多源微波遥感融合大尺度区域土壤水分数据集研究进展综述

doi:10.11871/jfdc.issn.2096-742X.2023.05.010

数据与计算发展前沿 ›› 2023, Vol. 5 ›› Issue (5): 119-127.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.05.010

doi: 10.11871/jfdc.issn.2096-742X.2023.05.010

多源微波遥感融合大尺度区域土壤水分数据集研究进展综述

刘杨晓月^1,^2,^*(),杨雅萍^1,²

1.中国科学院地理科学与资源研究所，资源与环境信息系统国家重点实验室，北京 100101
2.江苏省地理信息协同创新中心，江苏南京 210023

收稿日期:2022-07-17 出版日期:2023-10-20 发布日期:2023-10-31
通讯作者: *刘杨晓月（E-mail: lyxy@lreis.ac.cn）
作者简介:刘杨晓月，中国科学院地理科学与资源研究所，助理研究员，主要研究方向为多源数据融合在土壤水分数据反演中的应用。
本文中负责文献调研与初稿撰写。
LIU Yangxiaoyue is an assistant research fellow in the State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), Beijing, China. Her research interest is the application of multi-source data fusion in soil moisture data inversion.
In this paper, she is responsible for literature research and draft writing.
E-mail: lyxy@lreis.ac.cn
基金资助:
中国科学院网络安全和信息化专项应用示范项目(CAS-WX2021SF-0106-03);第二次青藏高原科学考察研究项目(2019QZKK09);国家自然科学基金(42101475);蒙古高原资源环境要素综合考察(2019FY102001);中国工程科技知识中心-地理资源与生态专业知识服务系统(CKCEST-2021-2-10);国家地球系统科学数据中心(http://www.geodata.cn/);中国科学院数据中心(WX145XQ07-11)

Progress on Large-Scale Soil Moisture Products by Fusion of Multi-Source Microwave Remote Sensing Datasets: a Review

LIU Yangxiaoyue^1,^2,^*(),YANG Yaping^1,²

1. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, China

Received:2022-07-17 Online:2023-10-20 Published:2023-10-31

摘要/Abstract

摘要：

【目的】 土壤水分是连接陆气水循环的重要媒介和载体。获取大尺度区域、长时间序列、高精度的土壤水分数据是环境遥感领域长期关注的难点和热点问题。【方法】 本文简述了包括必要气候变量土壤水分（Essential Climate Variable Soil Moisture, ECV SM）数据、土壤水分产品系统（Soil Moisture Products System, SMOPS）数据、土壤水分主动被动（Soil Moisture Active Passive, SMAP）数据、遥感地表土壤水分（Remote-sensing-based Surface Soil Moisture, RSSSM）数据、神经网络土壤水分（Neural Network soil moisture, NNsm）数据、高分辨率中国区域土壤水分数据在内的6种国内外典型多源微波遥感融合土壤水分数据集的发展历程、研制方法和精度水平。【结果】 通过分析数据产品的适用性，发现不同的微波遥感融合土壤水分产品各具特色，适用场景也有所区分。【结论】 如何充分利用现有海量微波遥感、光学遥感、站点观测数据，设计高性能土壤水分模拟融合算法，实现对过去、现在乃至未来多情景模式下多尺度土壤干湿状况的精准掌控，是未来环境遥感与数据融合领域值得持续深入探究的重要科学方向。

关键词: 微波遥感, 数据融合, 土壤水分

Abstract:

[Objective] Soil moisture is an important link connecting the land-atmosphere hydrological cycle. Retrieving large-scale, long-time series and high-precision soil moisture data products has been a challenging and hot issue in the field of environmental remote sensing for a long time.[Methods] This paper briefly introduces six kinds of typical international/domestic soil moisture products derived from the fusion of multi-microwave remote sensing datasets. They are Essential Climate Variable Soil Moisture (ECV SM), Soil Moisture Products System (SMOPS), Soil Moisture Active Passive (SMAP), Remote Sensing-based Surface Soil Moisture (RSSSM), Neural Network soil moisture (NNsm), and the fine-resolution soil moisture dataset for China, respectively. [Results] The development history, fusion method, accuracy level, and applicability of these products are analyzed. It is found that different microwave remote-sensing fused soil moisture products have their own characteristics, and the application scenarios are also different. [Conclusions] It is meaningful to make full use of the existing massive data obtained by microwave remote sensing, optical remote sensing, and site observation, and design a high-performance soil moisture simulation algorithm. Moreover, knowing accurate information on the dry and wet conditions of multi-scale land surface soil under the past, present and future multi-scenarios is an important scientific research direction, which is worthy of continuous and in-depth exploration in the field of environmental remote sensing and data fusion in the future.

Key words: microwave-based remote sensing, data fusion, soil moisture

刘杨晓月, 杨雅萍. 多源微波遥感融合大尺度区域土壤水分数据集研究进展综述[J]. 数据与计算发展前沿, 2023, 5(5): 119-127.

LIU Yangxiaoyue, YANG Yaping. Progress on Large-Scale Soil Moisture Products by Fusion of Multi-Source Microwave Remote Sensing Datasets: a Review[J]. Frontiers of Data and Computing, 2023, 5(5): 119-127, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.05.010.

图/表 1

表1

不同微波遥感融合土壤水分产品特征"

数据集名称	空间范围	空间分辨率	时间范围	时间分辨率	质量特征
ECV	全球	0.25°	1978.11-2020.12	逐日	ECV土壤水分数据整体精度随时间向现在推进而逐步提升，融合了多种不同任务周期的星载微波遥感数据，时间序列长达40余年，是迄今为止时间序列最长的多源微波融合全球日尺度土壤水分产品
SMOPS	全球	0.25°	2017.3至今	6小时、逐日	SMOPS土壤水分数据实时获取能力较强，6小时数据的时滞仅为3小时，日尺度数据的时滞仅为6小时。SMOPS提供对应像元的质量信息与元数据信息，能够有效辅助使用者了解掌握数据的时空精度
SMAP	全球	1/3/9/36km	2015.4至今	3小时（同化）、逐日（9/36km）、12天（1/3 km）	SMAP数据质量较好，1级亮温产品的时滞不超过12小时，2级单轨传感器反演土壤水分产品的时滞不超过24小时，3级合成土壤水分产品的时滞不超过50小时，4级无缝地表及根区同化土壤水分产品的时滞不超过7天
RSSSM	全球	0.1°	2003.1-2018.12	10天	RSSSM以SMAP为训练对象基于神经网络算法驱动建模，时间序列精度和空间覆盖率较高。就不同季节而言，数据在夏季（植被生长季）精度较高，在冬季精度相对偏低。时间序列拟合度和空间覆盖率优于ECV
NNsm	全球	36km	2002.6-2019.12	逐日	NNsm与SMAP相关性高，在插补SMAP缺值区域、时间序列扩展和高频监测全球土壤水分演化动态方面潜力巨大。数据误差小于ECV而拟合优度略差于ECV
高分辨率中国区域土壤水分数据	中国区域	0.05°	2002.7-2018.12	逐月	高分辨率中国区域土壤水分数据在东北季风区和华北季风区精度稳定，而华南季风区和青藏高原地区数据精度波动较大。与RSSSM数据呈现良好的时空一致性

表1

参考文献 29

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多源微波遥感融合大尺度区域土壤水分数据集研究进展综述

Progress on Large-Scale Soil Moisture Products by Fusion of Multi-Source Microwave Remote Sensing Datasets: a Review

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