Article

On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems

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Abstract

Soil organic carbon (SOC) dynamics affect soil quality, agricultural productivity and atmospheric CO2 concentration. Despite the need for spatial assessments of SOC content over time, reliable estimates from traditional field survey methods are limited by data availability; where measurements are often made at discrete point locations, at a coarse sample spacing or over a limited spatial extent. Remote sensing (RS) is in a strong position to provide spatially distributed, reproducible, scale-appropriate and resource-efficient measurements of SOC content and fluxes at field, landscape and regional scales. This paper provides a critical review of optical RS techniques for such applications. The first part of the paper reviews the methods, instruments and techniques used for developing predictive models for monitoring spatial SOC content. Secondly, sources of spatio-temporal SOC variations are examined, including the lateral transfer of SOC by erosion, soil structural breakdown and land management practices, in the context of RS data and techniques. The key challenges of using RS to monitor SOC contents are discussed along with opportunities for improving SOC predictions within a spatial framework. Such opportunities include the use of ancillary data, scale-specific methods, improved development of spectral libraries and better integration of RS technologies into empirical and simulation SOC models. This paper aims to provide a transparent assessment and practical guide to RS techniques and products in order to further advance and better incorporate the use of RS methods within soil science.

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... Starting with the launch of Landsat 5 in 1984, the availability of multispectral satellite data has increased substantially during the last decades. The potential of remote sensing to predict SOC dynamics was already recognized more than a decade ago (Croft, Kuhn, and Anderson 2012). Multitemporal soil samples can be combined with the corresponding EO data to conduct spatiotemporal models and generate information on SOC trends and changes Heuvelink et al. 2021). ...
... Based on this, spatially explicit and fine-scale SOC predictions can act as an important resource to support existing soil monitoring programs such as the Land Use/Cover Area Frame Survey (LUCAS) soil survey in Europe (De Rosa et al. 2024). However, this potential has rarely been realized due to challenges with SOC changes being small (< 0.5 g kg −1 year −1 ), the lack of appropriate ground truth data from long-term soil monitoring, and the low availability of EO data for early years (Croft, Kuhn, and Anderson 2012). ...
... Several studies have raised concerns about the high uncertainty and low signal-to-noise ratio of EO-based SOC models Croft, Kuhn, and Anderson 2012). For spatiotemporal SOC models and the prediction of SOC trends, the signal can be defined as the absolute SOC change in time, while the noise describes the SOC prediction uncertainty in space (Heuvelink et al. 2021;Paustian et al. 2019). ...
Article
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Soil monitoring requires accurate and spatially explicit information on soil organic carbon (SOC) trends and changes over time. Spatiotemporal SOC models based on Earth Observation (EO) satellite data can support large-scale SOC monitoring but often lack sufficient temporal validation based on long-term soil data. In this study, we used repeated SOC samples from 1986 to 2022 and a time series of multispectral bare soil observations (Landsat and Sentinel-2) to model high-resolution cropland SOC trends for almost four decades. An in-depth validation of the temporal model uncertainty and accuracy of the derived SOC trends was conducted based on a network of 100 long-term monitoring sites that were continuously resampled every 5 years. While the general SOC prediction accuracy was high (R 2 = 0.61; RMSE = 5.6 g kg −1), the direct validation of the derived SOC trends revealed a significantly greater uncertainty (R 2 = 0.16; p < 0.0001), even though predicted and measured values showed similar distributions. Classifying the results into declining and increasing SOC trends, we found that 95% of all sites were either correctly identified or predicted as stable (p < 0.001), highlighting the potential of our findings. Increased accuracies for SOC trends were found in soils with higher SOC contents (R 2 = 0.4) and sites with reduced tillage (R 2 = 0.26). Based on the signal-to-noise ratio and temporal model uncertainty, we were able to show that the necessary time frame to detect SOC trends strongly depends on the absolute SOC changes present in the soils. Our findings highlight the potential to generate significant cropland SOC trend maps based on EO data and underline the necessity for direct validation with repeated soil samples and long-term SOC measurements. This study marks an important step toward the usability and integration of EO-based SOC maps for large-scale soil carbon monitoring.
... The estimation of SOC in the laboratory frequently requires destructive sampling, which is then followed by preliminary processing to minimize the heterogeneity of the soil sample. In addition, soil BD may be promptly estimated in situ using near-infrared or mid-infrared spectroscopy, or with a gamma ray probe (Bellon-Maurel and McBratney, 2011;Croft et al., 2012). Despite the need for extensive sample preparation (collecting, grinding, sifting, and drying), spectroscopic techniques offer a fast and nondestructive method for measuring SOC with a greater precision, saving money and processing time. ...
... Despite the need for extensive sample preparation (collecting, grinding, sifting, and drying), spectroscopic techniques offer a fast and nondestructive method for measuring SOC with a greater precision, saving money and processing time. Croft et al. (2012) and Rossel et al. (2006) both found that spectroscopic techniques were superior to other methods. The spectroscopic methods may prove convenient for calibrating satellite and aerial data (Stevens et al., 2010). ...
... However, these are destructive methods that only offer point data (McCarty et al., 2002). Calculations of SOC and data collection could also be done on the go, thanks to sensors mounted on tractors (Croft et al., 2012). Even if these field methods cost less than lab-based methods, they do not significantly lower the cost of generating a soil carbon map across a large region. ...
... According to Croft et al. (2012), there have been considerably fewer studies using airborne and satellite platforms compared to proximal RS. Access to data, cost and training requirements affect the accessibility of airborne/satellite-derived reflectance products. ...
... Access to data, cost and training requirements affect the accessibility of airborne/satellite-derived reflectance products. Moreover, there is an increased complexity in deriving reflectance data from a pixel compared to controlled laboratory conditions, due to changes in illumination angles, terrain effects, atmospheric attenuation and low signal-to-noise ratios (Ben-Dor et al., 2002;Croft et al., 2012). Thus, using satellite RS to study soil properties involves multiple challenges. ...
... RS imagery has been used for several decades to determine soil properties, but the data have limitations in terms of access, cost, training requirements, and spatial and spectral resolution (Croft et al., 2012). Vis-NIR spectroscopy is therefore presented as a means to study soil ...
Thesis
Reflectance spectroscopy can be used to non-destructively characterize materials for a wide range of applications. In this study, visible-near infrared (Vis-NIR) spectroscopy was evaluated for the prediction of diverse soil properties (clay content, SOC, TN, and pH) related to different soil samples from the Eastern Cape Province in South Africa. Soil samples were scanned by a portable spectrometer at 1 nm wavelength resolution from 350 to 2500 nm. Calibrations between soil properties obtained from digital soil maps and reflectance spectra were then developed using cross-validation under partial least squares regression (PLSR) and support vector machine regression (SVMR). Raw reflectance and Savitzky-Golay first derivative data were used separately for all the samples in the data set. Key wavelengths to predict clay content, SOC, TN, and pH were identified using the variable importance projection (VIP) and Boruta algorithms. Data were additionally divided into two random subsets of 70 and 30% of the full data, which were each used for calibration and validation. The results indicated that Vis-NIR spectroscopy can be successfully used to predict soil clay content, SOC, TN and pH.
... Practical limitations include the cost of taking soil inventories and the inability to determine the spatiotemporal changes in soil resulting from changes in land use and land management. Spatiotemporal methods using remote sensing technologies provide a means of measuring, monitoring and verifying SOC stocks (Post et al, 2001, Croft et al, 2012, Smith et al, 2012, Minasny et al, 2013. ...
... Such uncertainty in the direction of change is explained by the complex interaction between the soil and the vegetation, land management methods (e.g. manure management, crop-fallow succession), erosion processes (Croft et al, 2012), climate and soil properties (Guo and Gifford, 2002) and whether the study design is based on a chronosequence or time series (Fujisaki et al, 2015). . DSM sets out to create soil databases at a given resolution by using field and laboratory observation methods coupled with spatial and non-spatial environmental data (covariates) through quantitative relationships (Boentinger et al, 2010). ...
... The datasets have the reflectance of the land surface in the visible and near infra-red regions which is widely used to indicate the primary and ecological productivity. The reflectance in the middle and thermal infrared bands depends mostly on the soil properties (Mulder et al, 2011, Croft et al, 2012. ...
Thesis
Le changement d’usage des terres, lié à l’agriculture et à la foresterie, engendre une perte importante de biodiversité et représente une part importante de nos émissions de gaz à effet de serres à l’origine du changement climatique. Le mécanisme de Réduction des Émissions liéesà la Déforestation et à la Dégradation des forêts, conservation, gestion durable et restauration des stocks de carbone (REDD+) initié il y a dix ans peine à se mettre en place du fait de nombreuses contraintes politiques et scientifiques. Malgré l’existence de lignes directrices élaborées par la communauté scientifique internationale, des outils et données sont nécessaires afin de fournir des informations précises, à moindre coût et utilisables à différentes échelles. L’objectif de cette thèse est de développer des méthodologies innovantes pour réduire les incertitudes sur les estimations des émissions et séquestrations de CO2 associées à la déforestation, dégradation et régénération des terres. Madagascar, pays engagédans la REDD+ depuis huit ans, et soumis à des pertes importantes de biodiversité et de couvert forestier, est pris comme exemple. Trois études complémentaires ont été réalisées : i) le suivi de la déforestation en région tropicale humide et sèche par satellites, ii) l’estimation des stocks de carbone dans les sols et les forêts et iii) la modélisation des changements d’usage de terres. Nous avons développé une nouvelle méthodologie de suivi de la déforestation à Madagascar permettant de tenir compte de la définition des forêts et d'améliorer la prise en compte des petites parcelles de défriche brûlis. Les chiffres de la déforestation ont ainsi été actualisés jusqu’en 2013. Une méthodologie innovante de cartographie des stocks de carbone dans le sol à des résolutions fines et à des échelles régionales a été mise au point en couplant plusieurs facteurs environnementaux etdesinventaires de terrain à l’aide d’un modèle d’arbres de « forêts aléatoire » (Random Forests). Ce modèle spatial du carbone a été appliqué sur des images satellites acquises vingt années plus tôt afin d’évaluer la dégradation des stocks de carbone du sol et leur régénération potentielle. Des facteurs de perte et gain de carbone dans le sol ont pu ainsi être estimés. Enfin, une approche de modélisation des changements d’usage des terres a permis de mieux comprendre les facteurs biophysiques et socio-économiques liés à la déforestation, dégradation des terres et régénération, et de proposer des scénarios spatialisés pour aider les décideurs. Les résultats obtenus dans cette thèse et les méthodologies développées permettentd’alimenter les discussions et documents concernant la stratégie REDD+ de Madagascar. Elle contribue plus largement à fournir des informations spatiales justes, précises spatialement et cohérentes à large échelle dans le but d’améliorer la gestion de nos écosystèmes terrestres.
... Soil reflectance varies according to chemical factors, such as soil mineralogy, SOM content and soil moisture, and also physical structure, such as surface roughness and particle size. Soil spectral signatures are defined by the reflectance of electromagnetic radiation by chemical substances as a function of wavelength (Croft et al. 2012). The color of the soil is usually closely related to its organic matter content, with darker soils being higher in organic matter, which indicates the relationship between soil organic matter content and its visible light reflectance (Aghababaie et al. 2018). ...
... To date, there is no versatile model, which fits all over the world, and the waveband selection for different study areas is also diverse (He et al. 2009). The soil generally has reflectance spectra in the 1100-2500 nm range, including three distinct absorption peaks around 1400, 1900 and 2200 nm with a few small absorption peaks between 2200 and 2500 nm (Croft et al. 2012). Organic matter affects the spectra by decreasing the overall reflectance (in the visible wavelengths (Blue, Green, Red; 400-700 nm), near-infrared (NIR; 700-1400 nm) and shortwave infrared (SWIR; 1400-2500 nm) regions of the electromagnetic spectrum (Croft et al. 2012), therefore bands around 1100, 1600, 1700 to 1800, 2000, and 2200 to 2400 nm have been identified as being particularly important for SOC calibration (Stenberg et al. 2010). ...
... The soil generally has reflectance spectra in the 1100-2500 nm range, including three distinct absorption peaks around 1400, 1900 and 2200 nm with a few small absorption peaks between 2200 and 2500 nm (Croft et al. 2012). Organic matter affects the spectra by decreasing the overall reflectance (in the visible wavelengths (Blue, Green, Red; 400-700 nm), near-infrared (NIR; 700-1400 nm) and shortwave infrared (SWIR; 1400-2500 nm) regions of the electromagnetic spectrum (Croft et al. 2012), therefore bands around 1100, 1600, 1700 to 1800, 2000, and 2200 to 2400 nm have been identified as being particularly important for SOC calibration (Stenberg et al. 2010). ...
Conference Paper
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Regarding the importance of soil organic carbon (SOC), there is an increasing demand for knowledge of the spatiotemporal variability of SOC. Because of difficulties with traditional analytical measurement (time-consuming, expensive, and also soil data of sampling points are discrete and incapable of providing continuous and complete information regarding the total study area), spatial variability of SOC through field soil sampling cannot be obtained. Using remote sensing data to provide an indirect measurement of SOC to inform digital soil mapping has the potential to provide a more reliable and cost-effective estimation. The objective of this paper is to review the remote sensing data for mapping and evaluating SOC. There are several statistical methods, including regression models, principal component analysis, the ‘soil line’ approach, and geostatistics, which have been applied to investigate the accuracy of such estimates. Results suggest that these techniques have proven valuable; however, the limitations of each technique must be tested under wider environmental circumstances to choose the best technique for predictions. It seems that predictive equations aren’t universal, and every scene needs new regression models. However, an important benefit of remotely sensed data is to present a sampling strategy that can lead to improved representation of spatial heterogeneity of SOC.
... Mapping SOC has also been studied with remote sensing using airborne and satellite platforms to cover extended areas although with lower precision. Consequently, it should be integrated with field and laboratory measurements and complementary sensor data for better results [50]. Our results showed the feasibility of using on-the-go soil spectra for mapping SOC with appropriate reliability, having an accuracy closer to laboratory measurements than remote sensing data. ...
... Long-term monitoring can help to identify trends and seasonal Mapping SOC has also been studied with remote sensing using airborne and satellite platforms to cover extended areas although with lower precision. Consequently, it should be integrated with field and laboratory measurements and complementary sensor data for better results [50]. Our results showed the feasibility of using on-the-go soil spectra for mapping SOC with appropriate reliability, having an accuracy closer to laboratory measurements than remote sensing data. ...
Article
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Agricultural soils serve as crucial storage sites for soil organic carbon (SOC). Their appropriate management is pivotal for mitigating climate change. Continuous monitoring is imperative to evaluate spatial and temporal changes in SOC within agricultural fields. In-field datasets of Vis-NIR soil spectra were collected on a long-term experimental site using an on-the-go spectrophotometer. Data processing for continuous SOC prediction involves a two-step modeling approach. In Step 1, a partial least square (PLSR) regression model is trained to establish a relationship between the SOC content and spectral information, including spectral preprocessing. In Step 2, the predicted SOC content obtained from the PLSR models is interpolated using ordinary kriging. Among the tested spectral preprocessing techniques and semivariogram models, Savitzky–Golay and the Gap-Segment derivative preprocessing along with a Gaussian semivariogram model, yielded the best performance resulting in a root mean square error of 1.24 and 1.26 g kg−1. A striping effect due to the transect-based data collection was addressed by testing the effectiveness of extending the spatial separation distance, employing data aggregation, and defining the distribution based on treatment plots using block kriging. Overall, the results highlight the high potential of on-the-go spectral Vis-NIR data for field-scale spatial-temporal monitoring of SOC.
... Robust quantification of change in SOC that is cost-effective and provides a statistical assessment of uncertainty is challenging; this is particularly the case in the presence of large spatial variability and slow SOC gains (Smith et al, 2020;Paustian et al., 2019;Stanley et al., 2023). For these reasons, data from universally available sources-such as freely available remote sensing imagery-that can provide an indication of temporal changes (or spatial differences) at landscape scales can help inform more effective decision making and reduce the costs associated with measurement of SOC (Ladoni et al., 2010;Croft at al., 2012;Paustian et al., 2019). ...
... Many studies have utilised remote sensing data to better model the spatial and spatio-temporal variation of SOC (Yang et al., 2009;Page et al., 2013;Wilson et al., 2017;Venter et al., 2021). However, relationships between remote-sensing variables and the temporal variation of SOC can be very site-specific (Croft et al., 2012), making it difficult to build broadly applicable predictive models of the spatiotemporal variation of SOC. Nonetheless, site-specific indicators of temporal changes can still be useful for informing the likelihood of increases or decreases in SOC. ...
Article
Full-text available
Temporal variation of soil organic carbon (SOC) is driven by land use/management practice, ecosystem conditions and climatic variation. Robust quantification of changes in SOC that is cost-effective and provides a statistical assessment of uncertainty is challenging, particularly in the face of large spatial variability and slow soil SOC changes. Remote-sensing indicators of above-ground vegetation provide some indication of the amount of fresh organic material being supplied to the soil. Although, because of the time taken for this organic material to decay and become incorporated into the soil, there will be a lag between the changes in the indicator of vegetation growth and the resulting changes in SOC. In this work, we investigate how a remotely sensed indicator of vegetation cover can be used with a lag period to predict or indicate changes in SOC for grazed pasture sites at a long-term monitoring study, which has been monitoring soil under different land uses for over forty years. We assessed how well this worked for indicating the SOC changes for different depths in the soil profile. Results suggested that a lagged remotely sensed vegetation cover-the average cover of the two preceding years-provides some indication of SOC changes for the 0-10 cm soil depth, but changes for deeper soil depths were not well predicted. Further, we investigated the potential of using soil data from a point-in-time spatial dataset (e.g. data from a baseline sampling round) to calibrate a relationship between the remotely sensed cover and SOC, which can then be applied to predict or indicate the temporal variation of SOC. Results showed this approach gave large prediction errors, likely because the temporal variation (at a fixed point in space) and spatial variation (for a fixed point in time) of SOC that is predictable by cover differences are not interchangeable.
... SOC is one of the core indices of soil fertility and plays an important role in the global carbon cycle. Although SOC is only a small part of the total soil mass, it plays a positive role in soil fertility, environmental protection, plant growth, and agricultural production (Croft et al. 2012;Nawar et al. 2016;Tiessen et al. 1994;Wang et al. 2009). Farmland soils have a relatively high carbon sequestration potential and can effectively mitigate climate change (Hutchinson et al. 2007;Liu et al. 2019). ...
... Farmland SOC sequestration is the objective of international initiative "4 per 1000". The decrease in SOC content will not only lead to the degradation of farmland ecology, but also reduce the sustainable use of farmland (Croft et al. 2012;Tiessen et al. 1994;Zhang et al. 2021). SOC stocks in topsoil are more abundant but less stable under climate change. ...
Article
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Background and aimsSolar-induced chlorophyll fluorescence (SIF) is closely related to vegetation photosynthesis and can sensitively reflect the growth and health of vegetation. Using the advantages of SIF in photosynthetic physiological diagnosis, this study carried out a collaborative study of SIF, land attributes and image reflectance spectra to estimate soil organic carbon (SOC) content in typical agricultural areas of the Qinghai-Tibet plateau (QTP).Methods The spectral reflectance (R), first derivative of reflectance (FDR), second derivative of reflectance (SDR) of spectral band of Landsat 8 Operational Land Imager (OLI) data were selected together with land attributes (i.e. elevation, slope, soil temperature, and soil moisture content) and SIF index and vegetation indices to establish the SOC content estimation models using the random forest (RF), back propagation neural network (BPNN) and partial least squares regression (PLSR), respectively.ResultsSIF index can significantly improve the SOC content estimation compared to the vegetation indices. The accuracy of the BPNN model established by combining SIF index with the FDR of Landsat 8 OLI data and land attributes was the highest (R2 = 0.977, RMSEC = 2.069 g·kg− 1, MAE = 0.945 g·kg− 1, RPD = 3.970, d-factor = 0.010).Conclusion This study confirmed the good effect of BPNN model driven by SIF index, land attributes, and Landsat 8 OLI data on the estimation of SOC content, which can provide a new way for the accurate estimation of the soil internal components in the agricultural areas.
... However, FMs have yet to achieve a similar impact on state-of-the-art performance in tasks involving spatio-temporal data. Encompassing all kinds of data with both spatial and temporal dimensions, spatiotemporal (ST) data is pervasive across an extremely diverse range of fields, including urban analysis [37], [45], [50], [53], [58], weather forecasting [9], [30], [38], climate science [16], [18], [31], [54], environmental monitoring [1], [4], [26], [51], agriculture [10], [14], [34], [55], and public health [36], [42], [49], [60], [64]. As the collection and distribution of spatiotemporal data continues to grow from diverse sources, so does the feasibility and potential of spatio-temporal foundation models (STFMs) to learn shared patterns across different domains, improving efficiency and enhancing generalization, particularly for data-deficient applications. ...
Preprint
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Foundation models have revolutionized artificial intelligence, setting new benchmarks in performance and enabling transformative capabilities across a wide range of vision and language tasks. However, despite the prevalence of spatio-temporal data in critical domains such as transportation, public health, and environmental monitoring, spatio-temporal foundation models (STFMs) have not yet achieved comparable success. In this paper, we articulate a vision for the future of STFMs, outlining their essential characteristics and the generalization capabilities necessary for broad applicability. We critically assess the current state of research, identifying gaps relative to these ideal traits, and highlight key challenges that impede their progress. Finally, we explore potential opportunities and directions to advance research towards the aim of effective and broadly applicable STFMs.
... Overall, the use of spectroscopy in the VNIR spectrum in combination with multivariate statistical methods has proven to be a precise technique for estimating not just soil C but various other soil properties as well (Viscarra Rossel et al., 2006;Croft et al., 2012 So far, monitoring of spatio-temporal changes in post-fire soil quality has yet to be conducted in the pedological and climatic conditions of Mediterranean Croatia. Therefore, the primary purpose of this research was to apply soil spectroscopy to provide a continuous quantitative assessment of post-fire effects on SOM using two modelling approaches, PLSR and ANN, to facilitate informed land management decisions for ecosystem recovery in Mediterranean conditions. ...
Article
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Wildfires profoundly impact ecosystems and soil organic matter (SOM), a critical factor in soil quality and carbon cycling. This research aimed to assess the impact of wildfire severity on SOM and the potential of visible-near infrared spectroscopy (VNIR) spanning the 350 - 1050 nm wavelength range for monitoring SOM in a post-fire landscape using two modelling approaches (i) Partial Least Squares Regression (PLSR) and (ii) Artificial Neural Networks (ANN). Following a comprehensive two-year investigation in Zadar County, Croatia, where a 13.5 ha mixed forest was moderately to severely affected by a wildfire, spectral reflectance analysis revealed that SOM content strongly influenced soil reflectance. High-severity samples exhibited the lowest reflectance compared to those with moderate severity and the control group. The critical region for SOM information in post-wildfire soil estimation models was between 550and 700 nm. ANN consistently outperformed PLSR, achieving a ratio of performance to deviation (RPD) values from1.74 to > 2.5. In contrast, PLSR achieved values between 1.62 and 2.29, demonstrating ANN's capability to provide accurate predictions of SOM content in complex post-fire SOM dynamics conditions. This research indicates that VNIR spectroscopy, particularly coupled with ANN-based models, offers a reliable and non-destructive method for assessing SOM content in post-fire environments, facilitating informed land management decisions for ecosystem recovery.
... The link between soil reflectance and particle size is well established, 25,31,66,67 as in Figure 1. The effective composition of the soil surface will also change upon crushing or grinding, as soil components such as organic matter break down, 68,69 and components previously hidden inside aggregates become exposed. ...
Article
Soil reflectance is a cumulative attribute determined by interactions between light (photons) and the physical, chemical, and biological properties of soil. Soil properties such as organic matter, moisture, mineral oxide contents, soil texture, and surface roughness all influence soil reflectance at unique wavelengths. Many standard sample preparation techniques are designed to alter soil properties, so as to homogenize samples to improve the consistency of reflectance data collected. This study aims to quantify the effects of one standard sample preparation activity, drying, and repeated wetting–drying cycles on visible–near-infrared soil reflectance, by collecting reflectance data from nine samples which were dried then wetted for a total of three times. This study demonstrates the major, permanent effects of a drying and wetting cycle on soil reflectance, and then presents a model which can be used to correct for these effects. These results have direct implications for remote sensing activities, soil libraries, and soil spectral libraries. These results show that without correction, data from soil spectral libraries, spectral data collected from stored samples, and spectral data transferred between studies, have limited utility for characterizing soils as they exist in the field. Soil spectral data collected in the lab requires correction before it may be used to predict soil properties in the field, as standard sample handling procedures change intrinsic soil properties and introduce systematic error into these data.
... Three replicates of each sample were taken and averaged into one spectrum per sample. For accurate estimation of the percentage of SOC, proper pretreatment of the soil samples for spectral analyses is important (Brunet et al., 2007;Ludwig et al., 2002;Van Waes et al., 2005;Croft et al., 2012). The soil samples were dried at 105°C, sieved and ground to a size fraction below 0.25 mm to exclude coarse and medium fine sand, thus obtaining a more homogeneous sample for analysis (Guillou et al., 2015;Nawar et al., 2016;Terra et al., 2015;Shi et al., 2015). ...
... Thus, the Veris field spectral measurements are affected by more disturbance effects compared to the site-specific above-ground ASD measurements. Surface roughness could also affect the model performance of the above-ground point ASD measurements due to the surface soil heterogeneity affecting the soil reflectance [33,64]. Models obtained from the ASD laboratory data showed better performance compared with the Veris laboratory data, displaying the effect of the inbuilt sensors and the lower spectral resolution. ...
Article
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Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. Spatial–temporal soil organic carbon (SOC) monitoring requires more efficient data acquisition. This study aims to evaluate the potential of spectral on-the-go proximal measurements to serve these needs. The study was conducted as a long-term field experiment. SOC values ranged between 14 and 25 g kg−1 due to different fertilization treatments. Partial least squares regression models were built based on the spectral laboratory and field data collected with two spectrometers (site-specific and on-the-go). Correction of the field data based on the laboratory data was done by testing linear transformation, piecewise direct standardization, and external parameter orthogonalization (EPO). Different preprocessing methods were applied to extract the best possible information content from the sensor signal. The models were then thoroughly interpreted concerning spectral wavelength importance using regression coefficients and variable importance in projection scores. The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. The use of different spectrometers under varying soil conditions revealed shifts in wavelength importance. Still, our findings on the use of on-the-go spectroscopy for spatial–temporal SOC monitoring are promising.
... These machine learning methods can effectively account for nonlinear data relationships and integrate supplementary variables in the learning process. Due to the fact that SOC dynamics and correlations with other variables are often non-proportional and even non-monotonic (Croft et al., 2012), nonlinear techniques become a core requirement for analyzing SOC dynamics. The proper model selection, hence, is determined by the available data, objectives, and level of understanding of underlying processes. ...
... Contour ridging reduced carbon loss compared to NT and SR. It has been shown that cropping systems and farm management practices have a great impact on SOC (Croft et al., 2012;Poffenbarger et al., 2017). Tillage induces soil aggregate breakdown and accelerates organic carbon mineralization. ...
Article
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Soil erosion has become one of the most common environmental problems and threatens food security. This study assessed the short‐term effect of tillage and mulch on soil redistribution using the beryllium‐7 method, soil moisture distribution, and soil organic carbon loss through soil erosion in typical agroecological conditions of Benin. The experiment was conducted on acrisols (at Dan) and ferralsols (at Za‐zounmè) in central Benin. Three tillage practices slope ridging (SR), contour ridging (CR), and no‐tillage (NT), and three mulch doses 0 (0 M), 3 (3 M), and 7 t ha⁻¹ (7 M) on soil erosion under maize were investigated. The results showed a tillage and mulch interaction significantly (p < 0.05) influencing the soil redistribution, the loss of total carbon, the carbon of the particulate organic matter (C_POM), and the carbon content of the fine organic matter (C_MOM). High soil erosion was observed under SR0M (−10.19 t ha⁻¹) at Dan and under NT0M (−7.36 t ha⁻¹) at Za‐zounmè. NT7M (0.80 t ha⁻¹), SR7M (0.69 t ha⁻¹), CR3M (2.07 t ha⁻¹), and CR7M (4.05 t ha⁻¹) showed deposition at Dan, while SR7M (0.23 t ha⁻¹), NT7M (1.69 t ha⁻¹), and CR7M (3.93 t ha⁻¹) showed deposition at Za‐zounmè. C_MOM was lost on both sites. Mulch increases soil moisture for all three tillage treatments, and this effect is well pronounced especially if the amount of mulch is great. This study revealed useful information to be taken into consideration when developing soil and water conservation management strategies in Benin.
... However, due to the strong spatial heterogeneity and uncertainty of soil, these first two methods hinder accurate estimation of regional SOC when soil samples are limited (Guo et al., 2021). The soil landscape model utilizes environmental variables formed by soil formation as covariates (such as digital elevation model, land use type, vegetation information, etc.) to construct a relationship model between SOC and environmental variables, thus achieving the prediction and mapping of SOC (Croft et al., 2012). This effectively solves the obstacle caused by strong soil variability to digital mapping (Arrouays et al., 2020). ...
Article
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Soil organic carbon (SOC) undergoes rapid changes due to human production activities, which have an impact on the land carbon cycle and ultimately global change. As one of the main human production activities, coal mining significantly impacts the soil carbon cycle. However, due to the lack of remote sensing modeling of soil carbon in mining areas, the spatio-temporal changes and driving mechanisms of SOC in mining areas remain unclear. Therefore, this study investigated and determined SOC data from 300 sampling points (depth of 0–20 cm) located in an arid mining area of China. Remote sensing images were then used to established a soil organic carbon density (SOCD) prediction model within the Random Forest (RF) model to achieve digital mapping of soil organic carbon stocks (SOCS). The spatiotemporal changes of SOCS were analyzed using SOCS digital mapping, and the influencing mechanism of SOCS was revealed using path analysis. The results showed that the constructed SOCD predictive model meets the demand for SOCD prediction (R2 ≥ 0.74, p < 0.01, RMSE ≤ 1.72 kg/m2). Under the combined influence of coal mining and land reclamation, the total amount of surface SOCS in the mining area exhibited a fluctuating upward trend from 1990 (6.34 Tg) to 2021 (7.73 Tg), with an annual growth rate of 0.038 Tg/a. The spatial distribution of SOCS generally increased from southeast to northwest. Precipitation, Normalized Difference Vegetation Index (NDVI), and land use were positively correlated to SOCS spatial distribution, while temperature, elevation, soil erosion, and mining intensity were negatively correlated to SOCS. The impact degree of factors on SOCS was as follows: NDVI > soil erosion > mining intensity > precipitation > elevation > land use > temperature. The negative impact of coal mining on SOCS was mainly indirect, through disturbance to elevation, vegetation, and soil erosion. The uneven ground subsidence and stretching caused by coal mining contribute to intensified soil erosion and vegetation degradation in the affected area, leading to a reduction in SOCS. However, SOCS did not decrease under high intensity mining, which was related to the increase in vegetation and the reduction in soil erosion in the mining area. In this study, a soil carbon prediction model was established based on remote sensing modeling to evaluate the temporal and spatial distribution of soil carbon in an arid mining area. The results can serve as valuable references for the scientific improvement of the ecological environment in mining areas, the rational planning of mining area construction, as well as low-carbon land reclamation and ecological compensation assessments.
... We also emphasize that our satellite data provides a single snapshot of the landscape at a specific time. SOC levels can vary seasonally and annually due to factors like temperature, precipitation, and vegetation dynamics (Croft et al., 2012). Ignoring these temporal variations could lead to an incomplete understanding of SOC dynamics. ...
Article
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Digital soil maps find application in numerous fields, making their accuracy a crucial factor. Mapping soil properties in homogeneous landscapes where the soil surface is concealed, as in forests, presents a complex challenge. In this study, we evaluated the spatial distribution of soil organic carbon stocks (SOCstock) under forest vegetation using three methods: regression kriging (RK), random forest (RF), and RF combined with ordinary kriging of residuals (RFOK) in combination with Sentinel-2A satellite data. We also compared their accuracies and identified key influencing factors. We determined that SOCstock ranged from 0.6 to 10.9 kg/m² with an average value of 4.9 kg/m². Among the modelling approaches, we found that the RFOK exhibited the highest accuracy (RMSE = 1.58 kg/m², NSE = 0.33), while the RK demonstrated a lack of spatial correlation of residuals, rendering this method inapplicable. An analysis of variable importance revealed that the SWIR B12 band of the Sentinel-2A satellite contributed the most to RFOK predictions. We concluded that the RFOK hybrid approach outperformed the others, potentially serving as a foundation for digital soil mapping under similar environmental conditions. Therefore, it is essential to consider spatial correlations when mapping soil properties in ecosystems that are inaccessible for capturing the spectral response of the soil surface.
... However, there are still limitations to the spaceborne measurement of SOC due to temperature, air pressure, spectral calibration, and the height of the crop. Moreover, the satellite is super expensive, so it's unsuitable for general-purpose use [23]. ...
... Machine learning techniques relate coarse-fine resolution pairs of observed images to anticipate unknown images of finer resolution. A range of methods have been employed in spatiotemporal data fusion, such as dictionary-pair learning , extreme learning (Tsagkatakis et al. 2019); regression tree (Hilker et al. 2009); random forest (Saini and Ghosh 2017); deep convolutional neural networks (Yu et al. 2020); and artificial neural networks (Croft et al. 2012). The SPSTFM was the first method to apply dictionary-pair learning approaches to spatiotemporal data fusion for natural picture super-resolution. ...
Article
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For many years, spectral remote sensing has been essential for research on the Earth’s surface. The data from a single satellite sensor is sometimes insufficient to fulfil the expanding needs of remote sensing applications. Spatial-temporal fusion techniques have become an effective way for merging spectral data from many sources and times, enabling improved data analysis and interpretation. The goal of this review paper is to offer a thorough examination of the historical growth of spatio-temporal fusion techniques for spectral remote sensing. The classification of all currently used fusion approaches, such as Unmixing, Weight-based, Bayesian-based, machine learning-based, and hybrid methods, is covered in detail. Additionally, it evaluates pixel-level, decision-level, and feature-level-based data fusion techniques and compares and contrasts their advantages and disadvantages. The report also discusses spatiotemporal fusion’s difficulties and recommends future advances. For those working in remote sensing research and practice, it offers an invaluable resource. In conclusion, this review paper provides a comprehensive overview of spatio-temporal fusion systems for spectral remote sensing, including an analysis of their comparative benefits and drawbacks and a description of their historical development. It aims to stimulate further research and development of spatio-temporal fusion methods for spectral remote sensing. In summary, this review paper presents a comprehensive overview of spatio-temporal fusion methods for spectral remote sensing, including their historical development, categorization of existing techniques and applications, and a comparative analysis of their strengths and limitations. It also discusses the current challenges and future research directions, providing a valuable resource for the remote sensing community.
... There is a certain correlation between the content of SOC and the reflectance of visible near-infrared spectroscopy, providing a basis for remotely sensing SOC [11]. Therefore, remote sensing can provide a rapid, repeatable, and cost-effective means of quantitatively assessing the spatial distribution of soil properties [12]. Hyperspectral remote sensing has been widely applied in predicting SOC content in spatially small areas, such as farmland and forests [13,14]. ...
Article
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The North China agro–pastoral zone is a large, ecologically fragile zone in the arid and semi-arid regions. Quantitative remote sensing inversion of soil organic carbon (SOC) in this region can facilitate understanding of the current status of degraded land restoration and provide data support for carbon cycling research in the region. Deep learning (DNN) for SOC inversion has been W.a hot topic over the past decade, but there have been few studies at the regional scale in the arid and semi-arid zones. In this study, a DNN model with five hidden layers and five skip connections was established using 644 spatially distributed SOC samples and Landsat 8 OLI imagery. The model was compared with the random forest algorithm in terms of generalization ability. The main conclusions were as follows: 1. The DNN algorithm can establish a high-precision SOC inversion model (R2 = 0.52, RMSE = 0.7), with 90% of errors concentrated in the range of −2.5 to 2.5 kg·C/m2; 2. the Boruta variable-screening algorithm can effectively improve the model accuracy of the random forest algorithm, but due to the DNN’s better ability to mine hidden information in the data, the improvement effect on the DNN model accuracy is limited; 3. the SOC samples in arid and semi-arid areas are highly positively skewed, with a significant impact on the modeling accuracy of DNN, and conversion is required to obtain a model with better generalization ability; and 4. in arid and semi-arid regions, SOC has a weak correlation with vegetation indices but a stronger correlation with temperature, elevation, and aridity. This study established a reliable deep learning model for SOC density in a large arid and semi-arid region, providing a reference and framework for the establishment of SOC inversion models in other regions.
... Mapping SOC has also been studied with remote sensing using airborne and satellite platforms to cover extended areas although with lower precision. Consequently, it should be integrated with field and laboratory measurements and complementary ancillary data for better results (Croft et al., 2012). Our results showed the feasibility of using on-the-go soil spectra for mapping SOC with appropriate reliability, having an accuracy closer to laboratory measurements than remote sensing data. ...
Preprint
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Agricultural soils serve as crucial storage sites for soil organic carbon (SOC). Their appropriate management is pivotal for mitigating climate change. To evaluate spatial and temporal changes in SOC within agricultural fields, continuous monitoring is imperative. In-field data sets of Vis-NIR soil spectra were collected on a long-term experimental site using an on-the-go spectrophotometer. Data processing for continuous SOC prediction involves a two-steps modelling approach. In Step 1, a Partial Least Square (PLSR) regression model is trained to establish a relationship between the SOC content and the spectral information also including spectral preprocesisng. In Step 2, the predicted SOC content obtained from the PLSR models is interpolated using ordinary kriging. Among the tested spectral preprocessing techniques and semivariogram models, SG and gapDer preprocessing along with a Gaussian semivariogram model, yielded the best performance resulting in a root mean square error of of 1.24 and 1.26 g kg-1. A striping effect due to the transect-based data collection was addressed by testing the effectiveness of extending the spatial separation distance, employing data aggregation, and defining the distribution based on treatment plots using block kriging. Overall, the results highlight the immense potential of on-the-go spectral Vis-NIR data for field-scale spatial-temporal monitoring of SOC.
... However, areas with yellow Ferralsols also showed increased goethite abundance at depth, while it did not occur for haematite in red Ferralsols (Fig. 10b). This phenomenon is possibly explained by the masking of iron oxides by SOC in the 0-0.2 m layer (Croft et al., 2012;Heller Pearlshtien and Ben-Dor, 2020). This effect is more pronounced for goethite than for haematite, as haematite pigmentation directly affects the spectral response of the visible region. ...
... Increasing the sampling density can improve the comprehensiveness; however, it will generate a disproportionate increase in the cost. Digital soil mapping (DSM) that integrates the field point data to remote sensing data has provided an economic, accurate, and practical solution for obtaining and exhibiting the spatial distribution of soil condition (Croft et al., 2012;Wang et al., 2018;Arrouays et al., 2021). Generally, various predictors are extracted by utilizing the ecological significance of remote sensing data, intrinsic correlations of the predictors in area form with field soil data in point form are established using mathematical models, and spatial simulations of the soil data are then conducted to realize the "from point to area" conversion (Chi et al., 2018;Fathololoumi et al., 2020). ...
Article
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Comprehensively revealing the spatial pattern of soil condition is vital for exploring the evolution mechanism of island ecosystem and providing reference for ecological maintenance. However, it is difficult to meet the demands of comprehensiveness and accuracy in mapping the soil condition in an archipelago that contains remote islands. Such an archipelago, namely, Shengsi Archipelago off the Yangtze River Estuary, China, was selected as the study area. Field survey was conducted on part of islands that have relatively high accessibility in the archipelago, and remote sensing data that cover the entire study area was adopted. An island soil index system, including soil content, storage, and quality indices, was proposed to represent the island soil integrated condition. Soil content indices are original soil measured data, and soil storage and quality indices are composite indices that are determined based on soil content indices. Then, a predictor system based on remote sensing and the 10-fold cross-validation were used to conduct the spatial simulations of soil indices. The results validated that the combination of soil measured data sourced by field survey could respond more sensitively to remote sensing data and integrate with it better than the original soil measured data, thereby increasing the simulation accuracies of soil storage and quality indices to higher levels than the corresponding soil content indices and achieving the spatial exhibition of soil integrated condition. The spatial pattern of soil indices in Shengsi Archipelago indicated that islands or areas with good vegetation condition but low soil salinity, land surface aridity, and human interference generally had good soil integrated condition. Then, suggestions to improve the island soil integrated condition were proposed from perspectives of different essential components of the island ecosystem. The study has provided a practical method for comprehensively mapping the soil condition in areas with low accessibility.
... However, soil properties are also estimated with a lower spectral resolution using satellite and airborne multispectral sensors. Imaging data from these sensors are recorded in only a few bands of the VNIR range and can be used to estimate the content of soil organic carbon (SOC) (Croft et al. 2012, de Paul Obade, Lal 2013 and clay (Nanni, Dematte 2006, Demattê, Fiorio 2009). However, better results can be obtained by combining satellite data with hyperspectral measurements (Peng et al. 2015). ...
Article
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Remote sensing techniques based on soil spectral characteristics are the key to future land management; however, they still require field measurement and an agrochemical laboratory for the calibration of the soil property model. Visible and near-infrared diffuse reflectance spectroscopy has proven to be a rapid and effective method. This study aimed to assess the suitability of multispectral data acquired with the agricultural digital camera in determining soil properties. This 3.2-Mpx camera captures images in three spectral bands – green, red and near-infrared. First, the reference data were collected, which consist of 151 samples that were later examined in the laboratory to specify the granulometric composition and to quantify some chemical elements. Second, additional soil properties such as cation exchange capacity, organic carbon and soil pH were measured. Finally, the agricultural digital camera photograph was taken for every soil sample. Reflectance values in three available spectra bands were used to calculate the spectra indices. The relationships between the collected data were calculated using the independent validation regression model such as Cubist and cross-validation model like partial least square in R Studio. Additionally, different types of data normalisation multiplicative scatter correction, standard normal variate, min–max normalisation, conversion into absorbance] were used. The results proved that the agricultural digital camera is suitable for soil property assessment of sand and silt, pH, K, Cu, Pb, Mn, F, cation exchange capacity and organic carbon content. Coefficient of determination varied from 0.563 (for K) to 0.986 (for soil organic carbon). Higher values were obtained with the Cubist regression model than with partial least squares.
... Although aerial RS employing Unmanned Aerial Vehicles (UAV) or manned flights can provide high resolution data (cm resolution), it cannot cover as big an area as satellite RS in the same amount of time [26]. Finally, proximal RS, which can be performed using Unmanned Ground Vehicles (UGV), vehicles with an operator, or on foot, can retrieve high-resolution data (mm resolution), but requires long and heavy effort [5,27,28]. ...
Article
Various Remote Sensing (RS) technologies and platforms have been widely used in olive cultivation studies over the last 16 years. These technologies and platforms have been applied throughout the olive cultivation cycle, providing significant insights into olive growth and productivity. The goal of this review was to determine the importance of RS technologies and platforms in specific agronomic focus areas in order to determine the equipment and platform requirements in olive cultivation studies. For this reason, frequency and correspondence studies were carried out. Unmanned aerial vehicles, multispectral sensors, and vigour assessment found to be important in olive cultivation studies. Additionally, each agronomic focus area in an olive cultivation study presents different needs in equipment, proximity of sensing, and coverage area, indicating that this must be taken into account during field experiments with RS. Further technological improvements will permit the use of other RS technologies and platforms during future studies. Finally, future studies are expected to focus more on RS data processing as well as on the use of unmanned aerial and ground vehicles in swarms for data collection and performance of actions.
... Particularly, remote sensing data provide abundant predictors, which are area data. The intrinsic correlations of the soil point data with the remote sensing area data are established, and then the conversion of soil data from point to area can be realized based on the intrinsic correlations by means of mathematical models (Croft et al., 2012;Chi et al., 2018a;Paul et al., 2020;Dharumarajan et al., 2021). In recent years, the soil organic carbon mapping in three-dimensional space, i.e., horizontal and vertical spatial simulation at a time point, was explored in certain areas. ...
Article
In the context of multiple disturbances, soil organic carbon stock (SOCS) in coastal wetlands experiences drastic spatiotemporal variations, which involve four dimensions. However, the finiteness and discontinuity of historical field soil data hinder the four-dimensional SOCS reconstruction in coastal wetlands. In this study, the zonal and progressive simulations were integrated to reconstruct the four-dimensional characteristics of coastal wetland SOCS by using field soil data in current time point and remote sensing data during the last decades. The zonal simulation was adopted to conduct the two-dimensional simulation at a low cost of field survey. The spatial and temporal progressive simulations were implemented based on the close relationships among soil factors in different depths and at different time points, respectively, for realizing the three- and four-dimensional simulations. The demonstration of the study in Chongming Island, an important coastal wetland in China, validated the low cost, high accuracy, and good applicability of the approach. Over the entire island during the last decades, SOCS in surface layer showed overall increasing characteristics, while SOCS in intermediate and bottom layers did not exhibit distinct change trend. Generally, SOCS in surface layer increased and that in intermediate and bottom layers decreased along the gradient from the shoreline to the inner island. Continuous sediment discharge and deposition enlarged the areas of coastal wetlands and thus increased SOCS in the alongshore areas. Human activities distinctly increased SOCS through long-term and large-scale agricultural activities in the inner island, while decreased it through urbanization. Then, healthy, coordinated, and sustainable measures for increasing coastal wetland SOCS were proposed from perspectives of scale control, spatial configuration, and quality promotion. Therefore, this approach could reconstruct SOCS four-dimensional characteristics and achieve the conversions of soil data from point to area, from surface to bottom, and from present to past.
... Even when studies sampled SOC multiple times to investigate changes in SOC (46,73,79,96,97,102), models were built separately for each year rather than based on use of A covariates likely due to the difficulty to resample the same soil profile. The review of Croft et al. (178) showed that RS data may be promising for modeling temporal SOC changes through the monitoring of soil structural changes, soil erosion, agricultural practices in time, but the accuracy of these covariate data obtained at the large spatial extent need to be tested further. ...
Article
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To explore how well large spatial scale digital soil mapping can contribute to efforts to monitor soil organic carbon (SOC) stocks and changes, we reviewed regional and national studies quantifying SOC within lands dominated by agriculture using SCORPAN approaches that rely on soil (S), climate (C), organisms (O), relief (R), parent material (P), age (A), and space (N) covariates representing soil forming factors. After identifying 79 regional (> 10,000 km2) and national studies that attempted to estimate SOC, we evaluated model performances with reference to soil sampling depth, number of predictors, grid-distance, and spatial extent. SCORPAN covariates were then investigated in terms of their frequency of use and data sources. Lastly, we used 67 studies encompassing a variety of spatial scales to determine which covariates most influenced SOC in agricultural lands using a subjective ranking system. Topography (used in 94% of the cases), climate (87%), and organisms (86%) covariates that were the most frequently used SCORPAN predictors, aligned with the factors (precipitation, temperature, elevation, slope, vegetation indices, and land use) currently identified to be most influential for model estimate at the large spatial extent. Models generally succeeded in estimating SOC with fits represented by R2 with a median value of 0.47 but, performance varied widely (R2 between 0.02 and 0.86) among studies. Predictive success declined significantly with increased soil sampling depth (p < 0.001) and spatial extent (p < 0.001) due to increased variability. While studies have extensively drawn on large-scale surveys and remote sensing databases to estimate environmental covariates, the absence of soils data needed to understand the influence of management or temporal change limits our ability to make useful inferences about changes in SOC stocks at this scale. This review suggests digital soil mapping efforts can be improved through greater use of data representing soil type and parent material and consideration of spatio-temporal dynamics of SOC occurring within different depths and land use or management systems.
... There are many studies that estimate the SOC content based on climate humidity, or on plant AGB or remotely-sensed vegetation index such NDVI (Croft et al., 2012;Sun et al., 2019;, and little attention is paid to the effects of soil properties such as DUL or sand % on the SOC content in different climate zones. Our results suggest that soil water retention parameters modulate the relations between climate and vegetation biomass or SOC content, and DUL is a better predictor than climate humidity for predicting SOC content, and it is necessary to incorporate soil physical and hydraulic parameters for the improvement of the prediction of SOC and plant production in semi-arid steppes. ...
Article
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Soil hydraulic properties determine water transport, distribution and storage in soils, thus affect ecosystem functioning. Soil water retention parameters, such as saturation water content (SAT), water drained upper limit (DUL) and lower limit (DLL), characterize soil water-holding capacity, thus are important for plant growth and carbon and nutrient cycling in grassland ecosystem. However, we are still unclear to what a content these soil water retention parameters may regulate plant production and soil organic matter content in natural ecosystems in semi-arid grassland environment. We investigated plant species richness (SR) and aboveground biomass (AGB), soil organic carbon (SOC) and nitrogen (SON) contents, and soil water retention and other physical parameters in the steppe ecosystems along a climate transect, which spans the desert steppe, typical steppe and meadow steppe zones in Northern China. We analyzed the relative importance of climate factors, soil water retention parameters and other soil physical parameters in regulating plant AGB and SOC and SON contents using partial correlation analysis, and quantified the contribution of each separate and combined climate and soil physical parameters in predicting plant AGB and SOC and SON contents using geographic detectors method. We found that (i) plant SR and AGB, soil SAT and DUL, and SOC and SON are all significantly inter-correlated, and increased with the increase of climate humidity. (ii) Climate, but not soil physical parameters, was the most preponderant factor in predicting plant SR and AGB. (iii) Soil physical parameters were more important than climate parameters in regulating soil organic matter content; soil DUL was the best predictor to SOC content. Our results suggest that soil water retention parameters especially soil DUL, may play a greater role than climate factors in modulating SOC content, though climate is more predominant in regulating plant SR and production. Our results imply that we need to be cautious in estimating SOC content based on climate factors and vegetation biomass or remotely-sensed vegetation indices, and that soil physical properties need also to be incorporated along with vegetation parameters for improving SOC estimation at a regional scale.
... There are many studies that estimate the SOC content based on climate humidity, or on plant AGB or remotely-sensed vegetation index such NDVI (Croft et al., 2012;Sun et al., 2019;, and little attention is paid to the effects of soil properties such as DUL or sand % on the SOC content in different climate zones. Our results suggest that soil water retention parameters modulate the relations between climate and vegetation biomass or SOC content, and DUL is a better predictor than climate humidity for predicting SOC content, and it is necessary to incorporate soil physical and hydraulic parameters for the improvement of the prediction of SOC and plant production in semi-arid steppes. ...
Article
Full-text available
Soil hydraulic properties determine water transport, distribution and storage in soils, thus affect ecosystem functioning. Soil water retention parameters, such as saturation water content (SAT), water drained upper limit (DUL) and lower limit (DLL), characterize soil water-holding capacity, thus are important for plant growth and carbon and nutrient cycling in grassland ecosystem. However, we are still unclear to what a content these soil water retention parameters may regulate plant production and soil organic matter content in natural ecosystems in semi-arid grassland environment. We investigated plant species richness (SR) and aboveground biomass (AGB), soil organic carbon (SOC) and nitrogen (SON) contents, and soil water retention and other physical parameters in the steppe ecosystems along a climate transect, which spans the desert steppe, typical steppe and meadow steppe zones in Northern China. We analyzed the relative importance of climate factors, soil water retention parameters and other soil physical parameters in regulating plant AGB and SOC and SON contents using partial correlation analysis, and quantified the contribution of each separate and combined climate and soil physical parameters in predicting plant AGB and SOC and SON contents using geographic detectors method. We found that (i) plant SR and AGB, soil SAT and DUL, and SOC and SON are all significantly inter-correlated, and increased with the increase of climate humidity. (ii) Climate, but not soil physical parameters, was the most preponderant factor in predicting plant SR and AGB. (iii) Soil physical parameters were more important than climate parameters in regulating soil organic matter content; soil DUL was the best predictor to SOC content. Our results suggest that soil water retention parameters especially soil DUL, may play a greater role than climate factors in modulating SOC content, though climate is more predominant in regulating plant SR and production. Our results imply that we need to be cautious in estimating SOC content based on climate factors and vegetation biomass or remotely-sensed vegetation indices, and that soil physical properties need also to be incorporated along with vegetation parameters for improving SOC estimation at a regional scale.
... Compared to other SOC remote sensing prediction models, the established models accounted for the impact of land-use types on SOC sensitive bands, and thereby improved the model precision. Several studies have confirmed that the use of ancillary data can improve the accuracy of soil prediction models [74,75]. ...
Article
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Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R2 > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg−1 in 1990 to 9.23 gC·kg−1 in 2010 and then declined to 5.31 gC·Kg−1 in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.
... Few studies use satellite sensors for SOC estimation [59]. However, the soil and the above-ground environment can be closely related allowing us to understand the biological, chemical and physical processes that govern the soil functions [60]. ...
Article
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Background Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador. Results Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R² of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R² of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature. Conclusions Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling.
... The successful application of satellite RS images in predicting agricultural SOC has been proved in much research at the regional, national and global scale (Croft et al., 2012;Dvornikov et al., 2021;Hamzehpour et al., 2019;Mirzaee et al., 2016;Paul et al., 2020;Zhou et al., 2020a The relative importance of prediction features is presented in Fig. 8. Only 24 variables (10 features derived from S-2 and 14 features derived S-1) out of 48 variables were shown the high relative importance in the agricultural SOC. ...
Article
Monitoring agricultural soil organic carbon (SOC) has played an essential role in sustainable agricultural management. Precise and robust prediction of SOC greatly contributes to carbon neutrality in the agricultural industry. To create more knowledge regarding the ability of remote sensing to monitor carbon soil, this research devises a state-of-the-art low cost machine learning model for quantifying agricultural soil carbon using active and ensemble-based decision tree learning combined with multi-sensor data fusion at a national and world scale. This work explores the use of Sentinel-1 (S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and an innovative machine learning (ML) approach using an integration of active learning for land-use mapping and advanced Extreme Gradient Boosting (XGBoost) for robustness of the SOC estimates. The collected soil samples from a field survey in Western Australia were used for the model validation and indicators including the coefficient of determination (R2) and root - mean – square - error (RMSE) were applied to evaluate the model’s performance. A numerous features computed from optical and SAR data fusion were employed to build and test the proposed model performance. The effectiveness of the proposed machine learning model was assessed by comparing with the two well-known algorithms such as Random Forests (RF) and Support Vector Machine (SVM) to predict agricultural SOC. Results suggest that a combination of S1 and S2 sensors could effectively estimate SOC in farming areas by using ML techniques. Satisfactory accuracy of the proposed XGBoost with optimal features was achieved the highest performance (R2 = 0.870; RMSE= 1.818 tonC/ha) which outperformed RF and SVM. Thus, multi-sensor data fusion combined with the XGBoost lead to the best prediction results for agricultural SOC at 10 m spatial resolution. The innovative method described here could contribute significantly to various agricultural SOC retrieval studies globally. What makes this possible is its high prediction performance at 10 m spatial resolution and cost-effectiveness with reliable free-of-charge optical and SAR data acquired from Copernicus Open Access Hub.
... The authors [8] argue that SOC dynamics affect soil quality, agricultural productivity and CO2 concentration in the atmosphere. Despite the need for spatial estimates of SOC content over time, reliable estimates using traditional field research methods are limited by data availability because measurements are often made at discrete points, large sample spacing, or confined spaces. ...
Article
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The article solves the urgent problem of developing a system for remote monitoring of the soil conditions of grape agrocenoses in the southern regions of Russia. It is shown that such monitoring is required to be carried out constantly because of the high anthropogenic load and critical decrease in soil fertility. Experimental studies showing the possibility of satellite remote monitoring of the soil conditions have been carried out, while the accuracy of determining the humus content in the soil when shooting with a camera in a laboratory and using satellite images obtained with multispectral equipment is no worse than 10-20 percent compared to chemical laboratory methods. For the experiment, 22 experimental plots, the condition of the soil on which was determined in the laboratory according to Tyurin and by shooting and processing images of a high-resolution digital camera, were laid. Synchronous data from the Russian satellite Kanopus-V were studied for the same areas. Analytical dependences of the humus content in the soil on the brightness level of the red spectral channel were obtained. The structure of the information system for remote satellite monitoring of the soil conditions in the region is proposed.
... This characteristic plays an important role in soil mapping. The shorter the revisit time, the greater is the number of images that can be obtained and, therefore, the soil will be better monitored over time (Croft et al., 2012). Thus, the discussion about these three components of an image, has gain great importance to understand soil variability. ...
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Soil maps at appropriate scales can aid decision-making in agriculture and the environment. In this sense, remote sensing products have shown their power to investigate soil properties, but the assessment of its spatial infor-mation can be hampered by the presence of other objects than soil. To overcome this issue, soil scientists have been studying spatial patterns from multi-temporal satellite images aiming at at improving soil property maps. In this work, we applied the cubist algorithm to predict topsoil properties (clay, sand, organic matter and iron contents, and soil color components) in south-eastern Brazil using multi-temporal (Landsat8-OLI, and Sentinel2- MSI) and single-date images (PlanetScope, Landsat8-OLI, and Sentinel2-MSI). We aimed to evaluate the influence of satellite’s spatial, spectral and temporal resolutions on soil mapping. Predictive models were constructed with 120 soil samples and using four (vis-NIR) and six (vis-NIR-SWIR) spectral bands as predictors in a 10-fold cross- validation procedure. The multi-temporal image obtained from the Sentinel2-MSI satellite (with 10 m pixel size and six spectral bands), showed the best model performances in cross-validation (R2 between 0.48 and 0.78). The PlanetScope image, which has only four bands in the vis-NIR region and spatial resolution of 3 m did not improve model performances, although the R2 values were higher for soil color components (R2 >0.5). Satellite images in different spatial, spectral and temporal resolution provides slightly different soil property maps which may promote different strategies regarding soil classification and management. However, satellite images should be used with caution, as they provide only surficial information about the soil variability and confirmation with field surveys is required.
... This reduces errors in quantifying soil carbon and other key properties that are often caused by spatial heterogeneity of soils. Infrared data can be integrated with geostatistic data (Cobo et al., 2010), remote sensing data and topographic information for digital soil mapping at the landscape level (Croft et al., 2012). Rossel et al. (2014), for instance, used infrared data to develop a soil carbon map of Australia. ...
... Soil physic and chemical properties, land control measures, tree species planted, vegetation types and environmental factors determine the change degree of SOC stocks and the timing of the switch between increase and decrease of stocks (Arai and Tokuchi, 2010;Don et al., 2011). In addition, it is also affected by the following factors, such as interactions between geochemistry and climate (i.e., precipitation and temperature) (Doetterl et al., 2015), previous land use type (England et al., 2016;van Straaten et al., 2015v), erosion processes (Croft et al., 2012), and whether the study design is based on a chrono sequence or time series (Fujisaki et al., 2015). However, different criteria of the research blocks, such as study design and scale, stock calculation method, and land management measures, which caused the considerable uncertainties in the SOC stock dynamics (Grinand et al., 2017). ...
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In situ or on-the-go visible and near infrared (VisNIR) diffuse reflectance spectroscopy has been proposed as a rapid and inexpensive tool for intensively mapping soil texture and organic carbon (SOC). While lab-based VisNIR has been established as a viable technique for estimating various soil properties, few experiments have compared the predictive accuracy of on-the-go and lab-based VisNIR. In this study, eight north central Montana wheat fields were intensively interrogated using on-the-go and lab-based VisNIR. The on-the-go VisNIR system employed a spectrophotometer (350–2224nm, 8-nm spectral resolution) built into an agricultural shank mounted on a toolbar and pulled behind a tractor. Regional (whole-field out cross-validation) and hybrid (regional model including randomly chosen “local” calibration samples) spectral models were calibrated using partial least squares regression. Lab-based spectral data consistently provided more accurate predictions than on-the-go data. However, neither in situ nor lab-based spectroscopy yielded even semi-quantitative SOC predictions. For hybrid models with nine local samples included in the calibrations, standard error of prediction (SEP) values were 2.6 and 3.4gkg−1 for lab and on-the-go VisNIR respectively, with σSOC=3.2gkg−1. With an SOC coefficient of variation (CV)=26.7%, even with a relatively low SEP values, there was little SOC variability to explain. For clay content, hybrid-7 calibrations yielded lab SEP=53.1gkg−1 and residual product differential (RPD)=1.8 with on-the-go SEP=69.4gkg−1 and RPD=1.4. With more variability (σclay=91.4gkg−1 and CV=49.6%), both lab and on-the-go VisNIR show better explanatory power. There a number of potential explanations for degraded on-the-go predictive accuracy: soil heterogeneity, field moisture, consistent sample presentation, and a difference between the spatial support of on-the-go measurements and soil samples collected for laboratory analyses. In terms of predictive accuracy, our results are largely consistent with those previously published by Christy (2008), but on-the-go VisNIR was not able to capture the subtle SOC variability in Montana soils. Though the current configuration of the Veris on-the-go VisNIR system allows for rapid field scanning, on-the-go soil processing (i.e. drying, crushing, and sieving) could improve predictions.
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