The validation of remote sensing land surface temperature (LST) data necessitates a comparison between satellite retrieval outcomes andin situobservations. The efficiency ofin situobservations can be ameliorated via analysis and modeling, whereby the heterogeneity ofin situobservations on temporal and spatial scales is central to the analysis. A fresh algorithm has been developed to optimize deployment by relying on the standard deviation of spatial heterogeneity. The validation outcomes indicated that the coefficient of determination (R2) of the five typical surface features at three time points was 0.66, with a root mean square error (RMSE) of 1.99 °C and a mean absolute error (MAE) of 1.62 °C. Moreover, the spatiotemporal heterogeneity character of typical surface features displayed different features, and the LST variation curves of each typical surface feature displayed a similar pattern under sunny conditions. The application of the Savitzky–Golay filtering method reduced errors by 4% of the total errors caused by random errors inin situobservations. With the analysis of the spatiotemporal characteristics of in-situ observation. First, the number of required sites algorithm computed a minimum sampling number of 4. Second, the analysis of the means algorithm computed the 5 optimal points. Additionally, the multipointin situobservations were regularized by standard scores. The optimization of the selected points could be executed to improve the results by eliminating the "distance" points, which are located further away from the multipointin situobserved LST statistical mean. Our outcomes will deepen the comprehension of the spatiotemporal character ofin situobserved LST and enhance the efficiency of equipment with equivalent accuracy.

Uncertainty quantification is an important part of validation, because the pixel scale reference generally suffers from uncertainty caused by different factors, lowering the accuracy of validation results. In order to take a step forward to characterize the uncertainty of validation results, this study proposed a simulated shift-based pixel matching (SSPM) method with the aim of quantifying the uncertainty caused by geometric mismatch in the multiscale validation. Furthermore, its relationships with spatial heterogeneity and subpixel size were also explored. It was found that the uncertainty caused by the geometric mismatch is nonnegligible in multiscale validation, which would obscure the true accuracy of satellite products. Spatial heterogeneity makes a positive contribution to the uncertainty caused by geometric mismatch, but the magnitude depends on subpixel size, being weaker with small subpixel size and stronger with larger subpixel size. Subpixel size is generally positively related to geometric uncertainty. But in the case of very large spatial heterogeneity, their correlation is very weak. This study is an important step toward quantitatively characterizing the uncertainties of pixel scale reference in order to increase the confidence of validation results.

Validation of satellite albedo products is an essential step because their quantitative application lie in their ability to record the real state of the earth surface. Upscalingin situmeasurements to the corresponding pixel scale is necessary due to the spatial scale mismatch betweenin situand satellite measurements. Machine learning-based models have been increasingly used for upscaling because they can yield more reliable results than traditional methods. Nevertheless, the main controlling factors on upscaled results have rarely been discussed. This article explores the control factors that bring uncertainties to the upscaled results based on machine learning models. Three machine learning models, including random forest (RF),$k$-nearest neighbor (KNN), and Cubist models, were selected to upscale single sitein situ-based albedo to the coarse pixel scale. The upscaled results were carefully assessed through comparison with pixel scale albedo reference. The results indicate that the accuracy of upscaled results depends on the machine learning models, the inclusion of key variables related to albedo, the dataset selection of these variables, the amount of training data, and the sensitivity of machine learning models to these factors. Despite the dependence on control factors, the machine learning-based upscaling methods generally have excellent applicability across different spatial scales and over other untrained areas. Therefore, they open the door to generating a time series of globally, spatially continuous distributed reference datasets with sufficient length, consistency, and continuity to adequately fulfill the requirement of a comprehensive validation.

A comprehensive assessment of satellite-derived albedo products is undeniably essential for better use consideration and the further refinement of the retrieval algorithm. Although satellite albedo products have been extensively validated over spatially homogeneous areas, it remains a challenge to validate them over rugged terrain. Consequently, the accuracy of satellite albedo products over rugged terrain is still unknown. This study for the first time systematically evaluated two widely used satellite albedo products (i.e., MCD43A3 V006 and GLASS V04 albedo) over mountainous areas with a Mountain Radiation Transfer (MRT) coupled multi-scale validation strategy. Fine-scale albedo was first generated with a root mean square error (RMSE) smaller than 0.0317. Then they were upscaled to the coarse pixel and as the reference data for validation. The validation results indicated that the accuracy of the two products tends to decrease with the increase of means slopes. The RMSE and relative RMSE (RMSER) of full retrieval MCD43A3 C6 black-sky albedo (BSA) and white-sky albedo (WSA) over abrupt slopes (mean slope >10°) increase to 0.0432 and 31.87% and to 0.0436 and 32.21%, respectively. The RMSE and RMSERof high quality GLASS V04 were 0.0452 and 33.71% of BSA and 0.0458 and 33.92 % of WSA respectively over abrupt slopes. Particularly, if the backup retrievals were included over the abrupt slopes, the RMSE and RMSER of MCD43A3 C6 can reach to 0.0600 and 36.92% for BSA and 0.0613 and 37.67% for WSA, and those of GLASS V04 can reach to 0.0567 and 36.28% for BSA and 0.0540 and 35.72% respectively.

A comprehensive assessment of satellite-derived albedo products is undeniably essential for better use consideration and the further refinement of the retrieval algorithm. Although satellite albedo products have been extensively validated over spatially homogeneous areas, it remains a challenge to validate them over rugged terrain. Consequently, the accuracy of satellite albedo products over rugged terrain is still unknown. This study for the first time systematically evaluated two widely used satellite albedo products (i.e., MCD43A3 C6 and Global Land Surface Satellite (GLASS) V04 albedo) over mountainous areas with a mountain radiation transfer (MRT) coupled multiscale validation strategy. Fine-scale albedo was first generated with a root-mean-square error (RMSE) smaller than 0.0317. Then, they were upscaled to the coarse pixel and as the reference data for validation. The validation results indicated that the accuracy of the two products tends to decrease with the increase of means slopes. The RMSE and relative RMSE (RMSER) of full retrieval MCD43A3 C6 black-sky albedo (BSA) and white-sky albedo (WSA) over abrupt slopes (mean slope >10°) increase to 0.0432 and 31.87% and to 0.0436 and 32.21%, respectively. The RMSE and RMSERof high-quality GLASS V04 were 0.0452 and 33.71% of BSA and 0.0458 and 33.92% of WSA, respectively, over abrupt slopes. In particular, if the backup retrievals were included over the abrupt slopes, the RMSE and RMSERof MCD43A3 C6 can reach 0.0600 and 36.92% for BSA and 0.0613 and 37.67% for WSA, respectively, and those of GLASS V04 can reach 0.0567 and 36.28% for BSA and 0.0540 and 35.72%, respectively.

Characterizing the subpixel heterogeneity within satellite pixels is a key issue in validation. Nevertheless, it is challenging due to multi-scale problems in the geological description based on remote sensing. Based on an airborne platform, the multi-scale variation laws of several key indicators in validation including spatial heterogeneity (SH), representativeness errors, and representative area with subpixel size were analyzed and discussed. Furthermore, this article discussed the optimal subpixel size to assess SH within a coarse pixel and the optimal footprint ofin situmeasurements for building dense and sparse validation networks. SH decreases with the increase of subpixel size. And a reduction of about 10% can be obtained from 5 m$\times 5$m to 150 m$\times150$m subpixel size, depending on the degree of SH within the typical satellite pixels. And the sensitiveness of SH to subpixel size decreases gradually with the increasing of subpixel size. Ideally, SH should be assessed using maps with pixel sizes corresponding to the footprint ofin situmeasurements. Regarding the deployment of future validation networks, the footprint ofin situsites should be designed at least larger than 25 m for dense networks. And much larger footprints (e.g., 100 m) are preferred in designing sparse networks. The representativeness error is not fully related to subpixel sizes because it is affected by many factors. The findings are also transferable to model evaluation when comparing model grid values to local observations.

Hydrothermal uranium deposit is very important in China and mainly distributes in the south of the country. The deposit often occurred in an area of multi periods magmatism and tectonic action. In order to summarize the characteristics of this area in remote sensing, method of visual interpretation, calculation of fractal box dimension and a-f(a) multi-fractal spectral are used to study the texture feature of ETM image in 11uranium ore-field and their neighbor area.