Xiaodan Wu

Wireless Sensor Network of Typical Land Surface Parameters and Its Preliminary Applications for Coarse-Resolution Remote Sensing Pixel

How to obtain the “truth” of land surface parameter as reference value to validate the remote sensing retrieved parameter in heterogeneous scene and coarse-resolution pixel is one of the most challenging topics in environmental studies. In this paper, a distributed sensor network system named CPP-WSN was established to capture the spatial and temporal variation of land surface parameters at coarse-resolution satellite pixel scale around the Huailai Remote Sensing Station, which locates in the North China Plain. The system consists of three subnetworks that are RadNet, SoilNet, and VegeNet. Time series observations of typical land surface parameters, including UVR, PAR, SWR, LWR, albedo, and land surface temperature (LST) from RadNet, multilayer soil moisture and soil temperature from SoilNet, and fraction of vegetation cover (FVC), clumping index (CI), and leaf area index (LAI) from VegeNet, have been obtained and shared on the web. Compared with traditional single-point measurement, the “true” reference value of coarse pixel is obtained by averaging or representativeness-weighted averaging the multipoint measurements acquired using the sensor network. The preliminary applications, which validate several remote sensing products with CPP-WSN data, demonstrate that a high quality ground “truth” dataset has been available for remote sensing as well as other applications.

Optimal Nodes Selectiveness from WSN to Fit Field Scale Albedo Observation and Validation in Long Time Series in the Foci Experiment Areas, Heihe

To evaluate and improve the quality of land surface albedo products, validation with ground measurements of albedo is crucial over the spatially and temporally heterogeneous land surface. One of the essential steps for satellite albedo product validation is coarse scale observation technique development with long time ground-based measurements. In this paper, the optimal nodes were selected from the wireless sensor network (WSN) to perform observation at large scale and in longer time series for validation of albedo products. The relative difference is used to analyze the spatiotemporal representativeness of each node. The random combination method is used to assess the number of required sites (NRS) and then to identify the most representative combination (MRC). On this basis, an upscaling transform function with different weights for each node in the MRC, which are calculated with the ordinary least squares (OLS) linear regression method, is used to upscale WSN node albedo from point scale to the field scale. This method is illustrated by selecting the optimal nodes and upscaling surface albedo from point observation to the field scale in the Heihe River basin, China. Primary findings are: (a) The method of reducing the number of observations without significant loss of information about surface albedo at field scale is feasible and effective; (b) When only few sensors are available, the most representative locations in time and space should be the first priority; when a number of sensors are available in the heterogeneous land surface, it is preferable to install them in different land surface, rather than the most representative locations; (c) The most representative combination (MRC) combined with the upscaling weight coefficients can give a robust estimate of the field mean surface albedo. These efforts based on ground albedo observations promote the chance to use point information for validation of coarse scale albedo products. Moreover, a preliminary validation of the MODIS (Moderate Resolution Imaging Spectroradiometer) albedo product was performed as the tentative application for upscaling predictions.

A Multi-Scale Validation Strategy for Albedo Products over Rugged Terrain and Preliminary Application in Heihe River Basin, China

The issue for the validation of land surface remote sensing albedo products over rugged terrain is the scale effects between the reference albedo measurements and coarse scale albedo products, which is caused by the complex topography. This paper illustrates a multi-scale validation strategy specified for coarse scale albedo validation over rugged terrain. A Mountain-Radiation-Transfer-based (MRT-based) albedo upscaling model was proposed in the process of multi-scale validation strategy for aggregating fine scale albedo to coarse scale. The simulated data of both the reference coarse scale albedo and fine scale albedo were used to assess the performance and uncertainties of the MRT-based albedo upscaling model. The results showed that the MRT-based model could reflect the albedo scale effects over rugged terrain and provided a robust solution for albedo upscaling from fine scale to coarse scale with different mean slopes and different solar zenith angles. The upscaled coarse scale albedos had the great agreements with the simulated coarse scale albedo with a Root-Mean-Square-Error (RMSE) of 0.0029 and 0.0017 for black sky albedo (BSA) and white sky albedo (WSA), respectively. Then the MRT-based model was preliminarily applied for the assessment of daily MODerate Resolution Imaging Spectroradiometer (MODIS) Albedo Collection V006 products (MCD43A3 C6) over rugged terrain. Results showed that the MRT-based model was effective and suitable for conducting the validation of MODIS albedo products over rugged terrain. In this research area, it was shown that the MCD43A3 C6 products with full inversion algorithm, were generally in agreement with the aggregated coarse scale reference albedos over rugged terrain in the Heihe River Basin, with the BSA RMSE of 0.0305 and WSA RMSE of 0.0321, respectively, which were slightly higher than those over flat terrain.

Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface

ABSTRACT Upscaling ground albedo is challenged by the serious discrepancy between the heterogeneity of land surfaces and the small number of ground-based measurements. Conventional ground-based measurements cannot provide sufficient information on the characteristics of surface albedo at the scale of coarse pixels over heterogeneous land surfaces. One method of overcoming this problem is to introduce high-resolution albedo imagery as ancillary information for upscaling. However, due to the low frequency of updating of high-resolution albedo maps, upscaling time series of ground-based albedo measurements is difficult. This paper proposes a method that is based on the idea of conceptual universal scaling methodology for establishing a spatiotemporal trend surface using very few high-resolution images and time series of ground-based measurements for spatial-temporal upscaling of albedo. The construction of the spatiotemporal trend surface incorporates the spatial information provided by auxiliary remote sensing images and the temporal information provided by long time series of ground observations. This approach was illustrated by upscaling ground-based fine-scale albedo measurements to a coarse scale over the core study area in HiWATER. The results indicate that this method can characterize the spatiotemporal variations in surface albedo well, and the overall correlation coefficient was 0.702 during the study period.

A web-based land surface remote sensing products validation system (LAPVAS): application to albedo product

ABSTRACT Quantitative remote sensing product (QRSP) validation is a complex process to assess the accuracy and uncertainty independently using reference data with multiple land cover types and long-time series. A web-based system named as LAnd surface remote sensing Product VAlidation system (LAPVAS) is described in this paper, which is used to implement the QRSPs validation process automatically. The LAPAVS has two subsystems, the Validation Databases Subsystem and the Accuracy Evaluation Subsystem. Three functions have been implemented by the two subsystems for a comprehensive QRSP validation: (1) a standardized processing of reference data and storage of these data in validation databases; (2) a consistent and comprehensive validation procedure to assess the QRSPs’ accuracy and uncertainty; and (3) a visual process customization tool with which the users can register new validation data, host new reference data, and readjust the validation workflows for the QRSP accuracy assessment. In LAPVAS, more than 100 GB of reference data warehoused in validation databases for 13 types of QRSPs’ validation. One of the key QRSPs, land surface albedo, is selected as an example to illustrate the application of LAPVAS. It is demonstrated that the LAPVAS has a good performance in the land surface remote sensing product validation.

Upscaling in situ albedo for validation of coarse scale albedo product over heterogeneous surfaces

ABSTRACT One of the essential steps for satellite albedo validation is upscaling in situ measurements to corresponding pixel scale over relatively heterogeneous land surfaces. Although the multi-scale validation strategy is applicable for heterogeneous surfaces, the calibration of the high-resolution imagery during upscaling process is never perfect, and thus the upscaling results suffer from errors. The regression-kriging (RK) technique can compensate the calibration part by applying kriging to upscale residuals and produce more accurate upscaling results. In this paper, in situ measurements and high spatial resolution albedo imagery combined with RK technique was proposed. This method is illustrated by upscaling surface albedo from in situ measurements scale to the coarse pixel scale in the core experimental area of HiWATER, where 17 WSN nodes were deployed at heterogeneous area. The upscaling results of this method were compared with the upscaling results from multi-scale strategy. The results show that the upscaling method based on in situ measurements and high-resolution imagery combined with RK technique can capture the spatial characteristics of surface albedo better. Further, an attempt was made to expand this method in time series. Finally, a preliminary validation of the Moderate Resolution Imaging Spectroradiometer albedo product was performed as the tentative application.

Sensor intercomparison of distributed surface radiation measurement system

The Wireless Sensor Networks of Coarse-resolution Pixel Parameters (CPP-WSN) was established to monitor the heterogeneity of coarse spatial resolution pixel, with consideration of different categories of land surface parameters in Huailai, Hebei province, China (40.349°N, 115.785°E). The observation network of radiation parameters (RadNet) in CPP-WSN was developed for multi-band radiation measurement and consisted of 6 nodes covering 2km*2km area to capture its heterogeneity. Each node employed four sensors to observe the five radiation parameters. The number and location of nodes in RadNet were determined through the representativeness-based sampling method. Thus, the RadNet is a distributed observation system with nodes work synchronously and measurements used together. The intercomparison experiment for RadNet is necessary and was conducted in Huailai Remote Sensing Station from 5th Aug to 10th Aug in 2012. Time series observations from various sensors were collected and analyzed. The maximum relative differences among sensors of UVR, SWR, LWR, PAR, and LST are 4.83%, 5.3%, 3.71%, 11%, and 0.54%, respectively. Sensor/parameter differences indeed exist and are considerable large for PAR, SWR, UVR, and LWR, which cannot be ignored. The linear normalization and quadratic polynomial normalization perform similar for CUV5/UVR, PQS1/PAR, CNR4/SWR, and SI-111/LST. As for CNR4/LWR, quadratic polynomial normalization show higher accuracy than linear normalization, especially in node2, node4, and node5. Thus, the LWR measured by CNR4 is proved to be nonlinear, and should be normalized with quadratic polynomial coefficients for higher precision.

Remote sensing albedo product validation over heterogenicity surface based on WSN: preliminary results and its uncertainty

The evaluation of uncertainty in satellite-derived albedo products is critical to ensure their accuracy, stability and consistency for studying climate change. In this study, we assess the Moderate-resolution Imaging Spectroradiometer(MODIS) albedo 8 day standard product MOD43B3 using the ground-based albedometer measurement based on the wireless sensor network (WSN) technology. The experiment have been performed in Huailai, Hubei province. A 1.5 km*2 km area are selected as study region, which locates between 115.78° E-115.80° E and 40.35° N-40.37° N. This area is characterized by its distinct landscapes: bare ground between January and April, corn from May to Octorber. That is, this area is relatively homegeneous from January to Octorber, but in Novermber and December, the surface is very heterogeneous because of straw burning, as well as snow fall and snow melting. It is a big challenge to validate the MODIS albedo products because of the vast difference in spatial resolution between ground measurement and satellite measurement. Here, we use the HJ albedo products as the bridge that link the ground measurement with satellite data. Firstly, we analyses the spatial representativeness of the WSN site under green-up, dormant and snow covered situations to decide whether direct comparison between ground-based measurement and MODIS albedo can be made. The semivariogram is used here to describe the ground hetergeneity around the WSN site. In addition, the bias between the average albedo of the certain neighborhood centered at the WSN site and the center pixel albedo is also calculated.Then we compare the MOD43B3 value with the ground-based value. Result shows that MOD43B3 agree with in situ well during the growing season, however, there are relatively large difference between ground albedos and MCD43B3 albedos during dormant and snow-coverd periods.

Assessment of NPP VIIRS Albedo Over Heterogeneous Crop Land in Northern China

In this paper, the accuracy of Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) land surface albedo, which is derived from the direct estimation algorithm, was assessed using ground-based albedo observations from a wireless sensor network over a heterogeneous cropland in the Huailai station, northern China. Data from six nodes spanning 2013-2014 over vegetation, bare soil, and mixed terrain surfaces were utilized to provide ground reference at VIIRS pixel scale. The performance of VIIRS albedo was also compared with Global LAnd Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) albedos (Collection 5 and 6). The results indicate that the current granular VIIRS albedo has a high accuracy with a root-mean-square error of 0.02 for typical land covers. They are significantly correlated with ground references indicated by a correlation coefficient (R) of 0.73. The VIIRS albedo shows distinct advantages to GLASS and MODIS albedos over bare soil and mixed-cover surfaces, while it is inferior to the other two products over vegetated surfaces. Furthermore, its time continuity and the ability to capture the abrupt change of surface albedo are better than that of GLASS and MODIS albedo.

Evaluation of the MODIS and GLASS albedo products over the Heihe river Basin, China

This study describes the use of ground-based albedometer measurement based on the automatic weather stations (AWS) for validating MCD43A3 and GLASS albedo products over heterogeneous landscapes in Heihe river Basin, China. Because the footprint of ground observed albedo was far less than the spatial resolution of albedo products, high-resolution albedo imageries were used as an upscaling bridge to reduce the scale discrepancy. Based on this scheme, we present the results from an accuracy assessment of MODIS and GLASS. The validation results show that MODIS and GLASS have RMSEs less than 0.05 over large areas and over a full year of measurements.