Next Article in Journal
Remote Sensing for Characterisation and Kinematic Analysis of Large Slope Failures: Debre Sina Landslide, Main Ethiopian Rift Escarpment
Next Article in Special Issue
C3 Vegetation Mapping and CO2 Fertilization Effect in the Arid Lower Heihe River Basin, Northwestern China
Previous Article in Journal
Estimation of the Land Surface Albedo Changes in the Broader Mediterranean Area, Based on 12 Years of Satellite Observations
Previous Article in Special Issue
Estimating Crop Albedo in the Application of a Physical Model Based on the Law of Energy Conservation and Spectral Invariants
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(12), 16164-16182;

Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover

The State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography, Beijing Normal University, Beijing 100875, China
The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, A11, Datun road, Beijing 100101 China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Author to whom correspondence should be addressed.
Academic Editors: Xin Li, Yuei-An Liou, Qinhuo Liu, Randolph H. Wynne and Prasad S. Thenkabail
Received: 22 July 2015 / Revised: 11 November 2015 / Accepted: 19 November 2015 / Published: 2 December 2015
Full-Text   |   PDF [4801 KB, uploaded 7 December 2015]   |  


Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. This paper focuses on the sampling methods used to validate the coarse-resolution fractional vegetation cover (FVC) product in the Heihe River Basin, where the patterns of spatial variations in and between land cover types vary significantly in the different growth stages of vegetation. A sampling method, called the mean of surface with non-homogeneity (MSN) method, and three other sampling methods are examined with real-world data obtained in 2012. A series of 15-m-resolution fractional vegetation cover reference maps were generated using the regressions of field-measured and satellite data. The sampling methods were tested using the 15-m-resolution normalized difference vegetation index (NDVI) and land cover maps over a complete period of vegetation growth. Two scenes were selected to represent the situations in which sampling locations were sparsely and densely distributed. The results show that the FVCs estimated using the MSN method have errors of approximately less than 0.03 in the two selected scenes. The validation accuracy of the sampling methods varies with variations in the stratified non-homogeneity in the different growing stages of the vegetation. The MSN method, which considers both heterogeneity and autocorrelations between strata, is recommended for use in the determination of samplings prior to the design of an experimental campaign. In addition, the slight scaling bias caused by the non-linear relationship between NDVI and FVC samples is discussed. The positive or negative trend of the biases predicted using a Taylor expansion is found to be consistent with that of the real biases. View Full-Text
Keywords: validation; sampling methods; fractional vegetation cover; remote sensing product; scaling bias; spatial autocorrelation. validation; sampling methods; fractional vegetation cover; remote sensing product; scaling bias; spatial autocorrelation.

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Mu, X.; Hu, M.; Song, W.; Ruan, G.; Ge, Y.; Wang, J.; Huang, S.; Yan, G. Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover. Remote Sens. 2015, 7, 16164-16182.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top