Topsoil organic carbon prediction using vis-nir-swir reflectance spectra at lab, field and satellite levels over a periurban region
Résumé
Within the framework of the French Gessol3 Programme (Prostock project), this study aims at
comparing various observation scales for predicting topsoil organic carbon (SOC) content using
Vis
-
NIR
-
SWIR reflectance spectra successively collected at the lab, i
n bare agricultural fields or
extracted from atmospherically corrected multispectral SPOT images of very high (2.5 m) and
medium low (20 m) spatial resolutions. The spatial coverage is that of a large periurban area (221
km²) characterized by cereal croppi
ng systems and contrasting soil types. Considering either
regional (entire periurban area) or local (a 6 ha
-
experimental field) scales, a series of 500
-
1000
bootstrapped datasets of calibration/validation samples were generated amongst a total of 165
sampl
ed sites and used to predict SOC contents. At the regional scale, Partial Least Squares
Regression (PLSR) lab and field
-
based SOC models resulted in median validation Root Mean
Square Errors (RMSE) values of ~3 g.kg
-
1 and ~4 g.kg
-
1 respectively (=0.95 g.kg
-
1 locally for lab
-
based models), while multiple linear (ML) image
-
based SOC models resulted in median validation
RMSE values between ~4
-
6.6 g.kg
-
1. Using an additional independent set of pixels with bare soils,
ML models applied to the SPOT images were ‘p
ost
-
validated’ resulting in validation RMSE values
of ~4
-
5 g.kg
-
1 at the regional scale and ~3 g.kg
-
1 locally. Image
-
based models thus resulted in
acceptable validation errors, in possible agreement with the need to spatially monitor SOC
contents of region
al territories. However, having higher validation bias and error uncertainty than
lab or field
-
based models, they should be considered with caution.