以清河水库为研究区，选用landsat8卫星OLI数据与同步实测高锰酸盐指数（CODMn），通过SPSS软件分析OLI数据的单波段及波段组合与实测CODMn之间的相关关系，建立比值线性回归模型和比值非线性最小二乘支持向量机（LS-SVM）模型，对清河水库CODMn进行定量遥感反演研究。结果表明，相比于比值线性回归模型，LS-SVM模型更适合于清河水库CODMn的定量反演研究。LS-SVM模型将预测值与实测值的相关系数R2从0.6931提升到0.9427、误差的算数平方根RMSE从4.76减低到2.71、平均相对误差从7.89%减小到6.26%，利用LS-SVM模型明显提高了清河水库CODMn的反演精度。

以清河水库为研究区，选用landsat8卫星OLI数据与同步实测高锰酸盐指数（CODMn），通过SPSS软件分析OLI数据的单波段及波段组合与实测CODMn之间的相关关系，建立比值线性回归模型和比值非线性最小二乘支持向量机（LS-SVM）模型，对清河水库CODMn进行定量遥感反演研究。结果表明，相比于比值线性回归模型，LS-SVM模型更适合于清河水库CODMn的定量反演研究。LS-SVM模型将预测值与实测值的相关系数R2从0.6931提升到0.9427、误差的算数平方根RMSE从4.76减低到2.71、平均相对误差从7.89%减小到6.26%，利用LS-SVM模型明显提高了清河水库CODMn的反演精度。

Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10–15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.

. High-spatial-resolution and long-term climate data are\nhighly desirable for understanding climate-related natural processes. China\ncovers a large area with a low density of weather stations in some (e.g.,\nmountainous) regions. This study describes a 0.5′ (∼ 1 km)\ndataset of monthly air temperatures at 2 m (minimum, maximum, and mean proxy monthly temperatures, TMPs)\nand precipitation (PRE) for China in the period of 1901–2017. The dataset\nwas spatially downscaled from the 30′ Climatic Research Unit (CRU) time\nseries dataset with the climatology dataset of WorldClim using delta spatial\ndownscaling and evaluated using observations collected in 1951–2016 by 496\nweather stations across China. Prior to downscaling, we evaluated the\nperformances of the WorldClim data with different spatial resolutions and\nthe 30′ original CRU dataset using the observations, revealing that their\nqualities were overall satisfactory. Specifically, WorldClim data exhibited\nbetter performance at higher spatial resolution, while the 30′ original CRU\ndataset had low biases and high performances. Bicubic, bilinear, and\nnearest-neighbor interpolation methods employed in downscaling processes\nwere compared, and bilinear interpolation was found to exhibit the best\nperformance to generate the downscaled dataset. Compared with the\nevaluations of the 30′ original CRU dataset, the mean absolute error of the new dataset (i.e., of the 0.5′ dataset downscaled by bilinear interpolation) decreased by 35.4 %–48.7 % for TMPs and by 25.7 % for PRE. The root-mean-square error decreased by 32.4 %–44.9 % for TMPs and by 25.8 % for PRE. The Nash–Sutcliffe efficiency coefficients increased by\n9.6 %–13.8 % for TMPs and by 31.6 % for PRE, and correlation\ncoefficients increased by 0.2 %–0.4 % for TMPs and by 5.0 % for PRE. The new dataset could provide detailed climatology data and annual trends of all climatic variables across China, and the results could be evaluated well using observations at the station. Although the new dataset was not evaluated before 1950 owing to data unavailability, the quality of the new\ndataset in the period of 1901–2017 depended on the quality of the original\nCRU and WorldClim datasets. Therefore, the new dataset was reliable, as the\ndownscaling procedure further improved the quality and spatial resolution of\nthe CRU dataset and was concluded to be useful for investigations related\nto climate change across China. The dataset presented in this article has\nbeen published in the Network Common Data Form (NetCDF) at\nhttps://doi.org/10.5281/zenodo.3114194 for precipitation (Peng,\n2019a) and https://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m\n(Peng, 2019b) and includes 156 NetCDF files compressed in zip\nformat and one user guidance text file.\n