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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.

Intra-field heterogeneity of soil properties, such as soil organic carbon (SOC), nitrogen (N), phosphorous (P), exchangeable cations, pH, or soil texture, is a function of complex interactions between biological factors, physical factors, and historic agricultural management. Mapping the crop growth and final yield heterogeneity and quantifying their link with soil properties can contribute to an optimization of amendment/fertilizer application and crop yield in a management variable zones (MVZ) approach. To this end, we studied a field of 17 ha consisting of four former fields that were merged in early 2017 and cropped with winter wheat in 2018. Historical management practices data were collected. The topsoil characteristics were analyzed by grid-based sampling and kriged to create maps. We tested the capacity of a multispectral MicaSense® RedEdge-MTM camera sensor embedded on an unmanned aerial vehicle (UAV) to map in-season growth of winter wheat. Relating several vegetation indices (VIs) to the plant area index (PAI) measured in the field highlighted the red-edge NDVI (RENDVI) as the most suitable to follow the crop growth throughout the growing season. The georeferenced final grain yield of the winter wheat was measured by a combine harvester. The spatial patterns in RENDVI at three phenological stages were mapped and analyzed together with the yield map. For each of these images a conditional inference forest (CI-forest) algorithm was used to identify the soil properties significantly influencing these spatial patterns. Historical management practices of the four former fields have induced significant heterogeneity in soil properties and crop growth. The spatial patterns of RENDVI are rather constant over time and their Spearman rank correlation with yield is similar along the growing season (r ≃ 0.7). Soil properties explain between 87% (mid-March) to 78% (mid-May) of the variance in RENDVI throughout the growing season, as well as 66% of the variance in yield. The pH and exchangeable K are the most significant factors explaining from 15 to 26% of the variance in crop growth. The methodology proposed in this paper to quantify the importance of soil parameters based on the CI-forest algorithm can contribute to a better management of amendment/fertilizer inputs by stressing the most important parameters to take into consideration for site-specific management. We also showed that heterogeneity induced by the soil properties can be described by a crop map early in the season and that this crop map can be used to optimize soil sampling and thus amendment/fertilizer management.

Global efforts to restore the world’s degraded croplands require knowledge on the degree and extent of accelerated soil organic carbon (SOC) loss induced by soil erosion. However, the methods for assessing where and to what extent erosion takes place are still inadequate for precise detection of erosion hotspots at high spatial resolution. Drawing on recent advances in multitemporal Sentinel-2 remote sensing to create a bare soil composite that reflects erosion-induced variations in soil spectral signatures, this study attempted to develop a spectra-based soil erosion mapping approach to pinpoint eroded hotspots in a typical catchment located in the black soil region of northeast China as characterized by undulating landscapes. We built a ground-truth dataset consisting of three classes of soils representing Severe, Moderate and Low erosion intensity because of their inter-class contrasts in estimated erosion rates from 137Cs tracing. The spectral separability of different erosion classes was first tested by a combined principal component and linear discriminant analysis (PCA-LDA) against laboratory hyperspectral data and then validated against Sentinel-2-derived broadband spectra. The results show that PCA-LDA produced excellent classification accuracy (Kappa coefficient > 0.9) for both data sources and even more so for Sentinel-2 spectra, highlighting the effectiveness of the multitemporal approach to extract bare soil pixels. Further investigations into the spectral curves enabled identification of distinctive spectral features representative of shifting soil albedo and biochemical composition due to erosion-induced SOC mobilization. A classification scheme comprising the spectral features was applied to the Sentinel-2 bare soil composite for pixel-wise soil erosion mapping, in which 15.9% of the cropland area was detected as erosion hotspots, while the Moderate class occupied 65.4%. Comparing the erosion map to a NDVI map demonstrated the negative impact of soil erosion on crop growth from a spatial perspective, highlighting the potential of the proposed approach to aid targeted cropland management for food security and climate.

Pilot studies have demonstrated the potential of remote sensing for soil organic carbon (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction 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 image composites. These composites tend to minimize the disturbing effects by applying sets of criteria. Here, we aim to develop a robust method that allows selecting Sentinel-2 (S-2) pixels with minimal influence of the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Belgian Loam Belt from January 2019 to December 2020 (in total 36 images). We then built nine exposed soil composites based on four sets of criteria: (1) lowest Normalized Burn Ratio (NBR2), (2) Normalized Difference Vegetation Index (NDVI) < 0.25, (3–5) NDVI < 0.25 and NBR2 < threshold, (6) the ‘greening-up’ period of a crop and (7–9) the ‘greening-up’ period of a crop and NBR2 < threshold. The ‘greening-up’ period was selected based on the NDVI timeline, where ‘greening-up’ is considered as the last date of acquisition where the soil is exposed (NDVI < 0.25) before the crop develops (NDVI > 0.25). We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the nine composites. We obtained non-satisfactory results (R2 < 0.30, RMSE > 2.50 g C kg–1, and RPD < 1.4, n > 68) for all composites except for the composite in the ‘greening-up’ stage with a NBR2 < 0.07 (R2 = 0.54 ± 0.12, RPD = 1.68 ± 0.45 and RMSE = 2.09 ± 0.39 g C kg–1, n = 49). Hence, the ‘greening-up’ method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be its coverage of the total cropland area, which in a two-year period reached 62%, compared to 95% coverage if only the NDVI threshold is applied.

Optical remote sensing and in particular hyperspectral or imaging spectroscopy remote sensing has been long proved to be an adequate method to predict topsoil organic carbon (Corg) content with good accuracy when the soils are well exposed and undisturbed. Several recent studies demonstrated further in science cases the potential of multispectral Copernicus Sentinel-2 data for bare soils Corg prediction, although challenges were reported related to the impact of disturbing factors. Disturbing factors that can affect the prediction and performances of soil surface properties from optical remote sensing are several and can be e.g. due to mixing in the field-of-view with partial vegetation cover depending on the landscape fragmentation. Most pixels at the remote sensing level are composites and in croplands, mixtures of soils with trees or green plants, or mixture with crop residues after harvest are likely. Another factor might be the presence of residual soil moisture or standing water after rain events. Soil reflectance decreases with increasing soil moisture and increasing soil roughness. Soil Surface roughness changes are observed due to variations in soil texture and to variable microtopography. Possible angular and solar illumination changes may affect the soil reflectance as well.In the frame of the ESA WORLDSOILS Project (https://www.world-soils.com) aiming at developing a pre-operational Soil Monitoring System to provide yearly estimations of soil organic carbon at global scale based on space-based EO data, we are working on the development of a spatially upscaled soil spectral library (SUSSL). The SUSSL is based on a sub-selection of the European LUCAS soil database, and includes simulation of realistic scenarios of ‘landscape-like’ cropland reflectance data with effect of mixture with green and dry vegetation, effect of varying soil moisture content, and effect of variable soil roughness. This database is further convoluted to the different spectral response functions of several EO sensors to simulate EO view of surface reflectances in croplands. In a next step, the SUSSL shall be used for the test and validation of different correction, disaggregation and unmixing techniques to assess the capabilities of the retrieval of undisturbed surface reflectance, to which soil prediction models can be applied with increased accuracy. In this talk, we will present the database developed, including methodological choices and parameter selections for the simulation of the different disturbing effects. Further, preliminary assessments will be shown on the uncertainties of the undisturbed vs. disturbed signal and impact on soil properties prediction.

Pilot studies have demonstrated the potential for remote sensing techniques for soil organic carbon (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface such as photosynthetic active and nonphotosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, many studies have focused on stabilizing the soil reflectance by building image composites that are generated using a set of criteria. These composites tend to minimize and cancel out the disturbing effects. Here we aim to develop a robust method that allows selecting Sentinel-2 (S-2) pixels that are not affected by the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Loam Belt of Belgium from January 2019 to December 2020 (in total 38 images). We then built four exposed soil composites based on four sets of criteria: (1) NDVI < 0.25, (2) NDVI < 0.25 & Normalized Burn Ratio (NBR2) < 0.07, (3) the ‘greening-up’ period of a crop and (4) the ‘greening-up’ period of a crop & NBR2 < 0.07. The ‘greening-up’ period was selected based on the NDVI timeline, where ‘greening-up’ is considered as the last date of acquisition where the soil is bare (NDVI < 0.25) before the crop develops (NDVI > 0.6).,We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the four above described composites. We obtained a non-satisfactory result for composites (1) to (3): R² = 0.22, RMSE = 3.46 g C kg-1 and RPD 1.12 for (1), R² = 0.19, RMSE = 3.43 g C kg-1 and RPD 1.10 for (2) and R² = 0.15, RMSE = 2.74 g C kg-1 and RPD 1.06 for (3). We, however, obtained a satisfactory result for composite (4): R² = 0.54, RMSE = 2.09 g C kg-1 and RPD 1.68. Hence, the ‘greening-up’ method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be the surface coverage, which for a two-year period reached 47% of croplands, compared to 89% exposure if only the NDVI threshold is applied.

Intra-field heterogeneity of soil properties is function of complex interaction between biological, physical factors and historic agricultural management. Quantifying the spatial patterns of soil properties such as soil organic carbon (SOC), nitrogen (N), phosphorous, exchangeable cations, pH, soil texture will contribute to an optimization of fertilizer application and crop yields. We tested the capacity of the multispectral Micasense rededge camera mounted on a UAV in order to map the development of winter wheat and related the Red-Edge NDVI (RENDVI) from the sensor to the Plant Area Index (PAI) measured in the field. The geo-referenced grain yield of the winter wheat was measured by a combine harvester and the top soil characteristics analysed by a grid based sampling. The spatial patterns in RENDVI at three phenological stages were mapped together with the yields. For each of these images conditional inference trees were used to derive the soil properties that significantly influenced these spatial patterns. Within-field variation in PAI (cv 41 % in March, 27% in April and 9 % in May) and yield (cv 4%) can be observed. The spatial patterns of RENDVI are rather constant and their correlation with yields is highest in March and April (r=0.64). Soil properties explain between 67 to 79 % of the variance in vegetation index throughout the growing season as well as 67 % of the variance in yield. Legacy effects of land consolidation two years earlier reflected in the field lay-out, pH and exchangeable K are significant factors explaining around 12-18 % of the variance in crop yield each. The SOC contents were overall low (8-15 g kg-1). Hence, the N supply resulting from SOC mineralization throughout the growing season covered less than 10% of the crop needs and the yield patterns did not reflect the variation in SOC contents.

Since the onset of agriculture, soils have lost their organic carbon to such an extent that the soil functions of many croplands are threatened. Hence, there is a strong demand for mapping and monitoring critical soil properties and in particular soil organic carbon (SOC). Pilot studies have demonstrated the potential for remote sensing techniques for SOC mapping in croplands. It has, however, been shown that the assessment of SOC may be hampered by the condition of the soil surface. While growing vegetation can be readily detected by means of the well-known Normalized Difference Vegetation Index (NDVI), the distinction between bare soil and crop residues is expressed in the shortwave infrared region (SWIR), which is only covered by two broad bands in Landsat or Sentinel-2 imagery. Here we tested the effect of thresholds for the Cellulose Absorption Index (CAI), on the performance of SOC prediction models for cropland soils. Airborne Prism Experiment (APEX) hyperspectral images covering an area of 240 km2 in the Belgian Loam Belt were used together with a local soil dataset. We used the partial least square regression (PLSR) model to estimate the SOC content based on 104 georeferenced calibration samples (NDVI < 0.26), firstly without setting a CAI threshold, and obtained a satisfactory result (coefficient of determination (R2)= 0.49, Ratio of Performance to Deviation (RPD) = 1.4 and Root Mean Square Error (RMSE) = 2.13 g kgC−1 for cross-validation). However, a cross comparison of the estimated SOC values to grid-based measurements of SOC content within three fields revealed a systematic overestimation for fields with high residue cover. We then tested different CAI thresholds in order to mask pixels with high residue cover. The best model was obtained for a CAI threshold of 0.75 (R2 = 0.59, RPD = 1.5 and RMSE = 1.75 g kgC−1 for cross-validation). These results reveal that the purity of the pixels needs to be assessed aforehand in order to produce reliable SOC maps. The Normalized Burn Ratio (NBR2) index based on the SWIR bands of the MSI Sentinel 2 sensor extracted from images collected nine days before the APEX flight campaign correlates well with the CAI index of the APEX imagery. However, the NBR2 index calculated from Sentinel 2 images under moist conditions is poorly correlated with residue cover. This can be explained by the sensitivity of the NBR2 index to both soil moisture and residues.

During the surface mount technological process the assembled PCB is exposed to heat when molten solder alloy wets the surfaces and creates future solder joints. The PCB with all components should ideally be exposed to the same temperature profile during the soldering process. The temperatures differ throughout the PCB due to different thermal capacities of used materials and components even though the temperature profile is stable. The aim of the study was to assess the solderability of the copper material with different thermal capacity and to consider the effect of different types of fluxes. The measurement was done by wetting balance method, where the wetting force is measured as a function of time during which the test specimen is immersed into, remains immersed and then it is pulled out from the solder bath. In our experiments we used copper wire of four different diameters (0.7 mm, 1 mm, 1.5 mm), three types of solders - one lead (Sn63Pb37) and two lead free (Sn99Cu1, Sn97Cu3) solders and three types of fluxes.