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Proceedings Paper

Comparison of different spatial sampling methods for validation of GEOV1 FVC product over heterogeneous and homogeneous surfaces
Author(s): Yanling Ding; Yong Ge; Maogui Hu; Hongyan Zhang
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Paper Abstract

The development of an efficient ground sampling strategy which can sample the natural dynamics of variations in variables of interest, is critical to ensuring the validation of remotely sensed products. This study attempts to take a fresh look at geostatistical methods for ground sampling and pixel-mean estimating in remote sensing validation campaigns. Spatial random sampling (SRS), Block Kriging (BK), and Means of Surface with Non-homogeneity (MSN) were implemented to estimate the fractional vegetation cover mean values at GEVO1 1 km2 pixel level using Landsat 8 OLI and SPOT4 HRVIR1 fine-resolution FVC maps respectively derived from a homogeneous area covered by forest and a heterogeneous area covered by crop. The GEOV1 FVC product was validated using the means estimated by SRS, BK, and MSN. Root square error (RMSE), mean absolute percentage error (MAPE) and product accuracy (PA) were used to evaluate the validation. Results showed that the MSN method performs well for estimating the means of the surface with non-homogeneity, with a high accuracy of the GEOV1 FVC product (RMSE=0.12, MAPE=29.37 and PA= 77.39%). The statistical values outputted by BK were respectively 0.13, 31.46% and 76.21%. These values of SRS were respectively 0.13, 31.16% and 76.10%. For homogeneous surface, the statistical parameters outputted by these three methods were similar. These results revealed that MSN is an effective method for estimating the spatial means for heterogeneous surface and validating remote sensing product. We can conclude that choosing an appropriate sampling method has a significant impact on the validation of remote sensing product.

Paper Details

Date Published: 25 October 2016
PDF: 10 pages
Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 99980I (25 October 2016); doi: 10.1117/12.2241618
Show Author Affiliations
Yanling Ding, Northeast Normal Univ. (China)
Yong Ge, Institute of Geographic Sciences and Natural Resources Research (China)
Maogui Hu, Institute of Geographic Sciences and Natural Resources Research (China)
Hongyan Zhang, Northeast Normal Univ. (China)

Published in SPIE Proceedings Vol. 9998:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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