# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## 1 Introduction  
In recent decades, unprecedented advances in characterising land surface parameters from satellite platforms have been achieved, boosting the development and a wide usage of numerous remote sensing products (RSPs) (e.g., [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]). Although these products provide reasonable spatial and temporal coverage, their accuracies and precisions should be independently assessed with reference data prior to utilisation. This process is well known as validation [14; 15]. In practice, validation generally refers to assessing the uncertainties in remote-sensing-derived results (e.g., RSPs) via comparison with ground truth or inter-comparison with other alternative information that presumably represents the true state of a target. Since validation is essential in various remote sensing applications, over the past two decades, huge efforts have been devoted internationally to address this issue in depth. These activities include the establishment of validation sites and networks as well as the contribution of large field campaigns and observation programmes to the topic of validation. * Many core sites have been identified, such as the La Crau site for high-resolution SPOT data calibration/validation activities [16]; soil moisture (SM) observatories located in the Tibetan Plateau [17; 18]; and numerous enhanced Earth Observing System (EOS) land validation core sites [19] recommended by the Committee on Earth Observation (CEOS) Land Product Validation (LPV) sub-group [20]. * Heavily instrumented observation networks have been established on scales ranging from regional to continental. These networks, such as the FLUXNET [21], the International Soil Moisture Network (ISMN) [22] and the Soil Climate Analysis Network (SCAN) [23], have paved the way towards meeting a broad range of national and international requirements for validation by continuously observing terrestrial variables. * Intensive land surface/remote sensing experiments and field campaigns as well as research/observation plans/programmes can also greatly contribute to addressing validation issues, such as the Boreal Ecosystem-Atmosphere Study (BOREAS) [24], Cold Land Processes Field Experiment (CLPX) [25], the Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) [26], Hydrological and Atmospheric Pilot Experiment in the Sahel (HAPEX) [27], First International Satellite Land Surface Climatology Project Field Experiment (FIFE) [28], Soil Moisture Experiment (SMEX) [29], Validation of the Land European Remote Sensing Instruments (VALERI) [30], Heihe Watershed Allied Telemetry Experimental Research (HiWATER) [31], the Global Climate Observing System (GCOS) [32], Critical Zone Observatory (CZO) [33], Terrestrial Environmental Observatories (TERENO) [34], Coordinated Enhanced Observing Period (CEOP) [35] and BigFoot [36]. It can be clearly seen that there has been a consensus to use diverse observatories converted from the site-to-network scale as a promising way to provide insight into the validation process. As a result, substantial outcomes, such as the establishment of a validation framework [19; 37] and multi-scale sampling strategy in validation [38] as well as concrete validation activities concerning various land surface variables, were extensively reported (Table 1). However, previous investigations have also demonstrated that at least two challenges remain in the validation process. First, in existing validation exercises, homogeneous land surfaces are a focus and are commonly selected as a validation field to deploy in situ measurements, while heterogeneous scenes are rarely discussed [39]. This type of validation scheme may be disputable since an intrinsic characteristic of land surface is heterogeneity, as opposed to homogeneity. Therefore, the validation approach and/or scheme fitted to homogeneous conditions may not be suitable to heterogeneous land surfaces. This could potentially lead to an unreliable assessment of the performance of an RSP.