Abstract
Predictive modeling with remotely sensed data requires an accurate representation of spatial variability by ground truth data. In this study, we assessed the reliability of the size and location of ground truth data in capturing the landscape spatial variability embedded in the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral image in an agricultural region in Anand, India. We derived simulated spectral vegetation and soil indices using Gaussian simulation from AVIRIS-NG image for two point-location datasets, (1) ground truth points from adaptive sampling and (2) points from conditional Latin Hypercube Sampling (cLHS). We compared values of the simulated image indices against the actual image indices (measured) through the analysis of mean absolute errors. Modeling the variogram of the measured indices with the hyperspectral image in high spatial resolution (4m), is an effective way to characterize the spatial heterogeneity at the landscape level. We used geostatistical techniques to analyze the shapes of experimental variograms in order to assess whether or not the ground truth points, when compared against the cLHS-derived points, captured the spatial structures and variability of the studied agricultural area using measured indices. In addition, we explored the capability of the variogram by running tests in different point sample sizes. The ground truth and cLHS datasets were able to derive equivalent values for field spatial variability from image indices, according to our findings. Furthermore, this research presents a methodology for selecting spectral indices and determining the best sample size for efficiently replicating spatial patterns in hyperspectral images.
Authors: Eric Ariel L. Salas, Sakthi Kumaran Subburayalu, Brian Slater, Rucha Dave, Parshva Parekh, Kaiguang Zhao, Bimal Bhattacharya
Citation: Salas EAL, Subburayalu SK, Slater B, Dave R, Parekh P, Zhao K, Bhattacharya B. 2021. Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques, Heliyon, 7(7), e07439, ISSN 2405-8440. https://doi.org/10.1016/j.heliyon.2021.e07439
cLHS R-code: Conditional Latin Hypercube Sampling (cLHS) R-Code to Sample Points From Raster Images
Keywords: Conditioned Latin Hypercube Sampling; Sample optimization; Adaptive soil sampling; Field data collection; cLHS algorithm
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