Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China
Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China
复制标题
青藏高原几种土壤湿度降尺度方法的比较
DOI：
10.1016/j.jhydrol.2020.125616
复制
发表时间：
2020-10
影响因子：
6.4
通讯作者：
Han Tian
中科院分区：
文献类型：
--
作者：
Qu Yuquan;Zhu Zhongli;Carsten Montzka;Chai Linna;Liu Shaomin;Ge Yong;Liu Jin;Lu Zheng;He Xinlei;Zheng Jie;Han Tian
微波遥感能够以足够的精度反演土壤水分(SM)。然而，这些微波遥感SM产品通常具有数十公里的空间分辨率，不能满足农业灌溉、当地水资源管理等精细到中等规模应用的要求。已经提出了几种SM降尺度方法来解决这种不匹配问题，方法是将粗尺度SM降至细尺度(几公里或数百米)。虽然已经对不同气候带、不同数据集进行了研究，取得了较好的结果，但仍缺乏对它们进行全面的比较和评价，以指导高分辨率和高精度的SM数据的生产。因此，在本研究中，我们从与原始微波产品的对比、与现场测量的对比、基于三角帽(TCH)方法的相互对比和空间可行性分析四个方面，对几种基于多元正态拟合、物理模型、机器学习和地统计学的SM降尺度方法(0.25°到0.01°)在气候条件广泛的青藏高原进行了比较。比较结果表明，基于物理模型的分解方法，在这种情况下，基于物理和理论尺度变化(调度)的分解方法在保持原始微波SM产品的粗尺度特征方面具有最高的能力，但在一定程度上，这种能力可能不利于提高降尺度结果的精度。此外，仅有土壤蒸发效率(SEE)不足以表示复杂地表上的SM空间格局。基于地统计学的面-面回归克里格法(ATARK)在残差内插过程中引入了最高的过校正不确定性，同时该过程还可以提高相关性(R)，纠正偏差，并提供更多可行的空间模式和细节。随机森林(RF)和高斯过程回归(GPR)两种机器学习方法对所有比较结果都表现出很高的稳定性，但提供了更平滑的空间模式。多元统计回归(MSR)方法由于其简单的线性回归模型不能满足复杂地表SM拟合的要求而表现最差。此外，所有五种降尺度方法在降尺度后精度都有所下降。这种现象可能是由细尺度上的空间失配引起的。此外，这也可能是因为缩小尺度的结果通常会提供更多的空间细节，而它们不能很好地捕捉微波SM产品的时间变化。一般来说，这种现象在非均质地表上更为明显。总而言之，在综合比较方案的基础上，对五种广泛使用的土壤水分降尺度方法进行了比较，以增加关于不同天气条件下降尺度方法的适用性的知识。
Microwave remote sensing is able to retrieve soil moisture (SM) at an adequate level of accuracy. However, these microwave remotely sensed SM products usually have a spatial resolution of tens of kilometers which cannot satisfy the requirements of fine to medium scale applications such as agricultural irrigation and local water resource management. Several SM downscaling methods have been proposed to solve this mismatch by downscaling the coarse-scale SM to fine-scale (several kilometers or hundreds of meters). Although studies have been conducted over different climatic zones and from different data sets with good results, there is still a lack of a comprehensive comparison and evaluation between them to guide the production of high-resolution and high-accuracy SM data. Therefore, in this study we compared several SM downscaling methods (from 0.25° to 0.01°) based on polynormal fitting, physical model, machine learning and geostatistics over the Qinghai-Tibet plateau where there is a wide range of climate conditions from four aspects, that is, comparison with the original microwave product, comparison with in situ measurements, inter-comparison based on three-cornered hat (TCH) method, and a spatial feasibility analysis. The comparison results show that the method based on a physical model, in this case the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) method, has the highest ability on preserving the coarse-scale feature of original microwave SM product, while to some extent, this ability could be a disadvantage for improving the accuracy of the downscaling results. In addition, soil evaporation efficiency (SEE) alone is not sufficient to represent SM spatial patterns over complex land surface. Geostatistics based area-to-area regression Kriging (ATARK) introduces the highest uncertainty caused by the overcorrection during the residual interpolation process while this process can also improve correlation (R) and correct the bias as well as provide more feasible spatial patterns and details. Two machine learning methods, the random forest (RF) and Gaussian process regression (GPR) show high stability on all comparison results but provide smoother spatial patterns. The multivariate statistical regression (MSR) method performs worst due to the fact that its simple linear regression model could not meet the requirement of SM fitting on complicated land surface. Moreover, all five downscaling methods show a declining accuracy after downscaling. This phenomenon may be caused by the spatial mismatch on fine-scale. In addition, this could also be caused by the tendency that downscaled results will usually provide more spatial details from downscaling predictors, while they cannot capture the temporal changes of the microwave SM product well. In general, this phenomenon tends to be more significant over heterogeneous land surface. All in all, five widely used soil moisture downscaling methods were compared based on a comprehensive comparison scheme to add to the body of knowledge in applicability of downcaling methods under different weather conditions.
登录
查看更多内容
用于遥感空间降尺度的地理加权面对点回归克里金法
影响因子：
5
作者：
Jin, Yan;Ge, Yong;Wang, Le
通讯作者：
Wang, Le
使用地理加权区域间回归克里金法缩小 AMSR-2 土壤湿度数据
影响因子：
8.2
作者：
Jin Yan;Ge Yong;Wang Jianghao;Chen Yuehong;Heuvelink Gerard B. M.;Atkinson Peter M.
通讯作者：
Atkinson Peter M.
DOI：
10.5539/jas.v8n10p107
发表时间：
2016-09
期刊：
The Journal of Agricultural Science
影响因子：
--
作者：
Kansuma Burapapol;R. Nagasawa
通讯作者：
Kansuma Burapapol;R. Nagasawa
学习图像超分辨率的局部高斯过程回归
影响因子：
6
作者：
Li Jianmin;Qu Yanyun;Li Cuihua;Xie Yuan;Wu Yang;Fan Jianping
通讯作者：
Fan Jianping
影响因子：
6.2
作者：
P. Song;Jingfeng Huang;Lamin R. Mansaray
通讯作者：
P. Song;Jingfeng Huang;Lamin R. Mansaray