A method for estimating spatially continuous soil moisture from the synergistic use of geostationary and polar-orbit satellite data
A method for estimating spatially continuous soil moisture from the synergistic use of geostationary and polar-orbit satellite data
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协同使用对地静止卫星和极轨卫星数据估算空间连续土壤湿度的方法
DOI：
10.1016/j.jhydrol.2022.127590
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发表时间：
2022-05
影响因子：
6.4
通讯作者：
Guofei Shang
中科院分区：
文献类型：
--
作者：
Qiu-Yu Yan;Pei Leng;Zhao-Liang Li;Qian-Yu Liao;Fang-Cheng Zhou;Xiao-Jing Han;Jianwei Ma;Yanlong Sun;Xiaojing Zhang;Guofei Shang
•协同地球静止卫星和极轨道卫星用于估算空间连续土壤湿度。•研究了不同时间组成周期的土壤水分恢复情况。•估算的土壤湿度通过再分析和微波数据集进行评估。土壤水分是地表能量平衡和水循环的关键变量，其时空动态变化对气候、农业等领域具有重要意义。光学遥感已被广泛用于较精细空间分辨率的SM估计。然而，光学观测容易受到云的污染，难以获得大区域的空间连续SM。在本研究中，利用中国风云（FY）地球静止（FY- 4a）和极轨道（FY- 3d）观测的协同利用，基于先前开发的梯形特征空间，以像素对像素的方式获得了整个内蒙古研究区几乎完全空间覆盖的半月SM数据集。以中国气象局土地资料同化系统（CLDAS）和土壤水分主动被动同化系统（SMAP）产品为参考，对研究区两种主要的土地覆被类型（草地和农田）进行了初步评价。结果表明，估算SM与参考SM数据集具有较好的相关性，相关系数在0.5 ~ 0.8之间。此外，草地（耕地）与CLDAS和SMAP的均方根误差分别为0.062（0.097）和0.055 (0.069)m 3 /m 3。
• Synergistic geostationary and polar-orbit satellites were used to estimate spatially continuous soil moisture. • Soil moisture retrievals with different temporal composing periods were investigated. • Estimated soil moisture were assessed with both reanalysis and microwave-based datasets. Soil moisture (SM) is a key variable in the surface energy balance and water cycle, and its spatiotemporal dynamics are of great significance to climate, agriculture and other fields. Optical remote sensing has been widely used to estimate SM with relatively fine spatial resolution. However, optical observations are easily contaminated by clouds, making it difficult to obtain spatially continuous SM over large regions. In the present study, a semimonthly SM dataset over the study area of the entire Inner Mongolia region with nearly full spatial coverage was derived from the synergistic use of China’s Feng-Yun (FY) geostationary (FY-4A) and polar-orbit (FY-3D) observations, following a previously developed trapezoid feature space in a pixel-to-pixel manner. A preliminary assessment was conducted to evaluate the performances of the proposed method over two main dominant land cover types (grassland and cropland) in the study region, where the China Meteorological Administration Land Data Assimilation System (CLDAS) and the Soil Moisture Active Passive (SMAP) SM products were provided as references. The results indicated that the estimated SM was well correlated to the referenced SM datasets, with a significant correlation coefficient varying from 0.5 to 0.8. Furthermore, for the grassland (cropland), unbiased root mean square errors of approximately 0.062 (0.097) m 3 /m 3 and 0.055 (0.069) m 3 /m 3 can be found when comparing the estimated SM with the CLDAS and the SMAP product, respectively.
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