Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques

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.

Challenges and Opportunities in Remote Sensing for Soil Salinization Mapping and Monitoring: A Review

“…Validation based on ground truth data can improve salinization products derived from remote sensing, but obtaining reliable field data can be complex due to factors such as the heterogeneity of soil properties and salinity distribution, the lack of uniformity in the methods used to collect and analyze soil samples, and the inaccessibility of some locations [164]. Standardized methods for collecting and analyzing field data are essential to account for soil heterogeneity and salinity distribution across various regions.…”

Statistical considerations of using the 1-ft2 quadrat for monitoring peak standing crop and residual dry matter on California annual rangelands

“…4- 5 Collecting on-the-ground data is necessary to validate and calibrate detection algorithms. 6,7 Also, trend monitoring for environmental assessments, such as desertification, erosion control, and carbon sequestration, with respect to climate change and land-use change, requires accurate biomass measurements at the regional scale. Furthermore, the United States Department of Agriculture (USDA), Farm Service Agency rangeland drought disaster program requires an accurate assessment of drought-year forage production for comparison to long-term average production rates.…”