# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## 4 Validation Activities at Reference Sites
### Validation Efforts in HiWATER  
HiWATER was a watershed-scale remote sensing experiment conducted in the HRB in Northwest China (2016). It greatly supported validation studies. This is because first, the HRB has a unique natural environment, which spans the mountain cryosphere (upstream), coexisting forest, irrigated oasis and farmland (midstream), and natural oasis, semi-desert, and desert areas (downstream). Such diverse landscapes allow us to validate different ecological and hydrological RSPs. Second, numerous experimental activities that concern remote sensing and land surface processes have been implemented in this river basin, resulting in robust research achievements and datasets as well as sound observation facilities [167]. Finally, remote sensing validation and its related issues were highly emphasised in HiWATER, reflected by the implementation of specific experimental configuration and observatory deployment that can contribute to validation activities. In HiWATER, ground truth is not limited to being collected at the point scale; there is also the pixel scale based on WSN and the footprint scale based on EC system and LAS measurements. Two ground-based WSNs were established over heterogeneous farmland in the middle stream of the HRB in 2012. One WSN was an eco-hydrological WSN (EHWSN) designed to capture spatial-temporal variations of soil moisture, soil temperature, and LST (Figure 3) [138; 139]. It is worth mentioning in Figure 3 that a flux observation matrix (the oblique red domain) was identified in HiWATER. Because this area is heterogeneous, a 4 km \(\times\) 4 km site was further identified (blue box) that can then be used to validate various MODIS products to minimise the PSF effect and geo-location uncertainties of sensors. The other WSN was LAINet, with each node equipped with light sensors to provide continuous measurements on corn LAI in growing season [89]. Such ground-based facilities can provide a priori spatial distribution information on eco-hydrological variables and can be used to evaluate corresponding RSPs on the order of a kilometer, i.e., approximately the MODIS pixel size. The utilisation of these WSNs in validation is highlighted by several case studies, as described in more detail below. (1) Validating LST and soil moisture using the EHWSN. [PERSON] and [PERSON] [95], investigated the scale mismatch between in situ and remotely sensed LST. Two types of ground-based observations were obtained from a CNR4 net radiometer and an SI-111 infrared radiometer. Then, these two ground-based measurements were compared with MODIS LST products (MOD11A1) with 1 km resolution, while an LST retrieval of 3 m resolution derived from airborne thermal-infrared observations was used to assess land heterogeneity. Based on a semi-variance analysis, the results suggested that it was not reliable to directly use ground-based LSTs to represent their located MODIS pixel value. Surface heterogeneity triggered strong spatial variations of LST and pronounced errors in the validation process. [PERSON] et al. [168] estimated soil moisture at the footprint scale using a novel Cosmic-ray Soil  
Figure 3: The setup of the EHWSN deployed in the Yingke and Daman irrigation districts in the middle stream of the HRB. The left panel shows the location, KEAs, and validation sites identified of the HRB. The right panel shows the EHWSN. Moisture Observing System (COSMOS) over heterogeneous farmland, and in situ measurements acquired by the EHWSN were used to calibrate and validate soil moisture estimates. (2) Validating vegetation index products using LAINet. The primary objective of LAINet was to collect continuous corn LAI measurements using 42 in situ WSN nodes. Thus, a comparative analysis was conducted among different ground measurements and MODIS observations [89]. This study included a direct comparison between LAINet measurements and data collected by LAI-2000. Daily and 5-day integrated times series analysis and aggregated ground LAINet LAI values were compared with satellite remotely sensed data.