Estimating Soil Organic Carbon Research Articles

Changes in soil organic carbon pools in a long‐term trial with perennial fodder crops in acid soils of north‐east India

AbstractEfficient estimation of soil organic carbon (SOC) is vital for understanding and monitoring the effect of perennial fodder crops in conserving SOC. In subtropical regions, there is limited information on SOC accumulation and its allocation into different pools under long‐term grasses and legumes. Therefore, we investigated the dynamics of SOC in a 20‐year‐old field trial with seven perennial grass species and a legume in a Typic Paleudalf soil under subtropical climate in north‐east India by analysing oxidizable organic C (Coc) and its fractions of very labile (CVL), labile (CL), less labile (CLL) and non‐labile (CNL), microbial biomass C (Cmic) and mineralizable C (Cmin). Growing perennial fodder crops increased SOC in the 0–0.60 m soil depth from 19.9%–39.6% compared with the conventional cultivation with maize (Zea mays). The relative efficacy of the fodder species to SOC accumulation was Setaria sphacelata = Brachieria rosenesis > Panicum maximum cv. Makunia = Arachis pintoi > Panicum maximum cv. Hamil > Paspalam conjugalum = Pennisetum purpureum > Thysanolaena maxima. Among the analysed fractions, CVL, CL, CLL and Cmic were influenced most by the fodder crops and the active pools (CVL+CL) constituted 71.6% of the SOC. The results indicate that under the tested subtropical climate, soil under perennial grasses and legumes conserves organic C and that most of the SOC is in labile pools of short residence time.

A modified soil organic carbon density model for a forest watershed in southern China

In the context of global climate change, correctly estimating soil organic carbon (SOC) stocks is significant. Because SOC density is the basis for calculating total SOC, exploring the spatial distribution of SOC density is more important. In this study, a typical forest watershed in southern China was analysed. An established exponential model that combined the soil erosion, topography, and average annual rainfall in the region to estimate SOC density with varying soil depth was modified by simulated rainfall experiments and 137Cs (Caesium-137) tracer soil erosion techniques. Thus, a modified exponential model for the SOC density in southern China was established. The results showed that the correlation coefficient (R2) reached 0.870 for the linear regression analysis of the simulated and measured SOC densities. The differences between the measured and simulated SOC densities in different soil layers (0–60cm) all passed the independent sample t-test. Additionally, the Nash-Sutcliffe coefficient for the simulated and measured SOC densities was 0.97 in the forest watershed. Furthermore, the application of the modified exponential model showed that the measured SOC densities were in good agreement with the simulated SOC densities in the different forest areas tested. These results illustrated that the modified exponential model could be effectively used to simulate the vertical distribution of SOC density in southern China. Because the parameters in the modified exponential model were easy to obtain, this modified model could be applied to simulate the vertical distribution of the SOC density in different geomorphological areas. Therefore, the results of this study will help to understand the global carbon cycle and provide valuable information for constructing the ecological environment of various landscapes.

Estimation of soil organic carbon under different vegetation types on a hillslope of China’s northern Loess Plateau using state-space approach

Soil organic carbon (SOC) plays a critical role in revegetation of semi-arid areas. The accurate estimation of SOC under various land-use types is fundamental to sustain ecosystem productivity. Thus, the dominant factors of the spatial distribution of SOC in shallow soil layers were determined at hillslope scale. The state-space modeling approach was used to quantify the relationship between SOC stock and land-use type, soil properties, topographic features, and fine root biomass (FRB) at 0–20 and 20–40 cm soil layers of a hillslope on the Loess Plateau. The best state-space models explained more than 96% of the variations in SOC stocks on the hillslope. The best multivariate state-space models including land-use type, FRB, soil pH, and total nitrogen were optimal for 0–20 and 20–40 cm soil layers. Land-use type was the dominant factor for identification of localized variation in SOC in the 0–40 cm soil layer. The results underscored the importance of land-use type in SOC variation on the hillslopes of th...

Factors acquisition and content estimation of farmland soil organic carbon based upon Internet of Things

Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon (SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things (IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.

Organic carbon prediction in soil cores using VNIR and MIR techniques in an alpine landscape

Diffuse reflectance spectroscopy (DRS), including visible and near-infrared (VNIR) and mid-infrared (MIR) radiation, is a rapid, accurate and cost-effective technique for estimating soil organic carbon (SOC). We examined 24 soil cores (0–100 cm) from the Sygera Mountains on the Qinghai–Tibet Plateau, considering field-moist intact VNIR, air-dried ground VNIR and air-dried ground MIR spectra at 5-cm intervals. Preprocessed spectra were used to predict the SOC in the soil cores using partial least squares regression (PLSR) and a support vector machine (SVM). The SVM models performed better with three predictors, with the ratio of performance to inter-quartile distance (RPIQ) and R2 values typically exceeding 1.74 and 0.73, respectively. The SVM using the DRS technique indicated accurate predictive results of SOC in each core. The RPIQ values of the shrub meadow, forest and total dataset prediction using air-dried ground VNIR were 1.97, 2.68 and 1.99, respectively; the values using field-moist intact VNIR were 1.95, 2.07 and 1.76 and those using air-dried ground MIR were 1.78, 1.96 and 1.74, respectively. We conclude that the DRS technique is an efficient and rapid method for SOC prediction and has the potential for dynamic monitoring of SOC stock density on the Qinghai–Tibet Plateau.

Towards complete and harmonized assessment of soil carbon stocks and balance in forests: The ability of the Yasso07 model across a wide gradient of climatic and forest conditions in Europe

Accurate carbon-balance accounting in forest soils is necessary for the development of climate change policy. However, changes in soil organic carbon (SOC) occur slowly and these changes may not be captured through repeated soil inventories. Simulation models may be used as alternatives to SOC measurement. The Yasso07 model presents a suitable alternative because most of the data required for the application are readily available in countries with common forest surveys. In this study, we test the suitability of Yasso07 for simulating SOC stocks and stock changes in a variety of European forests affected by different climatic, land use and forest management conditions and we address country-specific cases with differing resources and data availability. The simulated SOC stocks differed only slightly from measured data, providing realistic, reasonable mean SOC estimations per region or forest type. The change in the soil carbon pool over time, which is the target parameter for SOC reporting, was generally found to be plausible although not in the case of Mediterranean forest soils. As expected under stable forest management conditions, both land cover and climate play major roles in determining the SOC stock in forest soils. Greater mean SOC stocks were observed in northern latitudes (or at higher altitude) than in southern latitudes (or plains) and conifer forests were found to store a notably higher amount of SOC than broadleaf forests. Furthermore, as regards change in SOC, an inter-annual sink effect was identified for most of the European forest types studied. Our findings corroborate the suitability of Yasso07 to assess the impact of forest management and land use change on the SOC balance of forests soils, as well as to accurately simulate SOC in dead organic matter (DOM) and mineral soil pools separately. The obstacles encountered when applying the Yasso07 model reflect a lack of available input data. Future research should focus on improving our knowledge of C inputs from compartments such as shrubs, herbs, coarse woody debris and fine roots. This should include turnover rates and quality of the litter in all forest compartments from a wider variety of tree species and sites. Despite the limitations identified, the SOC balance estimations provided by the Yasso07 model are sufficiently complete, accurate and transparent to make it suitable for reporting purposes such as those required under the UNFCCC (United Nations Framework Convention on Climate Change) and KP (Kyoto Protocol) for a wide range of forest conditions in Europe.

The resistance of centennial soil charcoal to the “Walkley-Black” oxidation

The “Walkley-Black” oxidation with dichromate is a routine protocol for the estimation of soil organic carbon (SOC) content. Dichromate oxidation is also used to quantify black carbon (BC) in soil and sediments based on the larger resistance of BC to dichromate oxidation than uncharred SOC. The aim of this study was (i) to evaluate whether dichromate-based SOC quantification methods discriminate between uncharred SOC and charcoal residues in the topsoil of pre-industrial charcoal kiln sites and (ii) to test the influence of aging on the recovery of charcoal by dichromate oxidation. We selected 40 topsoil samples from 20 pre-industrial charcoal kiln sites of Wallonia, Belgium, and adjacent reference soils unaffected by charcoal production. Samples included soils from forest and cropland, with a diversity of soil types and textural classes. SOC content was estimated by the original Walkley-Black procedure (results were multiplied by the traditional correction factor of 1.32), and by an adapted method that includes external heating of the digestion mixture to overcome incomplete oxidation of uncharred SOC and get rid of the correction factor of 1.32. Results were compared to the amount of total organic carbon (TOC) content, measured by dry combustion, and to the content of charcoal-C in soil, estimated by differential scanning calorimetry. The soil of a currently active charcoal kiln site was also analyzed and used as a reference subject to limited aging. 23.6% of charcoal-C was recovered by the Walkley-Black procedure in the soil of the currently active kiln site against 65% for the soil of pre-industrial kiln sites, which indicates that the resistance of charcoal to dichromate oxidation decreases with aging. The recovery of charcoal increased to 90% after boiling of the digestion mixture, providing evidence that heat catalyzes the oxidation of charcoal. The substantial oxidation of charcoal by dichromate and the variability of recovery according to the degree of alteration of charcoal and conditions of reaction support the idea that the quantification of BC based on its chemical resistance is challenging and can be subject to important biases if calibration is not adapted to the quality of BC of the sample of interest. Because the recovery of BC by the Walkley-Black method is incomplete, the presence of large amounts of BC in soils frequently affected by fire might be a significant cause of underestimation of SOC by the Walkley-Black method in regional and global databases.

Toward inventory-based estimates of soil organic carbon in forests of the United States.

Soil organic carbon (SOC) is the largest terrestrial carbon (C) sink on Earth; this pool plays a critical role in ecosystem processes and climate change. Given the cost and time required to measure SOC, and particularly changes in SOC, many signatory nations to the United Nations Framework Convention on Climate Change report estimates of SOC stocks and stock changes using default values from the Intergovernmental Panel on Climate Change or country-specific models. In the United States, SOC in forests is monitored by the national forest inventory (NFI) conducted by the Forest Inventory and Analysis (FIA) program within the U.S. Department of Agriculture, Forest Service. The FIA program has been consistently measuring soil attributes as part of the NFI since 2001 and has amassed an extensive inventory of SOC in forest land in the conterminous United States and southeast and southcentral coastal Alaska. That said, the FIA program has been using country-specific predictions of SOC based, in part, upon a model using SOC estimates from the State Soil Geographic (STATSGO) database compiled by the Natural Resources Conservation Service. Estimates obtained from the STATSGO database are averages over large map units and are not expected to provide accurate estimates for specific locations, e.g., NFI plots. To improve the accuracy of SOC estimates in U.S. forests, NFI SOC observations were used for the first time to predict SOC density to a depth of 100cm for all forested NFI plots. Incorporating soil-forming factors along with observations of SOC into a new estimation framework resulted in a 75% (48±0.78Mg/ha) increase in SOC densities nationally. This substantially increases the contribution of the SOC pool, from approximately 44% (17Pg) of the total forest ecosystem C stocks to 56% (28Pg), in the forest C budget of the United States.

Spatial Expansion and Soil Organic Carbon Storage Changes of Croplands in the Sanjiang Plain, China

Soil is the largest pool of terrestrial organic carbon in the biosphere and interacts strongly with the atmosphere, climate and land cover. Remote sensing (RS) and geographic information systems (GIS) were used to study the spatio-temporal dynamics of croplands and soil organic carbon density (SOCD) in the Sanjiang Plain, to estimate soil organic carbon (SOC) storage. Results show that croplands increased with 10,600.68 km2 from 1992 to 2012 in the Sanjiang Plain. Area of 13,959.43 km2 of dry farmlands were converted into paddy fields. Cropland SOC storage is estimated to be 1.29 ± 0.27 Pg C (1 Pg = 103 Tg = 1015 g) in 2012. Although the mean value of SOCD for croplands decreased from 1992 to 2012, the SOC storage of croplands in the top 1 m in the Sanjiang Plain increased by 70 Tg C (1220 to 1290). This is attributed to the area increases of cropland. The SOCD of paddy fields was higher and decreased more slowly than that of dry farmlands from 1992 to 2012. Conversion between dry farmlands and paddy fields and the agricultural reclamation from natural land-use types significantly affect the spatio-temporal patterns of cropland SOCD in the Sanjiang Plain. Regions with higher and lower SOCD values move northeast and westward, respectively, which is almost consistent with the movement direction of centroids for paddy fields and dry farmlands in the study area. Therefore, these results were verified. SOC storages in dry farmlands decreased by 17.5 Tg·year−1 from 1992 to 2012, whilst paddy fields increased by 21.0 Tg·C·year−1.

Proxies for soil organic carbon derived from remote sensing

The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.

A State‐Space Analysis of Soil Organic Carbon in China's Loess Plateau

AbstractAn accurate estimation of soil organic carbon (SOC) is important for the evaluation and management of carbon (C) flux in terrestrial ecosystems. However, there is little work on the spatial variability of SOC in deep soils and its driving factors. Thus, the objective of the study was to derive the primary factors dominating the spatial distribution of SOC in different soil layers with the use of the autoregressive state‐space approach. The concentration of SOC was measured to the depth of 500 cm (n = 86) along a south–north transect of China's Loess Plateau. The mean SOC of the 500‐cm soil profile generally decreased from south to north following the decreasing rainfall gradient. Based on the investigated factors, the state‐space model was able to capture 90.3–99.9% of the spatial variability of SOC in the various soil layers. According to the coefficients in the optimal state‐space model for each soil layer, climatic factors such as precipitation and temperature had a dominant control over the spatial distribution of SOC at shallow depths. However, both climatic and edaphic (e.g. soil texture) factors, and to a small extent land use, influenced the spatial behavior of SOC at the 40–200 cm soil depth. For soil layers below 200 cm, the importance of land use was revealed, and the spatial characteristics of SOC were together driven by land use, climatic and edaphic factors. This is critical for the management of soil C flux in deep soils and the C stock and cycle in terrestrial ecosystems.Table SI. Basic properties of soils and climate and elevation under three land uses along the south–north transect on the Loess Plateau (mean ± standard deviation). Note that SWC is gravimetric soil water content. Copyright © 2016 John Wiley & Sons, Ltd.

Soil organic carbon stocks are systematically overestimated by misuse of the parameters bulk density and rock fragment content

Abstract. Estimation of soil organic carbon (SOC) stocks requires estimates of the carbon content, bulk density, rock fragment content and depth of a respective soil layer. However, different application of these parameters could introduce a considerable bias. Here, we explain why three out of four frequently applied methods overestimate SOC stocks. In soils rich in rock fragments (> 30 vol. %), SOC stocks could be overestimated by more than 100 %, as revealed by using German Agricultural Soil Inventory data. Due to relatively low rock fragments content, the mean systematic overestimation for German agricultural soils was 2.1–10.1 % for three different commonly used equations. The equation ensemble as re-formulated here might help to unify SOC stock determination and avoid overestimation in future studies.

Spatial distribution of soil organic carbon stock in Moso bamboo forests in subtropical China

Moso bamboo (Phyllostachys heterocycla (Carr.) Mitford cv. Pubescens) is an important timber substitute in China. Site specific stand management requires an accurate estimate of soil organic carbon (SOC) stock for maintaining stand productivity and understanding global carbon cycling. This study compared ordinary kriging (OK) and inverse distance weighting (IDW) approaches to study the spatial distribution of SOC stock within 0–60 cm using 111 soil samples in Moso bamboo forests in subtropical China. Similar spatial patterns but different spatial distribution ranges of SOC stock from OK and IDW highlighted the necessity to apply different approaches to obtain accurate and consistent results of SOC stock distribution. Different spatial patterns of SOC stock suggested the use of different fertilization treatments in Moso bamboo forests across the study area. SOC pool within 0–60 cm was 6.46 and 6.22 Tg for OK and IDW; results which were lower than that of conventional approach (CA, 7.41 Tg). CA is not recommended unless coordinates of the sampling locations are missing and the spatial patterns of SOC stock are not required. OK is recommended for the uneven distribution of sampling locations. Our results can improve methodology selection for investigating spatial distribution of SOC stock in Moso bamboo forests.

Estimation of soil organic carbon and total nitrogen in different soil layers using VNIR spectroscopy: Effects of spiking on model applicability

Soil organic carbon (SOC) and total nitrogen (TN) play major roles in soil quality and the global carbon budget. They can be measured rapidly and cost-effectively via visible and near-infrared reflectance (VNIR) spectroscopy. However, the reliability of this method is questionable because of the effects of heterogeneity. At present, only a few publications have addressed the effect of soil layers on model applicability, especially for highly heterogeneous soils in forest ecosystems. In the current work, we evaluated the performance of VNIR spectroscopy in estimating SOC and TN contents of soils collected from a mixed mountain forest in Central China. We also investigated the applicability of spectroscopic models between soil layers. We then further explored the possibility of using spiking with extra-weighting to improve model applicability. To achieve such objectives, we evaluated the applicability accuracy of the initial models (global and layered models) and modified models (spiked models with and without extra-weighting). Results showed that all the initial models successfully predicted SOC and TN. That is, for SOC, R2P ranged from 0.79 to 0.90, ratio of performance to inter-quartile range (RPIQ) ranged from 3.07 to 3.97, and the root mean square error (RMSEP) ranged from 0.54% to 0.88%; for TN, R2P ranged from 0.66 to 0.86, RPIQ ranged from 2.12 to 3.78, and RMSEP ranged from 0.05% to 0.08%. However, the prediction accuracies were seriously reduced when the model constructed from the top soil layer was used to predict the sub-surface soil properties, and vice versa. In terms of model applicability, our results demonstrated that spiking improved the applicability of the initial calibrations (RMSEP and absolute prediction bias were obviously reduced) and that the accuracy was further improved when the spiking subset was extra-weighted. When the extra-weighting reached a certain level, the accuracies remained stable or slightly reduced. Our results illustrated that spiking alone and spiking with extra-weighing are effective approaches to improve model applicability in the VNIR estimation of SOC and TN between different soil layers in a highly heterogeneous forest. This approach is potentially useful in rapidly quantifying and monitoring soil carbon and nitrogen pools in heterogeneous landscapes.

Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression

Visible and near infrared (VIS-NIR) spectroscopy has been applied to estimate soil organic carbon (SOC) content with many modeling strategies and techniques, in which a crucial and challenging problem is to obtain accurate estimations using a limited number of samples with reference values (labeled samples). To solve such a challenging problem, this study, with Honghu City (Hubei Province, China) as a study area, aimed to apply semi-supervised regression (SSR) to estimate SOC contents from VIS-NIR spectroscopy. A total of 252 soil samples were collected in four field campaigns for laboratory-based SOC content determinations and spectral measurements. Semi-supervised regression with co-training based on least squares support vector machine regression (Co-LSSVMR) was applied for spectral estimations of SOC contents, and it was further compared with LSSVMR. Results showed that Co-LSSVMR could improve the estimations of SOC contents by exploiting samples without reference values (unlabeled samples) when the number of labeled samples was not excessively small and produce better estimations than LSSVMR. Therefore, SSR could reduce the number of labeled samples required in calibration given an accuracy threshold, and it holds advantages in SOC estimations from VIS-NIR spectroscopy with a limited number of labeled samples. Considering the increasing popularity of airborne platforms and sensors, SSR might be a promising modeling technique for SOC estimations from remotely sensed hyperspectral images.

Elevation-based upscaling of organic carbon stocks in High-Arctic permafrost terrain: a storage and distribution assessment for Spitsbergen, Svalbard

Accurate quantity and distribution estimates of permafrost soil organic carbon (SOC) stocks are needed to project potential feedbacks to climate, following warming. Still, upscaling from local fiel...

Storage and spatial patterns of organic carbon of soil profiles in Guangdong Province, China

Regional soil organic carbon (SOC) investigations play an important role in building knowledge of the global soil C cycle system. The purpose of the present study was to estimate soil organic carbon (SOC) storage for different soil types and land uses in Guangdong Province, China. The results showed that the total SOC storage in the study area was 1.25 Pg, of which 0.41 Pg SOC was in A horizon soils (mean depth 17.0cm), 0.51 Pg SOC was in the B horizon (mean depth 29.5cm) and 0.33 Pg SOC was in the C horizon (mean depth 48.9cm). SOC storage in Ferrallisols was approximately 0.976 Pg for the total soil profile, accounting for 78.1% of total SOC storage. Forest soils were the main SOC pool by land use, accounting for approximately 80.3% of total SOC storage. Regardless of soil type and land use, subsoil was the primary SOC storage location in the study area. The SOC contents of the upper soil horizon were closely related to the SOC contents of the lower soil horizon, possibly suggesting that there is movement of SOC from the surface soil to lower horizons. Because of soil degradation and erosion, approximately 13.3 Tg SOC entered the surrounding water, accounting for 3.2% of the SOC storage of A horizon soils, and approximately 20.9 Tg SOC was redistributed in surface soils of the study area each year.

Evaluation of the spectral characteristics of five hyperspectral and multispectral sensors for soil organic carbon estimation in burned areas

Frequent wildfires in the north-west region of Spain affect soil organic matter. Soil properties can be estimated both spatially and temporally using remote sensing. A wide range of satellite and airborne hyperspectral and multispectral sensors are currently available. The spectral resolution varies substantially among sensors, making it difficult to identify the most suitable sensors and spectral regions for a specific application. This study aims to identify the sensors and wavelengths with the greatest potential for topsoil organic C mapping. Total (TOC) and oxidisable organic carbon (OC) content were measured in 89 soil samples collected in a mountain region of north-western Spain. Reflectance spectra of the samples in the spectral region 400–2450 nm were resampled to the bands of five sensors: Compact Airborne Spectrographic Imager (CASI), Airborne Hyperspectral Scanner (AHS), Hyperion, Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS). Calibration models obtained using partial least-squares regression proved to be effective for hyperspectral sensors and also for the multispectral sensor MODIS (R2 = 0.75–0.89), which suggests that hyperspectral capability is not required to accurately predict topsoil organic C. Models based on Landsat performed well, but with an error ~30–45% greater than that obtained for the hyperspectral sensors and MODIS.

Estimation accuracies of near infrared spectroscopy for general soil properties and enzyme activities for two forest sites along three transects

Visible and near infrared spectroscopy (vis-NIRS) is an established method for estimating the contents of soil organic carbon (SOC) and soil nitrogen (N). Recent studies have suggested that it may also be useful for estimating enzyme activities, however this potential has not been explored in detail. Objectives were to determine estimation accuracies of vis-NIRS for general soil properties (SOC, N, pH, texture) and nine enzyme activities for two forest sites, one on a sandy soil (Grinderwald) and the other on a loess soil (Rüdershausen). For each site, a calibration sample consisting of two transects sampled down to 185cm (Grinderwald: 128units, Rüdershausen: 64units) and an independent validation sample consisting of one transect (Grinderwald: 64units, Rüdershausen: 32units) was obtained and their absorbance spectra recorded. Chemometric approaches included the standard partial least squares (PLS) regression and PLS regression with a genetic algorithm (GA-PLS) for variable selection, which may improve estimation accuracies. This study confirmed the marked usefulness of vis-NIRS for an estimation of SOC and N contents in independent transects of a field scale. Estimation accuracy of soil pH and texture in independent transects was variable and mainly dependent on the range of measured data. GA-PLS markedly improved estimation accuracies compared to PLS in the cross-validation, but generally not in the validation transects. Few enzyme activities could be estimated in independent validation, but there was almost no additional benefit of vis-NIRS for their estimations compared to estimations using measured contents of main properties; however, these contents of main properties may also be estimated with vis-NIRS. Overall, for the studied sites, we do not see a benefit of vis-NIRS for a direct estimation of enzyme activities compared to laboratory methods, most likely due to a lack of sufficiently strong specific impacts on the measured spectral signals.

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

The determination of soil texture and organic carbon across agricultural areas provides important information to derive soil condition. Precise digital soil maps can help to till agricultural fields with more accuracy, greater cost-efficiency and better environmental protection. In the present study, the laboratory analysis of sand, silt, clay and soil organic carbon (SOC) content was combined with hyperspectral image data to estimate the distribution of soil texture and SOC across an agricultural area. The aim was to identify regions with similar soil properties and derive uniform soil regions based on this information. Soil parameter data and corresponding laboratory spectra were used to calibrate cross-validated (leave-one-out) partial least squares regression (PLSR) models, resulting in robust models for sand (R2 = 0.77, root-mean-square error (RMSE) = 5.37) and SOC (R2 = 0.89, RMSE = 0.27), as well as moderate models for silt (R2 = 0.62, RMSE = 5.46) and clay (R2 = 0.53, RMSE = 2.39). The regression models were applied to Airborne Imaging Spectrometer for Applications DUAL (aisaDUAL) hyperspectral image data to spatially estimate the concentration of these parameters. Afterwards, a decision tree, based on the Food and Agriculture Organization (FAO) soil texture classification scheme, was developed to determine the soil texture for each pixel of the hyperspectral airborne data. These soil texture regions were further refined with the spatial SOC estimations. The developed method is useful to identify spatial regions with similar soil properties, which can provide a vital information source for an adapted treatment of agricultural fields in terms of the necessary amount of fertilizers or water. The approach can also be adapted to wider regions with a larger sample size to create detailed digital soil maps (DSMs). Further, the presented method should be applied to future hyperspectral satellite missions like Environmental Mapping and Analysis Program (EnMap) and Hyperspectral Infrared Imager (HyspIRI) to cover larger areas in shorter time intervals. Updated DSMs on a regular basis could particularly support precision farming aspects.