# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## Validation Guidelines and Technical Specifications
### Progress on the Development of Sampling Design and Scale Approach
#### 3.3.2 Scaling Methods  
From the perspective of the upscaling issue, variables with strong heterogeneity (e.g., ET and soil moisture) were intensively investigated. [PERSON] et al. [71] proposed an area-to-area regression kriging (ATARK) method to upscale sensible heat flux observations from EC scale to LAS footprints. Observations sourced from the footprints of 17 EC and four LAS systems were analysed. The results revealed the proposed ATARK was capable of delivering accurate predictions, with a high R\({}^{2}\) value (\(\geq\)0.9) acquired for the comparison between upscaled EC and LAS measured flux. Furthermore, [PERSON] et al. (2017) compared five upscaling methods and developed a combined method to acquire ET ground truth at the satellite pixel scale. Based on multi-site measurements from 11 EC systems and four groups of LASs in the HRB, the authors indicated that three simple upscaling methods--namely, the arithmetic average method, area-weighted method, and footprint-weighted method--showed promise when used over homogenous surfaces, with an average error of approximately 6%. However, for heterogeneous landscapes, auxiliary variables should be introduced to characterise heterogeneity, such as the integrated Priestley-Taylor equation method or the ATARK method, and can be used to improve the upscaled ET results. On this basis, a combined method was proposed to acquire both instantaneous and daily ET ground truth at the satellite pixel scale with the moment of MODIS overpass.  
[PERSON] et al. (2014) analysed regression kriging (RK)-based upscaling scheme for soil moisture observed via WSN with three types of remote sensing information. Compared with the ordinary kriging method, the derived spatial inferences showed that the RK method was able to decrease the RMSE by approximately 5%. [PERSON] et al. (2014) proposed a geostatistical approach to upscale soil moisture observations with unequal precisions. This approach considered random measurement errors and a Monte Carlo simulation in association with a block kriging (BK) upscaling strategy. When comparing the three upscaling approaches (simple average, error-free BK, and error-perturbed BK), the results showed that the aggregated soil moisture estimates were comparable, while the error-perturbed BK approach outperformed, with a minimum error standard deviation (<0.01 m\({}^{3}\cdot\)m\({}^{-3}\)).  
In addition, as shown in Component 1 in Figure 1, airborne mission is included. Using the observations acquired from a compact airborne spectrographic imager (CASI) aboard aircraft, a land-cover-based Linear Bi-directional Reflectance Distribution Function (BRDF) unmixing (LLBU) algorithm was proposed to estimate albedo over farmland in the HRB. The retrieved CASI albedo with 5 m resolution showed promising high accuracy, with an RMSE of 0.013 compared against in situ measurements. In addition, good performance was observed when the CASI albedo was upscaled to a 500 m pixel scale, with an RMSE of 0.019 compared with MODIS products ([PERSON] et al., 2014).  
The abovementioned research progress lays the foundation for answering several scientific questions; i.e., how could scale approaches be further developed to aggregate point-scale ground measurements to the satellite pixel scale, and how could the generated values improve spatial and temporal representativeness of heterogeneous land surfaces? In this case, there is an article ([PERSON] et al., 2014) summarising our various upscaling research.