Improving soil organic carbon predictions from a Sentinel–2 soil composite by assessing surface conditions and uncertainties

Soil organic carbon (SOC) prediction from remote sensing is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non–photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building reflectance composites using time series of images. Although composite imagery has demonstrated its potential in SOC prediction, it is still not well established if the resulting composite spectra mirror the reflectance fingerprint of the optimal conditions to predict topsoil properties (i.e. a smooth, dry and bare soil). We have collected 303 photos of soil surfaces in the Belgian loam belt where five main classes of surface conditions were distinguished: smooth seeded soils, soil crusts, partial cover by a growing crop, moist soils and crop residue cover. Reflectance spectra were then extracted from the Sentinel–2 images coinciding with the date of the photos. After the growing crop was removed by an NDVI < 0.25, the Normalized Burn Ratio (NBR2) was calculated to characterize the soil surface, and a threshold of NBR2 < 0.05 was found to be able to separate dry bare soils from soils in unfavorable conditions i.e. wet soils and soils covered by crop residues. Additionally, we found that normalizing the spectra (i.e. dividing the reflectance of each band by the mean reflectance of all spectral bands) allows for cancelling the albedo shift between soil crusts and smooth soils in seed–bed conditions. We then built the exposed soil composite from Sentinel–2 imagery for southern Belgium and part of Noord-Holland and Flevoland in the Netherlands (covering the spring periods of 2016–2021). We used the mean spectra per pixel to predict SOC content by means of a Partial Least Squares Regression Model (PLSR) with 10–fold cross–validation. The uncertainty of the models was assessed via the prediction interval ratio (PIR). The cross validation of the model gave satisfactory results (mean of 100 bootstraps: model efficiency coefficient (MEC) = 0.48 ± 0.07, RMSE = 3.5 ± 0.3 g C kg–1, RPD = 1.4 ± 0.1 and RPIQ = 1.9 ± 0.3). The resulting SOC prediction maps show that the uncertainty of prediction decreases when the number of scenes per pixel increases, and reaches a minimum when at least six scenes per pixel are used (mean PIR of all pixels is 12.4 g C kg–1, while mean SOC predicted is 14.1 g C kg–1). The results of a validation against an independent data set showed a median difference of 0.5 g C kg–1 ± 2.8 g C kg–1 SOC between the measured (average SOC content 13.5 g C kg–1) and predicted SOC contents at field scale. Overall, this compositing method shows both realistic within field and regional SOC patterns

Improving SOC predictions from Sentinel-2 soil composites by assessing surface conditions and uncertainties

SOC prediction from remote sensing is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non-photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building reflectance composites using time series of images. Even if SOC predictions from composite images are promising, it is still not well established if the resulting composite spectra mirror the reflectance fingerprint of the optimal conditions to predict topsoil properties (i.e. a smooth, dry and bare soil). We have collected 303 photos of soil surfaces in the Belgium loam belt where five main classes of surface conditions were distinguished: smooth seeded soils, soil crusts, vegetation, moist soils and soils covered by crop residues. Reflectance spectra were then extracted from the Sentinel-2 images coinciding with the date of the photos. The Normalized Burn Ratio (NBR2) was calculated to characterize the soil surface, and a threshold of NBR2 < 0.05 was found to be able to separate wet soils and soils covered by crop residues from dry bare soils. Additionally, we found that normalizing the spectra (i.e. dividing the reflectance of each band by the mean reflectance of all spectral bands) allows for cancelling the albedo shift between soil crusts and smooth soils in seed-bed conditions. We then built the exposed soil composite from Sentinel-2 imagery (covering the spring periods of 2016-2021), and used the reflectance information to predict SOC content by means of a Partial Least Square Regression Model (PLSR) with 10-fold cross-validation. The uncertainty of the models (expressed as q0.05+q0.95/q0.50) was assessed via bootstrapping, where each model was repeated 100 times with a slightly different calibration dataset. The cross validation of the model gave satisfactory results (R² = 0.49 ± 0.10, RMSE = 3.4 ± 0.6 g C kg-1 and RPD = 1.4 ± 0.2). The resulting SOC prediction maps show that (1) the uncertainty of prediction decreases when the number of scenes per pixel increases, and reaches a minimum when more than six scenes per pixel are used (median uncertainty of all pixels is 28% of predicted SOC value) and (2) the uncertainty of prediction diminishes if SOC predictions are aggregated per field (median uncertainty of fields is 22% of predicted value). The results of a validation against an independent data set showed a median difference of 0.5 g C kg-1 ± 2.8 g C kg-1 SOC between the measured and predicted SOC contents at field scale. Overall, this compositing method shows both realistic SOC patterns at the field scale and regional patterns corresponding to the ones reported in the literature

Practical Implications of the Availability of Multiple Measurements to Classify Agricultural Soil Compaction : A Case-Study in The Netherlands

Soil compaction is a severe threat to agricultural productivity, as it can lead to yield losses ranging from 5% to 40%. Quantification of the state of compaction can help farmers and land managers to determine the optimal management to avoid these losses. Bulk density is often used as an indicator for compaction. It is a costly and time-consuming measurement, making it less suitable for farmers and land managers. Alternatively, measurements of penetration resistance can be used. These measurements are cheaper and quicker but are prone to uncertainty due to the existence of a wide array of thresholds. Classifications using either measurement may provide different outcomes when used in the same location, as they approximate soil compaction using different mechanisms. In this research, we assessed the level of agreement between soil compaction classifications using bulk density and penetration resistance for an agricultural field in Flevoland, the Netherlands. Additionally, we assessed the possible financial implications of misclassification. Balanced accuracy results indicate that most thresholds from the literature show around 70% agreement between both methods, with a maximum level of agreement of 76% at 1.8 and 1.9 MPa. The expected cost of misclassification shows a dip between 1.0 and 3.0 MPa, with an effect of crop value on the shape of the cost function. Although these results are specific to our study area, we believe they show that there is a substantial effect of the choice of measurement on the outcome of soil compaction studies