Large-Scale Mapping of Soil Quality Index in Different Land Uses Using Airborne Hyperspectral Data

Large-scale mapping of soil quality index (SQI) is a challenging task because of the cost and time involved in measuring required soil parameters through conventional wet chemistry-based methods. Hyperspectral remote sensing (HRS) may be used to overcome such a challenge. We hypothesize that soil quality at a specific location may be estimated from remotely sensed reflectance spectra because both these attributes are composite parameters. We used the HRS data collected with the Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor to estimate SQIs in an agricultural catchment. SQIs were developed from 16 different soil properties measured at 101 locations coinciding AVIRIS-NG flight. Chemometric models were used to estimate SQIs from spectral reflectance data collected under laboratory conditions and those processed from AVIRIS-NG data before and after linear and nonlinear unmixing. Except for the linearly unmixed AVIRIS-NG data, three other spectral data sources yielded coefficient of determination (
$R^{2}$
) values exceeding 0.7. Specifically, the
$R^{2}$
values for the mixed and nonlinearly unmixed spectra were 0.71 and 0.72, respectively, suggesting that HRS approach may directly be used for estimating SQIs. With high validation statistics, we converted the AVIRIS-NG imagery to SQI map for the entire catchment. Such high spatial resolution maps allowed us to examine the effects of land use/cover on soil quality. Strong linear dependencies between SQI and land uses and terrain structures suggested that HRS-derived SQI maps may be used to prioritize soil management efforts for sustainable development.