# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## Validation Guidelines and Technical Specifications
### Products of Interest  
Currently, we focus on 16 land surface variables, as summarised in Table 1. For each variable, typical remote sensing observation methods and previous validation studies as well as our validation activities are presented. Based on discrepancies in spatial sampling and aggregation methods used in validation, the 16 variables can be grouped into the following two types (the first column in Table 1).  
1. Continuous product. This type of variable features continuously varying values in time and space at each grid. Aggregating ground measurements into the satellite pixel scale can be performed using conventional spatial sampling methods (such as simple random sampling, systematic sampling, and stratified sampling). In addition, the aggregated value is a weighted sum of all samples, in which the sample weight is determined by the sampling method.
2. Categorical product. The value of each grid of this type of product is usually assigned as a group name instead of magnitudes and numbers in order or size. Pixel scale values can also be validated by ground-based spatial sampling, while the aggregation methods differ from those used for the continuous products.  
Most of the variables in this table are usually observed on the ground at the point scale. However, different RSPs characterise varying spatial resolutions (e.g., soil moisture and LAI products), ground measurements are unable to cover all of these scales. Therefore, point-scale measurements (by fixed station or mobile device) must be rationally deployed with an appropriate sampling scheme. On this basis, the collected ground measurements can then be aggregated into the pixel scale to validate corresponding RSPs with the aid of upscaling approaches, particularly against coarse-scale products (e.g., [44]).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types of RSPs** & **Variables** & **RS Observation** & **Spatial** & **Previous Studies** & **Our Validation Effects (Basic Information and References)** \\ \hline \hline \multirow{8}{*}{Continuous product} & Albedo & VNIR * & 30 m-1 km & [39,45, 46, 47, 48, 49, 50, 51, 52] & Multi-scale validation strategy for satellite albedo products [53]; Optimal nodes selectiveness from WSN to fit field scale albedo observation and validation [54] \\ \cline{2-5}  & Emissivity & TIR & 90 m-1 km & [15, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types of RSPs** & **Variables** & **RS Observation Method** & **Spatial Resolution** & **Previous Studies** & **Our Validation Effects (Basic Information and References)** \\ \hline \hline \multirow{8}{*}{Continuous product} & NPP & VNIR, LiDAR & 30 m-1 km & [106–110] & Validation of MODIS GPF product at ten flux sites in northern China [111] \\  & & NPP & VNIR, LiDAR & 30 m-1 km & [106–110] & Spatial sampling design for estimating regional GPF [112] \\  & & NPP & VNIR, LiDAR & 30 m-1 km & [114–117] & Analysis of spatial representiveness and uncertainty of flux measurement \\  & &