## 1 Introduction  
An increasing number of earth observation satellites have been launched recently, and remote sensing technologies have continued to be developed [1]. The new sensors allow a large number of studies to produce abundant remote sensing products. New remote sensing retrieval algorithms have been emphasized by recent studies. Many projects have been launched to produce remote sensing products with long time series and global or regional coverage [2]. MODIS products are an example which comprise most varied class of products [3,4]. Validation of remote sensing products is important before the application of these products [5]. The Working Group on Calibration and Validation (WGCV) was established in 1984. Validation was defined by the WGCV as the process of assessing, by independent means, the quality of data products derived from system outputs [6]. The validation activities are defined as a means by which independent field, airborne, and other satellite data will be collected and used to assess the quality of remote sensing products [7]. Some validation plans have been implemented globally and regionally. For example, BigFoot as one of the earlier validation projects was initiated in 1999 [8,9]. Validation of the Land European Remote Sensing Instruments (VALERI) was initiated by the European Space Agency (ESA) at the beginning of the 21 st century [6]. The Land Product Validation (LPV) was established by the Committee of the Earth Observation Satellites (CEOS) for proposing a consistent strategy and dedicated methods and tools for validation and intercomparison of land biophysical products [7]. The above validation studies mainly use _in situ_ point observations to evaluate the estimation accuracy of remote sensing products. However, the representativeness of the _in situ_ point observations with regard to the pixel scales of the remote sensing data is questionable, especially for datasets with coarse spatial resolution. The scale and the scale effect are key scientific problems and are critical for up-scaling the field observation data to moderate resolution [10]. Even with significant efforts towards solving these key scientific problems, the insufficient understanding of the validation of remote sensing products and the lack of validation theories and practical methods has delayed validation projects relative to the development of algorithms for remote sensing data retrieval, which limits further applications of remote sensing data and products [11]. Some newly developed and advanced observation methods, such as wireless sensor networks (WSN) [12, 13] and footprint observation [14, 15, 16], have been initiated recently and have the potential to contribute to the resolution of the scale effect challenge. For example, tens to hundreds of nodes with automatic, intelligent, and remote-controllable sensors were installed in a 5 \(\times\)5 km region according to the spatial heterogeneity of the land surface parameters. These high-level, high-density, and automatic observation data can be used to measure the spatial distribution characteristics of eco-hydrological parameters (e.g., soil moisture, temperature, and LAI) and to develop scale transformation and validation methods [12]. Observations by eddy covariance (EC), large-aperture scintillometer (LAS), and cosmic-ray soil moisture/snow observing system (COSMOS) cover certain areas, with footprints of hundreds to thousands of meters [14, 15, 16]. Experiments were performed for data validation in the above projects. However, these experiments were regional and temporary. Some fixed sites can be used from existing networks such as the Aerosol Robotic NETwork (AERONET) [17], the Fluxnet Network (FLUXNET) [18], the Bench-mark Land Multisite Analysis and Intercomparison of Products (BELMANIP) [19], and the International Soil Moisture Network (ISMN) [20]. The underlying surface types of these sites normally include most typical land cover types.