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

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

Improving soil organic carbon predictions from a Sentinel–2 soil composite by assessing surface conditions and uncertainties

Soil organic carbon (SOC) prediction from remote sensing is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non–photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building reflectance composites using time series of images. Although composite imagery has demonstrated its potential in SOC prediction, it is still not well established if the resulting composite spectra mirror the reflectance fingerprint of the optimal conditions to predict topsoil properties (i.e. a smooth, dry and bare soil). We have collected 303 photos of soil surfaces in the Belgian loam belt where five main classes of surface conditions were distinguished: smooth seeded soils, soil crusts, partial cover by a growing crop, moist soils and crop residue cover. Reflectance spectra were then extracted from the Sentinel–2 images coinciding with the date of the photos. After the growing crop was removed by an NDVI < 0.25, the Normalized Burn Ratio (NBR2) was calculated to characterize the soil surface, and a threshold of NBR2 < 0.05 was found to be able to separate dry bare soils from soils in unfavorable conditions i.e. wet soils and soils covered by crop residues. Additionally, we found that normalizing the spectra (i.e. dividing the reflectance of each band by the mean reflectance of all spectral bands) allows for cancelling the albedo shift between soil crusts and smooth soils in seed–bed conditions. We then built the exposed soil composite from Sentinel–2 imagery for southern Belgium and part of Noord-Holland and Flevoland in the Netherlands (covering the spring periods of 2016–2021). We used the mean spectra per pixel to predict SOC content by means of a Partial Least Squares Regression Model (PLSR) with 10–fold cross–validation. The uncertainty of the models was assessed via the prediction interval ratio (PIR). The cross validation of the model gave satisfactory results (mean of 100 bootstraps: model efficiency coefficient (MEC) = 0.48 ± 0.07, RMSE = 3.5 ± 0.3 g C kg–1, RPD = 1.4 ± 0.1 and RPIQ = 1.9 ± 0.3). The resulting SOC prediction maps show that the uncertainty of prediction decreases when the number of scenes per pixel increases, and reaches a minimum when at least six scenes per pixel are used (mean PIR of all pixels is 12.4 g C kg–1, while mean SOC predicted is 14.1 g C kg–1). The results of a validation against an independent data set showed a median difference of 0.5 g C kg–1 ± 2.8 g C kg–1 SOC between the measured (average SOC content 13.5 g C kg–1) and predicted SOC contents at field scale. Overall, this compositing method shows both realistic within field and regional SOC patterns

Relevance of UAV and sentinel-2 data fusion for estimating topsoil organic carbon after forest fire

[EN] The evaluation at detailed spatial scale of soil status after severe fires may provide useful information on the recovery of burned forest ecosystems. Here, we aimed to assess the potential of combining multispectral imagery at different spectral and spatial resolutions to estimate soil indicators of burn severity. The study was conducted in a burned area located at the northwest of the Iberian Peninsula (Spain). One month after fire, we measured soil burn severity in the field using an adapted protocol of the Composite Burn Index (CBI). Then, we performed soil sampling to analyze three soil properties potentially indicatives of fire-induced changes: mean weight diameter (MWD), soil moisture content (SMC) and soil organic carbon (SOC). Additionally, we collected post-fire imagery from the Sentinel-2A MSI satellite sensor (10–20 m of spatial resolution), as well as from a Parrot Sequoia camera on board an unmanned aerial vehicle (UAV; 0.50 m of spatial resolution). A Gram-Schmidt (GS) image sharpening technique was used to increase the spatial resolution of Sentinel-2 bands and to fuse these data with UAV information. The performance of soil parameters as indicators of soil burn severity was determined trough a machine learning decision tree, and the relationship between soil indicators and reflectance values (UAV, Sentinel-2 and fused UAV-Sentinel-2 images) was analyzed by means of support vector machine (SVM) regression models. All the considered soil parameters decreased their value with burn severity, but soil moisture content, and, to a lesser extent, soil organic carbon discriminated at best among soil burn severity classes (accuracy = 91.18 %; Kappa = 0.82). The performance of reflectance values derived from the fused UAV-Sentinel-2 image to monitor the effects of wildfire on soil characteristics was outstanding, particularly for the case of soil organic carbon content (R2 = 0.52; RPD = 1.47). This study highlights the advantages of combining satellite and UAV images to produce spatially and spectrally enhanced images, which may be relevant for estimating main impacts on soil properties in burned forest areas where emergency actions need to be applied.S

Soil Reflectance Composites - Improved Thresholding and Performance Evaluation

Reflectance composites that capture bare soil pixels from multispectral image data are increasingly being analysed to model soil constituents such as soil organic carbon. These temporal composites are used instead of single-date multispectral images to account for the frequent vegetation cover of soils and, thus, to get broader spatial coverage of bare soil pixels. Most soil compositing techniques require thresholds derived from spectral indices such as the Normalised Difference Vegetation Index (NDVI) and the Normalised Burn Ratio 2 (NBR2) to separate bare soils from all other land cover types. However, the threshold derivation is handled based on expert knowledge of a specific area, statistical percentile definitions or in situ data. For operational processors, such site-specific and partly manual strategies are not applicable. There is a need for a more generic solution to derive thresholds for large-scale processing without manual intervention. This study presents a novel HIstogram SEparation Threshold (HISET) methodology deriving spectral index thresholds and testing them for a Sentinel-2 temporal data stack. The technique is spectral indexindependent, data-driven and can be evaluated based on a quality score. We tested HISET for building six soil reflectance composites (SRC) using NDVI, NBR2 and a new index combining the NDVI and a short-wave infrared (SWIR) band (PV+IR2). A comprehensive analysis of the spectral and spatial performance and accuracy of the resulting SRCs proves the flexibility and validity of HISET. Disturbance effects such as spectral confusion of bare soils with non-photosynthetic-active vegetation (NPV) could be reduced by choosing grassland and crops as input LC for HISET. The NBR2-based SRC spectra showed the highest similarity with LUCAS spectra, the broadest spatial coverage of bare soil pixels and the least number of valid observations per pixel. The spatial coverage of bare soil pixels is validated against the database of the Integrated Administration and Control System (IACS) of the European Commission. Validation results show that PV+IR2-based SRCs outperform the other two indices, especially in spectrally mixed areas of bare soil, photosynthetic-active vegetation and NPV. The NDVI-based SRCs showed the lowest confidence values (95%) in all bands. In the future, HISET shall be tested in other areas with different environmental conditions and LC characteristics to evaluate if the findings of this study are also valid

Assessing Agave sisalana biomass from leaf to plantation level using field measurements and multispectral satellite imagery

Biomassa, eli kasviaineksen määrä, on tärkeä muuttuja viljelykasvien kasvun seurannassa sekä arvioitaessa hiilen kiertoa. Kenttätöissä biomassaa voidaan arvioida kasveja vahingoittamatta hyödyntämällä allometrisia malleja. Suuremmassa mittakaavassa biomassaa voidaan kartoittaa kaukokartoitusmenetelmillä. Tässä tutkimuksessa arvioitiin Agave sisalanan eli sisalin lehtien kuivaa biomassaa. Sisal on trooppisilla ja subtrooppisilla alueilla viljeltävä monivuotinen kasvi, jonka lehdistä tuotetaan kuitua ja biopolttoainetta. Lehtibiomassan arvioimiseksi luotiin ensin allometrinen malli, minkä jälkeen biomassa mallinnettiin 8851 hehtaarin plantaasille Kaakkois-Keniassa käyttämällä Sentinel-2 multispektraalista satellittikuva-aineistoa. Allometrista mallia varten kerättiin 38:n lehden otos. Kasvin korkeuden ja lehden suurimman ympärysmitan avulla muodostettiin tilavuusarvio, jonka yhteyttä biomassaan mallinnettiin lineaarisella regressiolla. Muuttujien välille löytyi vahva log-log lineaarinen yhteys ja ristiinvalidointi osoitti, että mallin ennusteet ovat tarkkoja (R2 = 0.96, RMSE = 7.69g). Mallin avulla ennustettiin lehtibiomassa 58:lle koealalle, jotka muodostivat otoksen biomassan mallinnukseen Sentinel-2 kuvalla. Mallinnuksessa käytettiin yleistettyjä additiivisia malleja, joiden avulla tutkittiin lukuisten spektraalisten kasvillisuusindeksien yhteyttä biomassaan. Parhaaksi osoittautuivat indeksit, jotka laskettiin hyödyntämällä vihreää ja lähi-infrapunakanavaa, sekä ns. ”red-edge”-kanavia (D2 = 74%, RMSE = 4.96 Mg/ha). Keskeisin mallin selitysastetta heikentävä tekijä vaikutti olevan suuresti vaihteleva aluskasvillisuuden määrä. Hyödyntämällä parhaaksi todettua kasvillisuusindeksiä lehtibiomassa mallinnettiin koko plantaasin peltoalalle. Biomassa vaihteli 0 ja 45.1 Mg/ha välillä, keskiarvon ollessa 9.9 Mg/ha. Tämän tutkimuksen tuloksena syntyi allometrinen malli, jota voidaan käyttää sisalin lehtibiomassan arviointiin. Jatkotutkimuksissa tulisi ottaa huomioon myös kasvin muut osat, kuten varsi ja juuret. Biomassan mallinnus multispektraalisilla kasvillisuusindekseillä osoitti menetelmän toimivuuden sisalin biomassan kartoituksessa, mutta vaihtelevan aluskasvillisuuden todettiin heikentävän mallin suorituskykyä. Aluskasvillisuuden vaikutusta ja täydentäviä aineistolähteitä tulisi tutkia tulevaisuudessa. Plantaasin lehtibiomassan, ja näin ollen maanpäälle sitoutuneen hiilen määrä, on saman suuruinen, kuin alueen luonnollisella pensassavannilla. Sisal-plantaasin hiilen kierron kokonaisvaltainen ymmärtäminen vaatii kuitenkin lisätietoa kasvien ja maaperän hiilivuosta sekä maaperän hiilensitomisesta.Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation

Assessment and mapping of soil water repellency using remote sensing and prediction of its effect on surface runoff and phosphorus losses : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

The soil water repellency spatial and temporal dynamics remain ambiguous. Water repellency is an inherent soil property that refers to the impedance in dry soil wetting. This phenomenon was ascribable to the hydrophobic compounds coating the soil particles and has emerged as a recalcitrant issue impacting multiple processes upon agroecosystems. The apprehensions around soil water repellency include its impact on surface runoff, plant growth, and nutrients losses (e.g. phosphorus). The soil hydrophobic compounds, which are intrinsic constituents of the soil carbon pool, have different sources including plant leaves and roots, soil microbial communities and fungi. Previous methods for water repellency measurements are laborious, time-consuming and costly. The raison d'être of this thesis was to i) explore and test novel approaches for estimation of soil water repellency in pastoral ecosystems, and ii) study the factors controlling soil water repellency and assess its impact on surface runoff volumes and phosphorus losses in surface runoff. In the present work, multiple remote sensing approaches were tested to assess and map soil water repellency at multiple scales. The liaison between water repellency and soil surface reflectance was exploited to access the water repellency using the satellite multispectral reflectance and hyperspectral satellite data. A novel approach implicating the use of time series of surface reflectance and water deficit data was used to study the impact of both surface biomass and soil moisture temporal dynamics on the occurrence of water repellency and carbon content in pastoral systems. Multispectral broadband data from both Landsat-7 and Sentinel-2 satellites showed big potential for assessing soil water repellency and carbon content in permanent pastures. Partial least square regression models were calibrated and cross-validated using topsoil measurement of water repellency and soil carbon from 41 and 35 pastoral sites that were matched with reflectance spectra from Landsat-7 and Sentinel-2, respectively. Soil carbon showed higher predictability compared to water repellency with R2v=0.50, RMSEv=2.58 when using Landsat-7 spectra. The higher predictability performance for water repellency persistence was reached using Sentinel-2 spectral (R2v=0.45; RMSEv=0.98). However, using hyperspectral narrowband data from the Hyperion satellite showed a higher prediction accuracy (R2v=0.78; RMSEv=0.58). Prediction performance was generally higher when using the calibration sets, indicating the possibility of improving these prediction models when using larger datasets. A novel approach was tested using multiple predictors for soil water repellency occurrence. The predictors included time series of surface biomass assessed through normalised difference vegetation index (NDVI) and soil moisture data estimated through water deficit and synthetic aperture radar satellite data. The results showed an attractive opportunity for water repellency and soil carbon mapping. Three machine learning algorithms including artificial neural networks, random forest, and support vector machine were trained and cross-validated using multiple configurations of satellite time-series data and topsoil measurement from 58 pastoral sites. Random forest and support vector machine (RMSEv=0.82 and 0.87, respectively) outperformed artificial neural networks (RMSEv=1.23). With increasingly available remote sensing data, the use of satellite time-series data will open unprecedented opportunities for soil carbon, water repellency mapping, and potentially other functional chemical and physical soil attributes. To understand water repellency dynamics and evaluate their impact on surface runoff and phosphorus losses in pastoral soils, two experiments were conducted. The first experiment aimed to understand the relationship between the actual water repellency persistence and water content in drying hydrophobic soils. The second experiment had the objective to evaluate the impact of soil water repellency on the surface runoff and phosphorus losses in runoff. Results from the first experiment showed that the actual water repellency increased dramatically when water content decreased, especially when moisture dropped below a critical value. Using lab measurements, the actual water repellency was modelled using a simple sigmoidal model, as a function of water content, the potential water repellency, and two characteristic parameters related to the response curve shape. Results from the runoff trial showed that the surface runoff was influenced by soil water repellency to some extent (R2=0.46). Although more than 90 % of phosphorus losses happened in incidental losses following fertiliser application, the data point to non-incidental phosphorus loads being related to soil water repellency (R2=0.56). These results bespoke the effect of soil water repellency on background phosphorus losses through surface runoff during post-summer runoff events in pastoral ecosystems

The application of deep learning for remote sensing of soil organic carbon stocks distribution in South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Soil organic carbon (SOC) is a vital measure for ecosystem health and offers opportunities to understand carbon fluxes and associated implications. However, unprecedented anthropogenic disturbances have significantly altered SOC distribution across the globe, leading to considerable carbon losses. In addition, reliable SOC estimates, particularly over large spatial extents remain a major challenge due to among others limited sample points, quality of simulation data and suitable algorithms. Remote sensing (RS) approaches have emerged as a suitable alternative to field and laboratory SOC determination, especially at large spatial extent. Nevertheless, reliable determination of SOC distribution using RS data requires robust analytical approaches. Compared to linear and classical machine learning (ML) models, deep learning (DL) models offer a considerable improvement in data analysis due to their ability to extract more representative features and identify complex spatial patterns associated with big data. Hence, advancements in remote sensing, proliferation of big data, and deep learning architecture offer great potential for large-scale SOC mapping. However, there is paucity in literature on the application of DL-based remote sensing approaches for SOC prediction. To this end, this study is aimed at exploring DL-based approaches for the remote sensing of SOC stocks distribution across South Africa. The first objective sought to provide a synopsis of the use of traditional neural network (TNN) and DL-based remote sensing of SOC with emphasis on basic concepts, differences, similarities and limitations, while the second objective provided an in-depth review of the history, utility, challenges, and prospects of DL-based remote sensing approaches for mapping SOC. A quantitative evaluation between the use of TNN and DL frameworks was also conducted. Findings show that majority of published literature were conducted in the Northern Hemisphere while Africa have only four publications. Results also reveal that most studies adopted hyperspectral data, particularly spectrometers as compared to multispectral data. In comparison to DL (10%), TNN (90%) models were more commonly utilized in the literature; yet, DL models produced higher median accuracy (93%) than TNN (85%) models. The review concludes by highlighting future opportunities for retrieving SOC from remotely sensed data using DL frameworks. The third objective compared the accuracy of DL—deep neural network (DNN) model and a TNN—artificial neural network (ANN), as well as other popular classical ML models that include random forest (RF) and support vector machine (SVM), for national scale SOC mapping using Sentinel-3 data. With a root mean square error (RMSE) of 10.35 t/ha, the DNN model produced the best results, followed by RF (11.2 t/ha), ANN (11.6 t/ha), and SVM (13.6 t/ha). The DNN's analytical abilities, combined with its capacity to handle large amounts of data is a key advantage over other classical ML models. Having established the superiority of DL models over TNN and other classical models, the fourth objective focused on investigating SOC stocks distribution across South Africa’s major land uses, using Deep Neural Networks (DNN) and Sentinel-3 satellite data. Findings show that grasslands contributed the most to overall SOC stocks (31.36 %), while urban vegetation contributed the least (0.04%). Results also show that commercial (46.06 t/h) and natural (44.34 t/h) forests had better carbon sequestration capacity than other classes. These findings provide an important guideline for managing SOC stocks in South Africa, useful in climate change mitigation by promoting sustainable land-use practices. The fifth objective sought to determine the distribution of SOC within South Africa’s major biomes using remotely sensed-topo-climatic data and Concrete Autoencoder-Deep Neural Networks (CAE-DNN). Findings show that the CAE-DNN model (built from 26 selected variables) had the best accuracy of the DNNs examined, with an RMSE of 7.91 t/h. Soil organic carbon stock was also shown to be related to biome coverage, with the grassland (32.38%) and savanna (31.28%) biomes contributing the most to the overall SOC pool in South Africa. forests (44.12 t/h) and the Indian ocean coastal belt (43.05 t/h) biomes, despite having smaller footprints, have the highest SOC sequestration capacity. To increase SOC storage, it is recommended that degraded biomes be restored; however, a balance must be maintained between carbon sequestration capability, biodiversity health, and adequate provision of ecosystem services. The sixth objective sought to project the present SOC stocks in South Africa into the future (i.e. 2050). Soil organic carbon variations generated by projected climate change and land cover were mapped and analysed using a digital soil mapping (DSM) technique combined with space-for-time substitution (SFTS) procedures over South Africa through 2050. The potential SOC stocks variations across South Africa's major land uses were also assessed from current (2021) to future (2050). The first part of the study uses a Deep Neural Network (DNN) to estimate current SOC content (2021), while the second phase uses an average of five WorldClim General Circulation Models to project SOC to the future (2050) under four Shared Socio-economic Pathways (SSPs). Results show a general decline in projected future SOC stocks by 2050, ranging from 4.97 to 5.38 Pg, compared to estimated current stocks of 5.64 Pg. The findings are critical for government and policymakers in assessing the efficacy of current management systems in South Africa. Overall, this study provides a cost-effective framework for national scale mapping of SOC stocks, which is the largest terrestrial carbon pool using advanced DL-based remote sensing approach. These findings are valuable for designing appropriate management strategies to promote carbon uptake, soil quality, and measuring terrestrial ecosystem responses and feedbacks to climate change. This study is also the first DL-based remote sensing of SOC stocks distribution in South Africa

Combining Field and Imaging Spectroscopy to Map Soil Organic Carbon in a Semiarid Environment

Semiarid regions are especially vulnerable to climate change and human-induced land-use changes and are of major importance in the context of necessary carbon sequestration and ongoing land degradation. Topsoil properties, such as soil carbon content, provide valuable indicators to these processes, and can be mapped using imaging spectroscopy (IS). In semiarid regions, this poses difficulties because models are needed that can cope with varying land surface and soil conditions, consider a partial vegetation coverage, and deal with usually low soil organic carbon (SOC) contents. We present an approach that aims at addressing these difficulties by using a combination of field and IS to map SOC in an extensively used semiarid ecosystem. In hyperspectral imagery of the HyMap sensor, the influence of nonsoil materials, i.e., vegetation, on the spectral signature of soil dominated image pixels was reduced and a residual soil signature was calculated. The proposed approach allowed this procedure up to a vegetation coverage of 40% clearly extending the mapping capability. SOC quantities are predicted by applying a spectral feature-based SOC prediction model to image data of residual soil spectra. With this approach, we could significantly increase the spatial extent for which SOC could be predicted with a minimal influence of a vegetation signal compared to previous approaches where the considered area was limited to a maximum of, e.g., 10% vegetation coverage. As a regional example, the approach was applied to a 320 km2 area in the Albany Thicket Biome, South Africa, where land cover and landuse changes have occurred due to decades of unsustainable land management. In the generated maps, spatial SOC patterns were interpreted and linked to geomorphic features and land surface processes, i.e., areas of soil erosion. It was found that the chosen approach supported the extraction of soil-related spectral image information in the semiarid region with highly varying land cover. However, the quantitative prediction of SOC contents revealed a lack in absolute accuracy