B/3C数据集,对比评价AMSR2、SMOS、FY-3B/3C在内蒙古地区的适用性。结果表明:FY-3B升轨/降轨、FY-3C升轨/降轨中日间的数据质量好于夜间,通过加权平均融合后的FY-3B和FY-3C数据质量无显著改善,利用随机森林模型融合形成的FY-3B/3C数据产品质量得到显著提升。在雨季和高植被覆盖区(东北部),SMOS、AMSR2、FY-3B/3C三个数据产品中FY-3B/3C数据质量均好于SMOS和AMSR2。整体来看,SMOS在内蒙古中部和东南部地区适用性较好,AMSR2在全区适用性较差,FY-3B/3C在全区适用性最好。

关键词:  FY-3B/3C, 土壤水分, 数据融合, 遥感监测, 适用性

Abstract:  Soil moisture is one of the most important components of land-atmosphere coupling system, and soil moisture monitoring plays a significant part in climate, hydrology, and agriculture. Active microwave and passive microwave are two basic microwave approaches which are used to monitor soil moisture. As of now, the passive microwave method is widely used due to its longer wavelengths and stronger penetrating power. It was considered that the passive microwave retrieved method could work well in effectively monitoring spatial and temporal changes of soil moisture in large-scale areas. However, the data retrieved by satellites needs further evaluation and verification. At present, various microwave methods have been proposed for soil moisture retrieve, and a number of corresponding soil moisture products have also been published. Compared to station-based data, remote sensing data can better reveal the dynamic change of soil moisture in a certain region at grid points. Based on the observed data of station-based soil moisture at the upper soil layer (0-10cm) during the growing season (May-October) in 2018, this paper collected and examined the remote senescing datasets from FY-3B, FY-3C, ASMR2 and SMOS which were consistent with the station-based data in time and space. Furthermore, the applicability of FY-3B/3C fusion in different regions of Inner Mongolia was evaluated, which may provide a reliable scientific basis for the application of soil moisture products based on Fengyun Satellites and other related researches. The ascending and descending data of FY-3B and FY-3C were fused respectively by employing weighted average method. In order to evaluate and compare the applicability of remote senescing datasets from AMSR2, FY-3B/3C and SMOS in Inner Mongolia, FY-3B/3C datasets were then formed by random forest method. The results showed that daytime data were of better quality than night data of FY-3B ascending/descending and FY-3C ascending/descending. The data quality of fused FY-3B and FY-3C processed by weighted average method exhibited no significantly improved. And the data quality of FY-3B/3C products formed by random forest models was significantly enhanced. In the rainy season of high vegetation coverage area (NE), the quality of FY-3B/3C data products were better than those of SMOS and AMSR2. Overall, in Inner Mongolia,SMOS is more applicable in Middle (M) and Southeast (SE) regions, AMSR2 has poor applicability in the whole region, while FY-3B/3C performs the best.

Key words:  FY-3B/3C, Soil moisture, Data fusion, Remote sensing monitoring, Applicability

中图分类号: