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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.
Xingwen Lin; Jianguang Wen; Qinhuo Liu; Qing Xiao; DongQin You; Shengbiao Wu; Dalei Hao; Xiaodan Wu. A Multi-Scale Validation Strategy for Albedo Products over Rugged Terrain and Preliminary Application in Heihe River Basin, China. Remote Sensing 2018, 10, 156 .
AMA StyleXingwen Lin, Jianguang Wen, Qinhuo Liu, Qing Xiao, DongQin You, Shengbiao Wu, Dalei Hao, Xiaodan Wu. A Multi-Scale Validation Strategy for Albedo Products over Rugged Terrain and Preliminary Application in Heihe River Basin, China. Remote Sensing. 2018; 10 (2):156.
Chicago/Turabian StyleXingwen Lin; Jianguang Wen; Qinhuo Liu; Qing Xiao; DongQin You; Shengbiao Wu; Dalei Hao; Xiaodan Wu. 2018. "A Multi-Scale Validation Strategy for Albedo Products over Rugged Terrain and Preliminary Application in Heihe River Basin, China." Remote Sensing 10, no. 2: 156.
In this paper, the accuracy of Suomi National Polar-orbiting Partnership (NPP) 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 (WSN) over a heterogeneous cropland in the Huailai station, northern China. Data from 6 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 (RMSE) 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.
Xiaodan Wu; Jianguang Wen; Qing Xiao; Yunyue Yu; DongQin You; Andreas Hueni. Assessment of NPP VIIRS Albedo Over Heterogeneous Crop Land in Northern China. Journal of Geophysical Research: Atmospheres 2017, 122, 13,138 -13,154.
AMA StyleXiaodan Wu, Jianguang Wen, Qing Xiao, Yunyue Yu, DongQin You, Andreas Hueni. Assessment of NPP VIIRS Albedo Over Heterogeneous Crop Land in Northern China. Journal of Geophysical Research: Atmospheres. 2017; 122 (24):13,138-13,154.
Chicago/Turabian StyleXiaodan Wu; Jianguang Wen; Qing Xiao; Yunyue Yu; DongQin You; Andreas Hueni. 2017. "Assessment of NPP VIIRS Albedo Over Heterogeneous Crop Land in Northern China." Journal of Geophysical Research: Atmospheres 122, no. 24: 13,138-13,154.
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.
Xiaodan Wu; Jianguang Wen; Qing Xiao; DongQin You; Qiang Liu; Xingwen Lin. Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface. International Journal of Digital Earth 2017, 11, 470 -484.
AMA StyleXiaodan Wu, Jianguang Wen, Qing Xiao, DongQin You, Qiang Liu, Xingwen Lin. Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface. International Journal of Digital Earth. 2017; 11 (5):470-484.
Chicago/Turabian StyleXiaodan Wu; Jianguang Wen; Qing Xiao; DongQin You; Qiang Liu; Xingwen Lin. 2017. "Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface." International Journal of Digital Earth 11, no. 5: 470-484.
Xingwen Lin; Jianguang Wen; Yong Tang; Mingguo Ma; DongQin You; Baocheng Dou; Xiaodan Wu; Xiaobo Zhu; Qing Xiao; Qinghuo Liu. A web-based land surface remote sensing products validation system (LAPVAS): application to albedo product. International Journal of Digital Earth 2017, 11, 308 -328.
AMA StyleXingwen Lin, Jianguang Wen, Yong Tang, Mingguo Ma, DongQin You, Baocheng Dou, Xiaodan Wu, Xiaobo Zhu, Qing Xiao, Qinghuo Liu. A web-based land surface remote sensing products validation system (LAPVAS): application to albedo product. International Journal of Digital Earth. 2017; 11 (3):308-328.
Chicago/Turabian StyleXingwen Lin; Jianguang Wen; Yong Tang; Mingguo Ma; DongQin You; Baocheng Dou; Xiaodan Wu; Xiaobo Zhu; Qing Xiao; Qinghuo Liu. 2017. "A web-based land surface remote sensing products validation system (LAPVAS): application to albedo product." International Journal of Digital Earth 11, no. 3: 308-328.
Xiaodan Wu; Qing Xiao; Jianguang Wen; Qiang Liu; DongQin You; Baocheng Dou; Yong Tang. Upscaling in situ albedo for validation of coarse scale albedo product over heterogeneous surfaces. International Journal of Digital Earth 2016, 10, 604 -622.
AMA StyleXiaodan Wu, Qing Xiao, Jianguang Wen, Qiang Liu, DongQin You, Baocheng Dou, Yong Tang. Upscaling in situ albedo for validation of coarse scale albedo product over heterogeneous surfaces. International Journal of Digital Earth. 2016; 10 (6):604-622.
Chicago/Turabian StyleXiaodan Wu; Qing Xiao; Jianguang Wen; Qiang Liu; DongQin You; Baocheng Dou; Yong Tang. 2016. "Upscaling in situ albedo for validation of coarse scale albedo product over heterogeneous surfaces." International Journal of Digital Earth 10, no. 6: 604-622.
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.
Baocheng Dou; Jianguang Wen; Xiuhong Li; Qiang Liu; Jingjing Peng; Qing Xiao; Zhigang Zhang; Yong Tang; Xiaodan Wu; Xingwen Lin; DongQin You; Hua Li; Li Li; Yelu Zeng; Erli Cai; Jialin Zhang. Wireless Sensor Network of Typical Land Surface Parameters and Its Preliminary Applications for Coarse-Resolution Remote Sensing Pixel. International Journal of Distributed Sensor Networks 2016, 12, 9639021:1 -9639021:11.
AMA StyleBaocheng Dou, Jianguang Wen, Xiuhong Li, Qiang Liu, Jingjing Peng, Qing Xiao, Zhigang Zhang, Yong Tang, Xiaodan Wu, Xingwen Lin, DongQin You, Hua Li, Li Li, Yelu Zeng, Erli Cai, Jialin Zhang. Wireless Sensor Network of Typical Land Surface Parameters and Its Preliminary Applications for Coarse-Resolution Remote Sensing Pixel. International Journal of Distributed Sensor Networks. 2016; 12 (4):9639021:1-9639021:11.
Chicago/Turabian StyleBaocheng Dou; Jianguang Wen; Xiuhong Li; Qiang Liu; Jingjing Peng; Qing Xiao; Zhigang Zhang; Yong Tang; Xiaodan Wu; Xingwen Lin; DongQin You; Hua Li; Li Li; Yelu Zeng; Erli Cai; Jialin Zhang. 2016. "Wireless Sensor Network of Typical Land Surface Parameters and Its Preliminary Applications for Coarse-Resolution Remote Sensing Pixel." International Journal of Distributed Sensor Networks 12, no. 4: 9639021:1-9639021:11.
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.
Xiaodan Wu; Qing Xiao; Jianguang Wen; Qiang Liu; DongQin You; Baocheng Dou; Yong Tang; Xiaowen Li. Optimal Nodes Selectiveness from WSN to Fit Field Scale Albedo Observation and Validation in Long Time Series in the Foci Experiment Areas, Heihe. Remote Sensing 2015, 7, 14757 -14780.
AMA StyleXiaodan Wu, Qing Xiao, Jianguang Wen, Qiang Liu, DongQin You, Baocheng Dou, Yong Tang, Xiaowen Li. Optimal Nodes Selectiveness from WSN to Fit Field Scale Albedo Observation and Validation in Long Time Series in the Foci Experiment Areas, Heihe. Remote Sensing. 2015; 7 (11):14757-14780.
Chicago/Turabian StyleXiaodan Wu; Qing Xiao; Jianguang Wen; Qiang Liu; DongQin You; Baocheng Dou; Yong Tang; Xiaowen Li. 2015. "Optimal Nodes Selectiveness from WSN to Fit Field Scale Albedo Observation and Validation in Long Time Series in the Foci Experiment Areas, Heihe." Remote Sensing 7, no. 11: 14757-14780.