Estimating Soil Organic Carbon Research Articles

Potential of Hyperspectral Data Combined With Optimal Band Combination Algorithm for Estimating Soil Organic Carbon Content in Lakeside Oasis

ABSTRACTAccurate estimation of soil organic carbon (SOC) content is essential for promoting regional sustainable agriculture and improving land quality. Visible and near‐infrared (Vis‐NIR) near‐Earth remote sensing spectroscopy has become an effective alternative to the traditional time‐consuming and costly methods due to its high‐resolution and nondestructive application, but it is vulnerable to the redundancy of spectral information and the overlap between bands. This study delves into the potential of optimal spectral parameters for estimating SOC in arid lakeside oases, using Bosten Lake in Xinjiang, China, as a focal point. Soil samples (0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm) were collected, and their SOC content and hyperspectral reflectance were measured. The spectral data underwent preprocessing techniques, including continuum removal (CR), standard normal variate (SNV), and continuous wavelet transform (CWT). SOC content was predicted using back propagation neural network models constructed based on one‐dimensional (1D), two‐dimensional (2D), and three‐dimensional (3D) correlation coefficients. Results showcased the effectiveness of the CWT method in accentuating potential spectral information and enhancing variable correlation. Among the indices, 3D exhibited the highest performance (R2 = 0.82, RPD = 2.02 for TDI‐1 at 0–10 cm; R2 = 0.85, RPD = 2.28 for TDI‐2 at 10–20 cm; R2 = 0.83, RPD = 2.24 for TDI‐1 at 20–30 cm; R2 = 0.86, RPD = 2.53 for TDI‐4 at 30–40 cm), followed by 2D and then 1D. These insights offer guidance for future strategies in hyperspectral data preprocessing and spectral index determination, facilitating SOC spatial distribution mapping and advancing sustainable agricultural planning. They also have implications for determining the spatial distribution of SOC content based on spatial interpolation, which would contribute to regional agricultural planning and sustainable development.

A comprehensive review of soil organic carbon estimates: Integrating remote sensing and machine learning technologies

Abstract Purpose Accurately assessing soil organic carbon (SOC) content is vital for ecosystem services management and addressing global climate challenges. This study undertakes a comprehensive bibliometric analysis of global estimates for SOC using remote sensing (RS) and machine learning (ML) techniques. It showcases the historical growth and thematic evolution in SOC research, aiming to amplify the understanding of SOC estimation themes and provide scientific support for climate change adaptation and mitigation. Materials and Methods Employing extensive literature database analysis, bibliometric network analysis, and clustering techniques, the study reviews 1,761 articles on SOC estimation using RS technologies and 490 articles on SOC employing both RS and ML technologies. Results and Discussion The results indicate that satellite-based RS, particularly the Landsat series, is predominant for estimation of SOC and other associated studies, with North America, China, and Europe leading in evaluations with Africa is having low evaluations adopting RS technology. Trends in the research demonstrate an evolution from basic mapping to advanced topics such as carbon (C) sequestration, complex modeling, and big data utilization. Thematic clusters from co-occurrence analysis suggest the interplay between technology development, environmental surveys, soil properties, and climate dynamics. Conclusion The study highlights the synergy between RS and ML, with advanced ML techniques proving to be critical for accurate SOC estimation. These findings are crucial for comprehensive ecosystem SOC estimation, informed environmental management and strategic decision-making.

Estimation of Soil Organic Carbon Content Using Visible and Near-Infrared Spectroscopy in the Red River Delta, Vietnam

Accurate estimation of Soil Organic Carbon (SOC) is vital for assessing soil fertility, health, and carbon sequestration. Visible and Near-Infrared (Vis-NIR) spectroscopy has gained popularity worldwide for SOC estimation due to its cost-effectiveness and environmental benefits. However, inconsistencies arise from varying preprocessing techniques and regression models applied across different datasets and regions. Few studies explore combinations of spectral preprocessing, modeling algorithms, and resampling techniques. This study presents the first SOC estimation using Vis-NIR spectroscopy in the Red River Delta, Vietnam. We assessed estimation performances incorporating fifteen preprocessing techniques, four regression models, and three resampling methods to identify the most effective strategies. Standard Normal Variate (SNV) emerged as the top preprocessing technique, while Partial Least Squares Regression (PLSR) demonstrated the highest accuracy with minimal discrepancies between calibration and validation. Regarding resampling methods, repeated cross-validation (repeatedcv) proved most robust, with simple cross-validation as an alternative. By utilizing SNV, PLSR, and repeatedcv, we achieved the first successful Vis-NIR spectroscopy-based SOC estimation in the Red River Delta and Vietnam. This approach satisfied stringent statistical criteria for predictive models, yielding validation performance metrics of R2 = 0.740, RMSE = 0.166, RPD = 2.337, and RPIQ = 2.321. Our findings highlight the importance of optimizing preprocessing, regression, and resampling techniques for accurate Vis-NIR spectroscopy-based SOC prediction.

Developing a digital mapping of soil organic carbon on a national scale using Sentinel-2 and hybrid models at varying spatial resolutions

Mapping the spatial distribution of soil organic carbon (SOC) is crucial for monitoring soil health, understanding ecosystem functions, and contributing to global carbon cycling. However, few studies have directly compared the influence of hybrid models and individual models with varying spatial resolutions on SOC prediction at a national scale. In this study, by combining remote sensing data, we utilized the LUCAS 2018 soil dataset to evaluate the potential capacities of hybrid models for predicting SOC content at different spatial resolutions in Germany. The hybrid models PLSRK and RFK consisted of partial least square regression (PLSR) with residual original kriging (OK) models, and random forest (RF) models with residual OK models, respectively. Individual PLSR and RF models were used as reference models. All these models were applied to estimate SOC content at 10 m, 50 m, 100 m, and 200 m spatial resolutions. Sentinel-2 bands, band indices, and topography variables were as predictors. The results revealed that hybrid models had a more accurate prediction of SOC content with higher explanations and lower prediction errors compared with individual models. The RFK model at the spatial resolution of 100 m was the fittest model with R2 = 0.416, RMSE = 0.545, and RPIQ = 1.647, which enhanced 3.74% of explanation compared with the performance of RF model. The results also showed that hybrid models at a relatively coarse resolution (100 m) had better accuracy instead of those at high spatial resolution (10 m, 50 m). Sentinel-2 remote sensing data showed significant predictive capabilities for estimating SOC content. The predicted spatial distribution of SOC content revealed that the high SOC concentrated in the northwest grassland, central and southwestern mountains, and the Alps in Germany. Our study provided a benchmark SOC map in Germany for monitoring the changes resulting from land use and climate impacts, and we illustrated the accuracy of hybrid models and the effects of spatial resolutions on SOC predictions at a national scale.

An ensemble estimate of Australian soil organic carbon using machine learning and process-based modelling

Abstract. Spatially explicit prediction of soil organic carbon (SOC) serves as a crucial foundation for effective land management strategies aimed at mitigating soil degradation and assessing carbon sequestration potential. Here, using more than 1000 in situ observations, we trained two machine learning models (a random forest model and a k-means coupled with multiple linear regression model) and one process-based model (the vertically resolved MIcrobial-MIneral Carbon Stabilization, MIMICS, model) to predict the SOC stocks of the top 30 cm of soil in Australia. Parameters of MIMICS were optimised for different site groupings using two distinct approaches: plant functional types (MIMICS-PFT) and the most influential environmental factors (MIMICS-ENV). All models showed good performance with respect to SOC predictions, with an R2 value greater than 0.8 during out-of-sample validation, with random forest being the most accurate; moreover, it was found that SOC in forests is more predictable than that in non-forest soils excluding croplands. The performance of continental-scale SOC predictions by MIMICS-ENV is better than that by MIMICS-PFT especially in non-forest soils. Digital maps of terrestrial SOC stocks generated using all of the models showed a similar spatial distribution, with higher values in south-eastern and south-western Australia, but the magnitude of the estimated SOC stocks varied. The mean ensemble estimate of SOC stocks was 30.3 t ha−1, with k-means coupled with multiple linear regression generating the highest estimate (mean SOC stocks of 38.15 t ha−1) and MIMICS-PFT generating the lowest estimate (mean SOC stocks of 24.29 t ha−1). We suggest that enhancing process-based models to incorporate newly identified drivers that significantly influence SOC variation in different environments could be the key to reducing the discrepancies in these estimates. Our findings underscore the considerable uncertainty in SOC estimates derived from different modelling approaches and emphasise the importance of rigorous out-of-sample validation before applying any one approach in Australia.

Estimating soil organic carbon using sentinel-2 data under zero tillage agriculture: a machine learning approach

Estimating soil organic carbon using sentinel-2 data under zero tillage agriculture: a machine learning approach

Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration

Understanding and monitoring soil organic carbon (SOC) stocks is crucial for ecosystem carbon cycling, services, and addressing global environmental challenges. This study employs the BERTopic model and bibliometric trend analysis exploration to comprehensively analyze global SOC estimates. BERTopic, a topic modeling technique based on BERT (bidirectional encoder representatives from transformers), integrates recent advances in natural language processing. The research analyzed 1761 papers on SOC and remote sensing (RS), in addition to 490 related papers on machine learning (ML) techniques. BERTopic modeling identified nine research themes for SOC estimation using RS, emphasizing spectral prediction models, carbon cycle dynamics, and agricultural impacts on SOC. In contrast, for the literature on RS and ML it identified five thematic clusters: spatial forestry analysis, hyperspectral soil analysis, agricultural deep learning, the multitemporal imaging of farmland SOC, and RS platforms (Sentinel-2 and synthetic aperture radar, SAR). From 1991 to 2023, research on SOC estimation using RS and ML has evolved from basic mapping to topics like carbon sequestration and modeling with Sentinel-2A and big data. In summary, this study traces the historical growth and thematic evolution of SOC research, identifying synergies between RS and ML and focusing on SOC estimation with advanced ML techniques. These findings are critical to global ecosystem SOC assessments and environmental policy formulation.

Soil Organic Carbon Prediction Based on Vis-NIR Spectral Classification Data Using GWPCA-FCM Algorithm.

Soil visible and near-infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil-forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large-scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c-means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA-FCM-Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA-FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large-scale spectral libraries.

Accessing global soil raster images and equal-area splines to estimate soil organic carbon stocks on the regional scale

Accessing global soil raster images and equal-area splines to estimate soil organic carbon stocks on the regional scale

Handheld In Situ Methods for Soil Organic Carbon Assessment

Soil organic carbon (SOC) assessment is crucial for evaluating soil health and supporting carbon sequestration efforts. Traditional methods like wet digestion and dry combustion are time-consuming and labor-intensive, necessitating the development of non-destructive, cost-efficient, and real-time in situ measurements. This review focuses on handheld in situ methodologies for SOC estimation, underscoring their practicality and reasonable accuracy. Spectroscopic techniques, like visible and near-infrared, mid-infrared, laser-induced breakdown spectroscopy, and inelastic neutron scattering each offer unique advantages. Preprocessing techniques, such as external parameter orthogonalization and standard normal variate, are employed to eliminate soil moisture content and particle size effects on SOC estimation. Calibration methods, like partial least squares regression and support vector machine, establish relationships between spectral reflectance, soil properties, and SOC. Among the 32 studies selected in this review, 14 exhibited a coefficient of determination (R2) of 0.80 or higher, indicating the potential for accurate SOC content estimation using in situ approaches. Each study meticulously adjusted factors such as spectral range, pretreatment method, and calibration model to improve the accuracy of SOC content, highlighting both the methodological diversity and a continuous pursuit of precision in direct field measurements. Continued research and validation are imperative to ensure accurate in situ SOC assessment across diverse environments. Thus, this review underscores the potential of handheld devices for in situ SOC estimation with good accuracy and leveraging factors that influence its precision. Crucial for optimizing carbon farming, these devices offer real-time soil measurements, empowering land managers to enhance carbon sequestration and promote sustainable land management across diverse agricultural landscapes.

Spatial estimation of soil organic carbon, total nitrogen, and soil water storage in reclaimed post-mining site based on remote sensing data

The estimation of Soil Organic Carbon (SOC), Total Nitrogen (TN), and Soil Water Storage (SWS) is crucial in comprehending ecosystem services and environmental sustainability. It plays a crucial role in guiding sustainable restoration strategies and supporting the long-term health of post-mining sites. Remote sensing technology provides valuable tools for modelling and mapping soil properties in reclaimed post-mining sites efficiently and cost-effectively. This study aimed to utilize remote sensing data to estimate SOC, TN, and SWS in a reclaimed post-mining site. Field data was collected from 130 research plots to obtain reference data for SOC, TN, and SWS from the Sonica hard coal post-mine spoil heap. Remote sensing data were: airborne laser scanning (ALS) point clouds and Planets cope satellite imageries. Generalized Additive Models (GAM) were used to develop predictive models. Wall-to-wall predictions of analyzed variables were performed. The results identified topographic and remote sensing indicators that significantly influence SOC, TN, and SWS. Digital Terrain Model (DTM), aspect, and blue spectral band are variables that explain SOC storage, with a significant influence of DTM, ranging from −8 to 18 Mg ha−1. TN was explained by DTM, Canopy Height Model (CHM), blue and Near Infrared (NIR) spectral bands, and Normalized Difference Vegetation Index (NDVI), mainly influenced by NIR and NDVI, ranging from −1.1 to 0.8 and −0.9 to 1.4 Mg ha−1, respectively. The values of Topographic Wetness Index (TWI), aspect, CHM, blue and NIR spectral bands explained SWS, highlighting their importance in assessing soil water dynamics in post-mining landscapes, with TWI and CHM being particularly influential, ranging from −2 to 5.1 and −6 to 2 mm, respectively. However, caution is advised when predicting SOC and TN using remote sensing in post-mining sites due to geogenic carbon considerations.

Estimation of soil organic carbon in LUCAS soil database using Vis-NIR spectroscopy based on hybrid kernel Gaussian process regression

Soil Organic Carbon (SOC) is crucial for determining soil fertility and environmental quality. The problem with traditional SOC chemical analysis methods is that they are time-consuming and resource-intensive. In recent years, visible-near infrared (Vis-NIR) spectroscopy has been employed as an alternative method for SOC determination. However, when applied on a larger scale, the prediction accuracy of soil properties decreases due to the heterogeneity of samples. Therefore, this study compared and analyzed the performance of partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and gaussian process regression (GPR) in predicting SOC. On this basis, a GPR model based on a hybrid kernel function (HKF-GPR) was proposed for SOC prediction. This hybrid kernel function was designed according to the properties of single kernel functions and the characteristics of soil spectral data. Results indicate that in large soil spectral databases, the GPR model outperforms other models in estimating SOC. The HKF-GPR model achieved the best SOC estimation accuracy, with an R2 of 0.7671, RMSE of 5.2934 g/kg, RPD of 2.0721, and RPIQ of 2.5789. Compared to other regression models, the HKF-GPR model proposed in this paper offers broader applicability and superior performance, enabling SOC estimation in large soil spectral libraries.

Comparison of field and imaging spectroscopy to optimize soil organic carbon and nitrogen estimation in field laboratory conditions

Regenerative agriculture (RA) aims to improve soil health, water retention capacity, and resilience through sustainable regeneration and retention of soil organic carbon (SOC) and soil nitrogen (SN). Although laboratory analysis offers a reliable method for measuring SOC and SN, access to such facilities can be limited in remote areas and inconvenient, particularly if a large number of samples need to be analyzed. To address this, we compared two hyperspectral sensors, SVC HR-1024i field spectroradiometer (FS) and Specim IQ imaging spectrometer (IS), to estimate SOC and SN using 157 soil samples collected from agricultural sites in Taita Hills, Kenya. Reference SOC and SN content (%) were analyzed in the laboratory, and spectral measurements and images of the air-dried soil samples were generated using protocols suitable for field laboratories. Finally, we employed partial least squares regression (PLSR), Gaussian process regression (GPR), and least absolute shrinkage and selection operator (LASSO) to estimate SOC and SN from the preprocessed soil spectra over different wavelength ranges. Our best models yielded better accuracy for SOC estimation (R2 = 0.83andRMSE = 0.36 %) and an R2of 0.70with anRMSEof0.07 % for SN using the field spectroradiometer. Both SOC and SN modeling over the full wavelength and shortwave-infrared (SWIR) regions achieved considerably better predictive accuracy than visible to near-infrared regions. These results suggest that FS with the SWIR region is best suited for SOC and SN estimation to support the planning and monitoring needs of RA initiatives.

Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran

Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoising methods and a feature generation strategy have been used. For denoising, three distinct algorithms were employed over the PRISMA image, including Savitzky–Golay + first-order derivative (SG + FOD), VisuShrink, and total variation (TV), and their impact on SOC estimation was compared in four different methods: Method One (reflectance bands without denoising, shown as M#1), Method Two (denoised with SG + FOD, shown as M#2), Method Three (denoised with VisuShrink, shown as M#3), and Method Four (denoised with TV, shown as M#4). Based on the results, the best denoising algorithm was TV (Method Four or M#4), which increased the estimation accuracy by about 27% (from 40% to 67%). After TV, the VisuShrink and SG + FOD algorithms improved the accuracy by about 23% and 18%, respectively. In addition to denoising, a new feature generation strategy was proposed to enhance accuracy further. This strategy comprised two main steps: first, estimating the number of endmembers using the Harsanyi–Farrand–Chang (HFC) algorithm, and second, employing Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to generate high-level features based on the estimated number of endmembers from the HFC algorithm. The feature generation strategy was unfolded in three scenarios to compare the ability of PCA and ICA transformation features: Scenario One (without adding any extra features, shown as S#1), Scenario Two (incorporating PCA features, shown as S#2), and Scenario Three (incorporating ICA features, shown as S#3). Each of these three scenarios was repeated for each denoising method (M#1–4). After feature generation, high-level features were added to the outputs of Methods One, Three, and Four. Subsequently, three machine learning algorithms (LightGBM, GBRT, RF) were employed for SOC modeling. The results showcased the highest accuracy when features obtained from PCA transformation were added to the results from the TV algorithm (Method Four—Scenario Two or M#4–S#2), yielding an R2 of 81.74%. Overall, denoising and feature generation methods significantly enhanced SOC estimation accuracy, escalating it from approximately 40% (M#1–S#1) to 82% (M#4–S#2). This underscores the remarkable potential of hyperspectral sensors in SOC studies.

Mapping Topsoil Carbon Storage Dynamics of Croplands Based on Temporal Mosaicking Images of Landsat and Machine Learning Approach

Understanding changes of soil organic carbon (SOC) in top layers of croplands and their driving factors is a vital prerequisite in decision-making for maintaining sustainable agriculture. However, high-precision estimation of SOC of croplands at regional scale is still an issue to be solved. Based on soil samples, synthetic image of bare soil and geographical data, this paper predicted SOC density of croplands using Random Forest model in the Black Soil Region of Jilin Province, China in 2005 and 2020, and analyzed its influencing factors. Results showed that random forest model that integrates bare soil composite images improve the accuracy and robustness of SOC density prediction. From 2005 to 2020, the total SOC storage in croplands decreased from 89.96 to 82.79 Tg C with an annual decrease of 0.48 Tg C yr−1. The mean value of SOC density of croplands decreased from 3.42 to 3.32 kg/m2, and high values are distributed in middle parts. Changes of SOC represented significant heterogeneity spatially. 62.14% of croplands with SOC density greater than 4.0 kg/m2 decreased significantly, and 38.60% of croplands with SOC density between 2.5 and 3.0 kg/m2 significantly increased. Climatic factors made great contributions to SOC density, however, their relative importance (RI) to SOC density decreased from 44.65% to 37.26% during the study period. Synthetic images of bare soil constituted 23.54% and 26.29% of RI in the SOC density prediction, respectively, and the contribution of each band was quite different. The RIs of topographic and vegetation factors were low but increased significantly from 2005 to 2020. This study can aid local land managers and governmental agencies in assessing carbon sequestration potential and carbon credits, thus contributing to the protection and sustainable use of black soils.

Enhancing soil organic carbon estimation accuracy: Integrating spatial vegetation dynamics and temporal analysis with Sentinel 2 imagery

This article introduces an improved method for estimating Soil Organic Carbon (SOC) using Sentinel 2 images, with a specific emphasis on the Dakshina Kannada area in India. By examining 364 soil samples, SOC estimation models were constructed using Random forests (RF) and Partial Least Squares Regression (PLSR), focusing on the impact of nearby vegetation pixels. The approach consisted of classifying soil samples by the presence of plant pixels at distances of 0, 10, and 20 m, and evaluating the influence of dry vegetation by the use of the Normalised Burn Ratio 2 (NBR2). The findings demonstrated a significant improvement in the precision of the model (by up to 20 %) when vegetation pixels within a 20-meter radius of the sample locations were omitted. The research also included a temporal analysis utilizing Sentinel-2 images from April 2017 to May 2023. This analysis showed strong relationships between the amount of exposed soil and the accuracy of predicting soil organic carbon (SOC) levels. These results emphasize the need to take into account both the spatial dynamics of vegetation and the temporal variations in bare soil covering to get an accurate estimate of soil organic carbon (SOC). This study improves the accuracy and dependability of SOC evaluations by including geographical and temporal aspects, providing useful insights for agricultural and ecological applications.

Evaluating performance of miniaturized spectrometers in predicting soil properties using multivariate statistical analysis

Despite the increasing use of portable, low-cost spectrometers in estimating soil properties, there is lack of documentation regarding the factors contributing to the lower performance of these spectrometers when compared to conventional ones. This study investigates potential factors influencing performance of the Nanoquest, a low-cost spectrometer, in estimating soil organic carbon (SOC) and total nitrogen (TN). To conduct the study, five different models (cubist, partial least squares regression, support vector machines, random forest, and generalised boosted models) were tested for the estimation SOC and TN and a fivefold cross-validation analysis was conducted for model hyperparameter optimization. The Nanoquest achieved a Lin’s concordance correlation coefficient (CCC) value of 0.84 and an R2 value of 0.74 for SOC. For TN, CCC values of 0.86 and an R2 value of 0.78 were obtained. To understand the impact of the spectral range and spectral resolution on SOC and TN estimation, the ASD spectra were digitally resampled to match the Nanoquest spectral range and resolution. This resampling resulted in a slight decrease in model performance for the spectral range and a more pronounced decrease for the spectral resolution.

Estimating soil organic carbon and total nitrogen across inhabited and uninhabited islands based on dual simulations in the entire- and sub-areas

Soil total organic carbon (TOC) and total nitrogen (TN) mapping methods have been developed for different ecosystems; however, little attention has been paid to archipelagos comprising inhabited and uninhabited islands. In this study, we proposed a method of dual simulations in the entire- and sub-areas to adequately improve the simulation accuracy of soil TOC and TN in the archipelago. A typical archipelago, Miaodao Archipelago, was selected to demonstrate the method. The dual simulations comprehensively considered the common characteristics of the entire-area and the unique conditions of each sub-area in the archipelago. Through the dual simulations, the accuracy can be viewed in three hierarchies: the entire area (the first hierarchy), the inhabited and uninhabited islands (the second hierarchy), and the single island (the third hierarchy). The simulation accuracies for TOC were increased by 12.03%, 12.03%, and 16.94% in the first, second, and third hierarchies, respectively, compared with the baseline, and those for TN were increased by 7.13%, 9.21%, and 14.72%, respectively. The lowest normalized root mean squared errors for TOC and TN were 0.114 and 0.105, respectively, which were similar to those reported in previous studies that achieved high simulation accuracy in other areas. Considerable differences in soil TOC and TN existed across the inhabited (39.95 g/kg TOC and 3.61 g/kg TN) and uninhabited (63.42 g/kg TOC and 7.08 g/kg TN) islands, which were driven by the great differences in natural conditions and human activities across the two sub-areas. The dual simulations contributed to the further improvement of simulation accuracy in soil mapping, providing a practical method for soil TOC and TN estimation in the archipelago, and revealing the spatial differences in soil TOC and TN across the inhabited and uninhabited islands.

Estimating soil organic carbon sequestration potential in the Chinese Mollisols region

AbstractMollisols region of China is a ballast stone of Chinese food security. In this study, 484 soil organic carbon (SOC) data points from the documented soil surface (0–20 cm) samples in the typical Chinese Mollisols region were selected. The regional soil fertility was divided into high, medium, and low levels based on the SOC content from the long‐term positioning fertilization stations in Shenyang, Harbin, and Hailun of Northeast China. Furthermore, the regional SOC balance point (SOCb) model was built driven by the factors of annual mean temperature, annual precipitation, annual effective accumulative temperature, the ratio of precipitation and temperature, soil clay content, and pH. Using geographic information systems (GIS) interpolation method, the regional mean SOCb and SOC sequestration potential (SOCsp) at the SOCb's status were simulated to be 40.10 g kg−1 and 10.71 kg m−2, respectively. Moreover, the SOC content increased (SOCc) and the SOC sequestration capacity increased of medium‐ or low‐fertility soil were evaluated by GIS subtraction. Consequently, the means of SOCc in medium‐ and low‐fertility soils could be 16.88 g kg−1 and 26.79 g kg−1, respectively. Furthermore, the means of SOCsp in medium‐ and low‐fertility soils could be 4.30 kg m−2 and 7.43 kg m−2, respectively. The sensitivity analysis of this model showed that the high SOCb area was distributed in study region with low temperature, dry, high clay content, and low pH. The results provide an actual perspective on estimating regional SOCsp and soil fertility promotion target induced by the different soil fertility levels.

Estimation of soil organic carbon by combining hyperspectral and radar remote sensing to reduce coupling effects of soil surface moisture and roughness

Soil organic carbon (SOC) is important in the global carbon cycle. Accurate estimation of SOC content in cultivated land is a prerequisite for evaluating the carbon sequestration potential and quality of soils. However, existing SOC prediction studies based on hyperspectral remote sensing neglect the spectral response of the physical properties of surface soil, leading to inadequate model generalization. With the exponential growth of remote sensing data, the development of pixel-level soil spectral correction methods based on multi-source remote sensing data has become an interesting and challenging topic. This method aims to minimize the effect of soil physical properties on spectra, thus addressing the poor spatiotemporal transferability of SOC prediction models due to uncertain variations in surface soil physical properties. In this study, a soil spectral correction strategy is constructed using satellite hyperspectral image (HSI) and synthetic aperture radar (SAR) images through multi-order polynomial regression and convolutional neural networks. This strategy considers soil physical variables such as soil moisture (SM) content and root mean square height (RMSH) of soil surface roughness. The soil spectral correction model and SOC content prediction model were established using 80 soil samples collected from Site 1. Afterward, the performance and transferability of both models were verified using the remaining 25 samples from Site 1 and 50 samples from Site 2. The results showed that: 1) The effect of SM and RMSH on the soil pixel spectrum can be significantly reduced after correcting HSI using soil spectral correction strategy. The correlation coefficients between the corrected pixel spectrum and the ground-based spectrum increase by over 60 % compared with those between the original spectrum and the ground-based spectrum. 2) Soil spectral correction improves the prediction accuracy and mapping capability of HSI for SOC content, with the highest RP2 of 0.743 and RMSEP of 3.455 g/kg at Site 1. 3) Compared with the original HSI-based SOC prediction model, the soil spectral correction strategy based on multi-order polynomial and convolutional neural network reduced the RMSEP of SOC prediction results at Site 2 by 5.082 g/kg and 5.454 g/kg, and the RP2 increased by 0.390 and 0.409, respectively. 4) When predicting SOC content from raw HIS, SM and RMSH contribute to more than 60 % of the bias, with SM having a larger bias than RMSH. The findings of this study emphasize the influence of soil physical properties on SOC prediction and contribute to the existing research on SOC mapping using HSI and SAR data.