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

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

Abstract

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

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.

Figures/Tables 1

Table 1

Basic attributes of different microwave fused soil moisture products"

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

Table 1

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