Effects of rock fragments on physical degradation of cultivated soils by rainfall

To understand better the role of rock fragments in soil and water conservation processes, the effects of rock fragments in maintaining a favourable soil structure and thus also in preventing physical degradation of tilled soils was studied. Laboratory experiments were conducted to investigate the effects of rock fragment content, rock fragment size, initial soil moisture content of the fine earth and surface rock fragment cover on soil subsidence by rainfall (i.e. change in bulk density by one or more cycles of wetting and drying). A total of 15 rainfall simulations (cumulative rainfall, 192.5 mm; mean intensity, 70 mm h−1) were carried out. Before and after each rainfall application the surface elevation of a 19-cm thick plough layer was measured with a laser microrelief meter. In all experiments, the bulk density of the fine earth increased with applied rainfall volume to reach a maximum value at about 200 mm of cumulative rainfall. From the experimental results it was concluded that the subsidence rate decreased sharply for soils containing more than 0.50 kg kg−1 rock fragments, irrespective of rock fragment size. Fine earth bulk densities were negatively related to rock fragment content beyond a threshold value of 0.30 kg kg−1 for small rock fragments (1.7–2.7 cm) and 0.50 kg kg−1 for large rock fragments (7.7 cm). Initial soil moisture content influenced subsidence only in the initial stage of the experiments, when some swelling occurred in the dry soils. Surface rock fragment cover had no significant effect on subsidence of the plough layer. Therefore, subsidence of the plough layer in these experiments appears to be mainly due to changing soil strength upon drainage rather than the result of direct transfer of kinetic energy from falling drops. The relative increase in porosity of the fine earth as well as the absolute increase in macroporosity with rock fragment content will cause deeper penetration of rainfall into the soil, resulting in water conservation. Therefore, crushing of large rock fragments into smaller ones is to be preferred over removal of rock fragments from the plough layer

Determining RUSLE P-factors for stonebunds and trenches in rangeland and cropland, Northern Ethiopia

The implementation of soil and water conservation (SWC) measures in the Ethiopian highlands is a top priority to reduce soil erosion rates and to enhance the sustainability of agroecosystem. Nonetheless, the effectiveness of many of these measures for different hillslope and land use conditions remains currently poorly understood. As a result, the overall effects of these measures at regional or catchment scale remain hard to quantify. This study addresses this knowledge gap by determining the cover-management (C) and support practice (P) factors of the Revised Universal Soil Loss Equation (RUSLE), for commonly used SWC measures in semi-arid environments (i.e. stone bunds, trenches and a combination of both). Calculations were based on soil loss data collected with runoff plots in Tigray, northern Ethiopia (i.e. 21 runoff plots of 600 to 1000 m2 , monitored during 2010, 2011 and 2012). The runoff plots were installed in rangeland and cropland sites corresponding to a gentle (5%), medium (12%) and steep (16%) slope gradients. The C and P factors of the RUSLE were calculated following the recommended standard procedures. Results show that the C-factor for rangeland ranges from 0.31 to 0.98 and from 0.06 to 0.39 for cropland. For rangeland, this large variability is due to variations in vegetation cover caused by grazing. In cropland, C-factors vary with tillage practices and crop types. The calculated P-factors ranged from 0.32 to 0.74 for stone bunds, from 0.07 to 0.65 for trenches and from 0.03 to 0.22 for a combination of both stone bunds and trenches. This variability is partly due to variations in the density of the implemented measures in relation to land use (cropland vs rangeland) and slope angles. However, also annual variations in P factor values are highly significant. Especially trenches showed a very significant decline of effectiveness over time, which is attributable to their reduced static storage capacity as a result of sediment deposition (e.g. for trenches in rangeland: 0.07-0.13 in 2010 to 0.37-0.65 in 2012). Hence, the results of this work may not only help in better modelling and quantifying the average long-term impacts of SWC measures over larger areas, but also show the importance of considering temporal variations of the effectiveness of SWC measures

Remote Sensing for Soil Organic Carbon Mapping and Monitoring

[no abstract available

Characterising rainfall regimes in relation to recharge of the Sierra de Gador-Campo de Dalias aquifer system (S-E Spain)

This paper demonstrates the use of the deuterium excess parameter to distinguish between origin of precipitation in the Mediterranean basin. Isotope signatures in precipitation from the GNIP network around the Mediterranean basin and literature data are combined with isotopic data from aquifers in south east Spain to explain the typology of the precipitation events dominating recharge. Although precipitation from Atlantic origin (d-excess = 10‰) occurs more frequently in the western Mediterranean basin, the quantities in each event are generally moderate to low. Important rainfall events generally have Mediterranean origin (d-excess=15‰). The total precipitation in Gibraltar is a mixture between precipitation from both origins (d-excess in Gibraltar = 12.39‰). However, with increasing volume of the storms the Mediterranean character dominates. These heavier storms contribute mainly to recharge, as illustrated by the d-excess of 13.8‰ in deep groundwater of the Campo de Dalias (Almeria province). One of the challenges to meet ever-growing water demands is to increase recharge from events with a low return period yielding intermediate quantities per event, but forming the bulk of the annual precipitation.Cet article met en avant le paramètre «excès en deutérium» dans la détermination de l’origine des précipitations dans le bassin méditerranéen. Afin d’expliquer la nature des précipitations qui dominent la recharge de l’aquifère, nous avons combiné les signatures isotopiques fournies par le réseau GNIP avec les données de la littérature ainsi que des teneurs isotopiques provenant d’aquifères du sud-est de l’Espagne. Dans la partie ouest du bassin méditerranéen les précipitations provenant de l’Atlantique (excès en d. = 10‰) sont fréquentes mais celles d’origine méditerranéenne (excès en d. = 15‰) possèdent des volumes mensuels plus importants. La teneur isotopique des précipitations totales à Gibraltar indique une origine géographique mixte des pluies (excès en d. à Gibraltar = 12,39 ‰). Plus les volumes mensuels de pluie sont élevés plus le caractère méditerranéen s’affirme. Ces fortes précipitations contribuent principalement à la recharge, en effet l’excès en deutérium des nappes profondes du Campo de Dalias (province d’Almeria) correspond à 13,8 ‰. L’un des défis rencontrés pour satisfaire la demande croissante d’eau consiste à accroître la recharge à partir de précipitations présentant une faible période de retour et produisant des quantités moyennes de pluie par événement, mais qui constituent la majeure partie des précipitations annuelles

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

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km(2): dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of similar to 15 g.kg(-1) and a range of 30 g.kg(-1) in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information

A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols

Soil spectral libraries (SSLs) are important big-data archives (spectra associated with soil properties) that are analyzed via machine-learning algorithms to estimate soil attributes. Since different spectral measurement protocols are applied when constructing SSLs, it is necessary to examine harmonization techniques to merge the data. In recent years, several techniques for harmonization have been proposed, among which the internal soil standard (ISS) protocol is the most largely applied and has demonstrated its capacity to rectify systematic effects during spectral measurements. Here, we postulate that a spectral transfer function (TF) can be extracted between existing (old) SSLs if a subset of samples from two (or more) different SSLs are remeasured using the ISS protocol. A machine-learning TF strategy was developed, assembling random forest (RF) spectral-based models to predict the ISS spectral condition using soil samples from two existing SSLs. These SSLs had already been measured using different protocols without any ISS treatment the Brazilian (BSSL, generated in 2019) and the European (LUCAS, generated in 2009-2012) SSLs. To verify the TF's ability to improve the spectral assessment of soil attributes after harmonizing the different SSLs' protocols, RF spectral-based models for estimating organic carbon (OC) in soil were developed. The results showed high spectral similarities between the ISS and the ISS-TF spectral observations, indicating that post-ISS rectification is possible. Furthermore, after merging the SSLs with the TFs, the spectral-based assessment of OC was considerably improved, from R2 = 0.61, RMSE (g/kg) = 12.46 to R2 = 0.69, RMSE (g/kg) = 11.13. Given our results, this paper enhances the importance of soil spectroscopy by contributing to analyses in remote sensing, soil surveys, and digital soil mapping