A framework for estimating all-weather fine resolution soil moisture from the integration of physics-based and machine learning-based algorithms
A framework for estimating all-weather fine resolution soil moisture from the integration of physics-based and machine learning-based algorithms
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结合基于物理和机器学习的算法来估计全天候高分辨率土壤湿度的框架
DOI：
10.1016/j.compag.2023.107673
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发表时间：
2023-03
影响因子：
8.3
通讯作者：
Zhao-Liang Li
中科院分区：
文献类型：
--
作者：
Pei Leng;Zhe Yang;Qiu-Yu Yan;Guo-Fei Shang;Xia Zhang;Xiao-Jing Han;Zhao-Liang Li
由于射频干扰的影响和特定条件下算法的局限性，目前大多数基于微波的土壤水分（SM）产品是空间不连续的，具有粗糙的空间分辨率，而光学观测也揭示了各种数据缺口，由于云污染。因此，在无效像素上的SM的预测和从粗尺度到高尺度的分解是用于以精细时空分辨率获得SM的两个主要过程（例如，每日/1公里）。在本研究中，两种方法，相对于分解优先或预测优先的协同使用广泛认可的欧洲空间可持续性-气候变化倡议（ESA-CCI）SM产品和中分辨率成像光谱仪（MODIS）图像在青藏高原（TP）地区进行了研究。具体而言，基于物理和理论尺度变化的分解（DisPATCh）算法和广义回归神经网络（GRNN）分别实现在分解和预测。在DisPATCh中，空间完整的地表温度（LST），归一化植被指数（NDVI）和数字高程模型（DEM）作为必要的输入，以缩小规模的微波为基础的ESA-CCI的空间分辨率为1公里，而MODIS派生的LST，NDVI，地表温度和DEM被认为是在GRNN预测。根据这两种方法，最终估计了三年内每日/1 km SM数据集。在TP区域的地面原位SM测量的评估揭示了一个可接受的精度与无偏均方根误差为1.06 m3/m3，表明在未来的发展中获得业务每日/1公里空间连续SM产品的潜力。
Due to the effects of radio frequency interference and the limitations of algorithms under specific conditions, most of the currently available microwave-based soil moisture (SM) products are spatially discontinuous and have coarse spatial resolution, whereas optical observations also reveal various data gaps due to cloud contamination. Hence, the prediction of SM over invalid pixels and disaggregation from coarse to high scales are two main processes for obtaining SM at fine spatiotemporal resolution (e.g., daily/1-km). In the present study, two methods with respect to disaggregation-first or prediction-first were investigated from the synergetic use of the widely recognized European Space Agency-Climate Change Initiative (ESA-CCI) SM product and Moderate Resolution Imaging Spectroradiometer (MODIS) images over the Tibetan Plateau (TP) region. Specifically, the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) algorithm and the generalized regression neural network (GRNN) were implemented in the disaggregation and prediction, respectively. In DisPATCh, spatially complete land surface temperature (LST), normalized difference vegetation index (NDVI) and digital elevation model (DEM) were provided as essential inputs to downscale the microwave-based ESA-CCI to a spatial resolution of 1 km, whereas MODIS-derived LST, NDVI, land surface albedo and DEM were considered in the GRNN prediction. Following the two methods, the daily/1-km SM dataset over a period of three years was finally estimated. Assessments with ground in-situ SM measurements over the TP region reveal an acceptable accuracy with unbiased root mean square errors of ∼ 0.06 m3/m3, indicating the potential to obtain operational daily/1-km spatially continuous SM products in future developments.
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影响因子：
5.4
作者：
Hanzi Mao;D. Kathuria;N. Duffield;B. Mohanty
通讯作者：
Hanzi Mao;D. Kathuria;N. Duffield;B. Mohanty
影响因子：
6.1
作者：
H. Bormann
通讯作者：
H. Bormann
影响因子：
4.7
作者：
M. Neuhauser;S. Verrier;O. Merlin;B. Molero;C. Suere;S. Mangiarotti
通讯作者：
M. Neuhauser;S. Verrier;O. Merlin;B. Molero;C. Suere;S. Mangiarotti
影响因子：
3.8
作者：
Daqing Yang;B. Ye;A. Shiklomanov
通讯作者：
Daqing Yang;B. Ye;A. Shiklomanov
DOI：
10.1016/j.jag.2021.102333
发表时间：
2021-08
期刊：
Int. J. Appl. Earth Obs. Geoinformation
影响因子：
--
作者：
Yuean Qiu;Junxiong Zhou;Jin Chen;Xuehong Chen
通讯作者：
Yuean Qiu;Junxiong Zhou;Jin Chen;Xuehong Chen