Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database
| Autor: | Sabine Chabrillat, Bas van Wesemael, Kristin Vreys, Bart Bomans, Arwyn Jones, Fabio Castaldi |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
soil variables
010504 meteorology & atmospheric sciences Science computer.software_genre 01 natural sciences spectral library Partial least squares regression SOC APEX 0105 earth and related environmental sciences Topsoil Data collection Database Soil organic matter LUCAS airborne Soil chemistry Hyperspectral imaging 04 agricultural and veterinary sciences Soil carbon hyperspectral croplands Loam 040103 agronomy & agriculture 0401 agriculture forestry and fisheries General Earth and Planetary Sciences Environmental science computer |
| Zdroj: | Remote Sensing Remote Sensing; Volume 10; Issue 2; Pages: 153 Remote Sensing, Vol 10, Iss 2, p 153 (2018) |
| Popis: | The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland–Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model’s accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5–4.9 g·kg−1; ratio of performance to deviation (RPD) = 1.4–1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within- and between-field SOC variability. |
| Databáze: | OpenAIRE |
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