Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products

Abstract

The development of an efficient ground sampling strategy is critical to assess uncertainties associated with moderate-or coarse-resolution remote-sensing products. This work presents a comparison of estimating spatial means from fine spatial resolution images using spatial random sampling (SRS), Block Kriging (BK), and Means of Surface with Nonhomogeneity (MSN) at 1 km 2 spatial scale. Towards this goal, we focus on the sampling strategies for ground data measurements and provide an assessment of the MODIS LAI product validated by the spatial means estimated by the above-mentioned three methods. The results of this study indicate that: (1) for its effective stratification strategies and its criteria of minimum mean square estimation error, MSN demonstrates the lowest mean squared estimation error for estimating the means of stratified nonhomogeneous surface; (2) BK is efficient in estimating the means of homogeneous surfaces without bias and with minimum mean squared estimation errors. The MODIS LAI product is assessed using the means estimated by SRS, BK, and MSN based on Landsat 8 OLI and SPOT HRV fine-resolution LAI maps. For heterogeneous surfaces, MSN results in low RMSE and high accuracy of MODIS LAI product compared with BK and SRS, whereas for homogeneous surfaces, the statistical parameters outputted by these three methods are similar. These results reveal that MSN is an effective method for estimating the spatial means for heterogeneous surfaces. There are differences in the accuracies of MODIS LAI product assessed by these three methods.

Spatial models with covariates improve estimates of peat depth in blanket peatlands

“…When carrying out spatial interpolation, our results support previous studies who have shown that sampling design can have a considerable impact on the quality and validity of the interpolated mapping produced, e.g. [ 60 ]. The choice of sampling strategies is governed and affected by several factors, including the interpolation approach being used, the human resources available, distance between measurements, and site remoteness and accessibility [ 22 ].…”

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

“…The uncertainties in the high‐resolution reference data should ideally be smaller than those in the LAI products (Widlowski, ). In general, both field measurement and transfer function uncertainties need to be considered to improve the reference LAI accuracy (Ding et al, ; R. A. Fernandes et al, ; Garrigues, Lacaze, et al, ; A. H. Li et al, ). Prior to validation, it is important to examine the vegetation distribution within the pixel to check whether the field data are representative of the larger pixel (Fang, Wei, & Liang, ; Nikolov & Zeller, ).…”

“…However, this method has been mostly used in low LAI areas because of the easy saturation of VI and reflectance in high LAI areas. As an alternative, geostatistical techniques have been effective in identifying spatially representative areas and mitigating the spatial mismatch between satellite pixels and reference data (Ding et al, ; Martinez et al, , ). Using sampling schemes adapted to the spatial variability of the LAI (e.g., Validation of Land European Remote sensing Instruments) and by sampling sufficient numbers (>100) of ground measurements, the problem of scale differences in generating the reference data can be partly overcome (Nackaerts et al, ; Richter, Atzberger, et al, ).…”

Evaluation and Intercomparison of MODIS and GEOV1 Global Leaf Area Index Products over Four Sites in North China

“…For the Jingyuetan site, an empirical regression approach was also employed using 79 field-measured LAIs and two Landsat8 OLI images to retrieve the LAI maps. An enhanced vegetation index (EVI) was also used [ 40 ].…”