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, representativeness errors, and representative area with subpixel size, were analyzed and discussed. Furthermore, this paper discussed the optimal subpixel size to assess spatial heterogeneity within a coarse pixel and the optimal footprint of in situ measurements for building dense and sparse validation networks. Spatial heterogeneity decreases with the increase of subpixel size. And a reduction about 10% can be obtained from 5 m×5 m to 150 m×150 m subpixel size, depending on the degree of spatial heterogeneity within the typical satellite pixels. And the sensitiveness of spatial heterogeneity to subpixel size decreases gradually with the increasing of subpixel size. Ideally, spatial heterogeneity should be assessed using maps with pixel sizes corresponding to the footprint of in situ measurements. Regarding the deployment of future validation networks, the footprint of in situ sites 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.

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. Upscaling in situ measurements to the corresponding pixel scale is necessary due to the spatial scale mismatch between in situ and 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 site in 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.

Quantifying the spatial, seasonal (phenological), and inter-annual variations of gross primary productivity (GPP) in the Arctic is critical for comprehending the terrestrial carbon cycle and its feedback to climate warming in this region. Here, we evaluated the accuracy of the MOD17A2H GPP product using the FLUXNET 2015 dataset in the Arctic, then explored the spatial patterns, seasonal variations, and interannual trends of GPP, and investigated the dependence of the spatiotemporal variations in GPP on land cover types, latitude, and elevation from 2001 to 2019. The results showed that MOD17A2H was consistent with in situ measurements (R = 0.8, RMSE = 1.26 g C m⁻² d⁻¹). The functional phenology was also captured by the MOD17A2H product (R = 0.62, RMSE = 9 days) in the Arctic. The spatial variation of the seasonal magnitude of GPP and its interannual trends is partly related to land cover types, peaking in forests and lowest in grasslands. The interannual trend of GPP decreased as the latitude and elevation increased, except for the latitude between 62°~66° N and elevation below 700 m. Our study not only revealed the variation of GPP in the Arctic but also helped to understand the carbon cycle over this region.