Monitoring Soil Organic Carbon in Croplands using Imaging Spectroscopy - MOCA

Coordinating Institution: CRP Gabriel Lippmann
Other Partner(s): University of Liège , University of Louvain (B)
From: 01/12/2006
To: 31/12/2008
Budget: 22,174.00€
Contact(s): Udelhoven Thomas

Summary

Conventional sampling techniques are often too expensive and time consuming to meet the amount of data required in soil monitoring or modelling studies. The emergence of portable and flexible VNIR sensors could provide the large amount of spatial data needed. In particular, the ability of imaging spectroscopy to cover large surfaces in a single campaign and to study the spatial distribution of soil properties with a high spatial resolution represents an opportunity for improving the monitoring of soil characteristics and soil threats such as the decline of soil organic matter in the topsoil. However, imaging spectroscopy has been generally applied over small areas with homogeneous soil types and surface conditions.

Here, five hyperspectral images acquired with the AHS-160 sensor were analysed to predict soil organic carbon (SOC) in an area in Luxembourg characterized by different soil types and a large variation in SOC contents. Reflectance data were related to surface SOC contents of bare croplands by means of 3 different multivariate calibration techniques: partial least square regression (PLSR), penalized-spline signal regression (PSR) and least square support vector machine (LS-SVM). The performance of the methods was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to agro-geological zones, soil type and image number). The results demonstrated that PSR and LS-SVM performed better than PLSR using global calibrations. The Root Mean Square Error in the Predictions reached 5.6-6.2 g C kg-1. Under local calibrations, this error was reduced by a factor 1.3 to 1.9, depending on the stratification scheme adopted. Pixels of two agricultural fields were extracted from the data cube and the SOC was predicted using the best performing models. Intra- and inter-field variability of SOC contents were observed related to topography and land management. In the future, the mapping of SOC over the entire study area will constitute a database used as input in digital soil mapping and SOC monitoring.

Programme:

• STEREO II, Recherche en Observation de la Terre

Other Funding Agency:

• Service Fédéral Public de Programmation Scientifique (Belgium)

Refereed Scientific Publications:

• Stevens, A., Udelhoven, T., Denis, A., Tychon, B., Lioy, R, Hoffmann. L. and van Wesemael, B. (2008): Monitoring soil organic carbon in croplands at regional scale using imaging spectroscopy, Geoderma, submitted.

Figure 1: Results of the organics mapping in top soils. The organic carbon content has been estimated from airborne hyperspectral remote sensing date.

Figure 2: Results of the organic carbon mapping for one plot only. The markers in the image denote locations where reference soil samples have been taken in the fields that were used to train the models.

Figure 3: Top soils