Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1080/17538947.2016.1247300 | INTERNATIONAL JOURNAL OF DIGITAL EARTH |
Keywords | Field | DocType |
Upscaling,albedo,heterogeneous,validation,in situ,multi-scale | Kriging,In situ,Satellite,Remote sensing,Albedo,Pixel,Geography,Image resolution,Calibration | Journal |
Volume | Issue | ISSN |
10.0 | 6.0 | 1753-8947 |
Citations | PageRank | References |
1 | 0.40 | 16 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaodan Wu | 1 | 4 | 1.14 |
| Qing Xiao | 2 | 52 | 11.09 |
| Jianguang Wen | 3 | 67 | 21.30 |
| Qiang Liu | 4 | 5 | 1.21 |
| Dongqin You | 5 | 38 | 10.25 |
| Baocheng Dou | 6 | 36 | 6.18 |
| yong tang | 7 | 31 | 5.70 |