MOCA Final Report - Monitoring soil Organic CArbon in croplands using Imaging Spectroscopy (MOCA)

Executive summary

Conventional sampling technique are often too expensive and time consuming to meet the
amount of quantitative 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
study the spatial distribution of soil properties with a high spatial resolution represents an
opportunity for improving the monitoring of soils. However, some challenges still remain to be
solved concerning disturbing factors and the accuracy of the SOC analysis. Disturbing factors,
especially soil roughness and moisture content, must be taken into account to produce good
calibration models. These factors induce a spectral variability not related to the studied property
(here, SOC) and degrade the accuracy of the image-based predictions. The use of hyperspectral
remote sensing as a fast analysis of SOC stocks could lead to a loss of precision, which should
be evaluated because it may be incompatible with the accuracy needed by end-users in the
evaluation of the impact of agricultural practice on SOC stocks.

Until now, imaging spectroscopy has been generally applied over small areas or homogeneous
soil types and surface conditions. During the MOCA project:
· 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.
· The effect of soil Relative Shadow (RS, the percentage of shadowed soil of the surface
studied) on SOC prediction from spectral data under field conditions was quantified. First,
the impact of RS on reflectance and SOCp is briefly described. Then, a methodology to
measure RS and correct its impact on field reflectance measured with an ASD FieldSpec
Pro spectrometer and the AHS-160 sensor is proposed. Finally, SOC content is predicted
with uncorrected and corrected reflectance values to evaluate the enhancement in SOC
prediction accuracy.
· The results of the investigations both in the laboratory (wet chemical SOC analysis
(CONVIS), LECO CN analyzer (calibration and validation dataset) and with remote
sensing via airplane were compared

Reflectance data were related to surface SOC contents of bare croplands by means of 3
different multivariate calibration techniques: Partial Least Square Regression (PLSR), Penalizedspline
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 types 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 predicted for SOC with the best 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.

Tests under laboratory conditions showed that the prediction of SOC decreases when the
relative shadow increases. A methodology for correcting the effect of relative shadow on
reflectance spectra measured with ASD or AHS during field campaign was elaborated and
tested. Results show that the methodology enables to significantly enhance SOC prediction in all
cases studied. Correction always improves the prediction of SOC (and increase of 25 % in
RMSEP for raw reflectance) when using non pre-processed reflectance. The best prediction of
SOC is always achieved with corrected pre-processed reflectance.

From the point of view of an agricultural extension organization in the field of fertilization
planning as well as of maintaining and improving soil fertility such as the CONVIS s.c., the
results presented above have to be considered positive and the investigations of the MOCA
project a successful experiment. The results and the related calibration models appear to be
able to deliver in most cases values of SOC which are precise enough to be used in agricultural
extension. In order to optimize the calibration models of the remote sensing investigation,
traditional chemical analysis of other fields of the investigated air corridor should be made and
the results compared with the SOC values derived from imaging spectroscopy value. This could
deliver more information about the strong points and the limitations of the applied method.