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Vaudour, E.;  Gholizadeh, A.;  Castaldi, F.;  Saberioon, M.;  Borůvka, L.;  Urbina-Salazar, D.;  Fouad, Y.;  Arrouays, D.;  Richer-De-Forges, A.C.;  Biney, J.; et al. Satellites Spectral Information and Soil Organic Carbon. Encyclopedia. Available online: https://encyclopedia.pub/entry/24557 (accessed on 03 December 2024).
Vaudour E,  Gholizadeh A,  Castaldi F,  Saberioon M,  Borůvka L,  Urbina-Salazar D, et al. Satellites Spectral Information and Soil Organic Carbon. Encyclopedia. Available at: https://encyclopedia.pub/entry/24557. Accessed December 03, 2024.
Vaudour, Emmanuelle, Asa Gholizadeh, Fabio Castaldi, Mohammadmehdi Saberioon, Luboš Borůvka, Diego Urbina-Salazar, Youssef Fouad, Dominique Arrouays, Anne C. Richer-De-Forges, James Biney, et al. "Satellites Spectral Information and Soil Organic Carbon" Encyclopedia, https://encyclopedia.pub/entry/24557 (accessed December 03, 2024).
Vaudour, E.,  Gholizadeh, A.,  Castaldi, F.,  Saberioon, M.,  Borůvka, L.,  Urbina-Salazar, D.,  Fouad, Y.,  Arrouays, D.,  Richer-De-Forges, A.C.,  Biney, J.,  Wetterlind, J., & Wesemael, B.V. (2022, June 28). Satellites Spectral Information and Soil Organic Carbon. In Encyclopedia. https://encyclopedia.pub/entry/24557
Vaudour, Emmanuelle, et al. "Satellites Spectral Information and Soil Organic Carbon." Encyclopedia. Web. 28 June, 2022.
Satellites Spectral Information and Soil Organic Carbon
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This entry is adapted from the peer-reviewed paper 10.3390/rs14122917

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. A number of satellite-based spectral approaches for SOC assessment have been achieved from several satellite sensors, study scales and geographical contexts in the past decade.

soil organic carbon spectral models satellite imagery

1. Satellites Spectral Information

Since 1972, i.e., the very beginning of the civilian satellite remote sensing, until the mid-2010s, satellite data available in the optical domain were mostly acquired from multispectral sensors, i.e., sensors with a discrete number of spectral bands. Most historical data were obtained over wide swaths by the Landsat satellites equipped with Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and, more recently, Landsat 8 Operational Land Imager (OLI) sensors with 30 m resolution. Additionally, data from the Satellite Pour l’Observation de la Terre (SPOT) equipped with the sensor Haute Résolution Visible (HRV) with 20 m resolution were found dating back to 1986. Some pioneering studies about topsoil SOC content detection from satellite data have been carried out, for instance, considering SPOT HRV bands [1][2][3] or Landsat bands [4][5][6].
Since 2000, hyperspectral satellite images have been made available from the Hyperion sensor with 30 m resolution onboard the satellite Earth Observing 1 (2000–2017), and from the Compact High-Resolution Imaging Spectrometer (CHRIS) with 17 m resolution onboard the Project for On-Board Autonomy (PROBA-1) micro-satellite (2001–ongoing). Since 2019, the PRecursore IperSpettrale della Missione Applicativa (PRISMA) with 239 spectral bands between 400 and 2505 nm has delivered images with 30 m resolution [7]. Approaches of satellite-based SOC modeling have been carried out from Hyperion [8][9][10], CHRIS-PROBA [11], simulated PRISMA [12] and PRISMA [13]. As the Environmental Mapping and Analysis Program (EnMAP) [14] was just launched on 1 April 2022 and is currently in the commissioning phase, some studies have considered simulated EnMAP for the assessment of SOC content [15][16] till actual EnMAP data becomes available. To our knowledge, no reference was found about simulated spectra for other forthcoming hyperspectral satellites such as CHIME, SHALOM or HypXim. Some recent Chinese studies used the hyperspectral data of the Gaofen-5 satellite with a 30 m resolution and bandwidth of 60 km [17][18][19]. In parallel, with the emerging of precision agriculture, field-scale approaches to SOC modeling have also been developed from satellite sensors with higher spatial resolution: IKONOS with 4 m resolution [20], PlanetScope with 3 m resolution [21] and Worldview 2 with 2.5 m resolution [22][23].
Since 2015 and then 2017, when Sentinel-2A, followed by Sentinel-2B were launched, the Sentinel-2 (S2) time-series equipped with the MultiSpectral Instrument (MSI, 13 spectral bands) provided not only wide spatial coverage over swaths of 290 km, but also 10 to 20 m resolution (10 spectral bands) and a 5-day revisit. The advent of such time-series favored the renewal of the satellite-derived spectral models and particularly for SOC, using either single date acquisitions [21][24][25][26][27][28][29][30][31][32] or multi-date approaches [13][33][34][35][36]. In addition, some authors used Sentinel-1 synthetic aperture radar (SAR) images in their approach, either separately [27][32][35] or directly as covariates within their modeling [32][37]. Over very large areas or at national scales, other authors used coarse resolution satellite series, being either MODIS with 250 or 500 m resolution [38][39] or Sentinel-3 equipped with the Ocean and Land Colour Instrument (OLCI) with 300 m resolution [40].

2. Overall Characteristics of Soil Data

2.1. Soil Types and Agroecosystems under Study

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, with few in Mediterranean [11][12][26][41] and arid environments [6][42][43] and even fewer in tropical ecosystems [4][36][44][45] (Figure 1).
Figure 2. World map of satellite-derived SOC studies and the dominant soil types of the FAO-UNESCO Digital Soil Map of the World at 1:5.000.000 scale [46]. Time spans are split according to the first year of Sentinel-2 based SOC studies, i.e., 2018.
Most studies were in rainfed annual cropping systems with very few studies in vineyards and even fewer in orchards. It should be noted that information on management practices or soil surface conditions was scarce.
Soil types refer to the World Reference Base (WRB) [47] in most cases, although the US Soil Taxonomy [48] was also used. The soils are typically cambisols and luvisols and, to a lesser extent, regosols, leptosols, stagnosols, chernozems and the so-called “inceptisols” of the US Soil Taxonomy (mostly equivalent to cambisols) (Figure 2). The most frequent qualifier is “haplic”. The “calcaric” qualifier is not dominant, and therefore calcium carbonate contents can be assumed to be low.
Figure 2. Word cloud of soil types considered in satellite-derived SOC published studies (word size varies with frequency).

2.2. Spatial Scales, Sample Size and Density

Most studies were carried out at the scale of small regions, of some hundreds of km2: study areas covered a median of 118 km2 (Figure 3). The sample density for small regions ranged between 0.1 and 16.1 samples per km2, with a mean value of 2.7 samples per km2. For large regions (up to 10,000 km2) and very large regions (>10,000 km2 and up to 150,000 km2), the mean sample density was lower than or equal to 0.1 samples per km2, while it was higher than or equal to 201 for farm- or field-scale studies. The total sample size ranged from 32 to 1753 topsoil samples, most of them being collected from the 0–10 cm or 0–20 cm topsoil. The median sample size varies from 85 for field and farm, to 100 for small regions, 264 for large regions and reaches 625 samples for very large regions.
Figure 3. Histogram of study scales considered in satellite-derived SOC studies.

2.3. Ranges of SOC Considered and the Issue of Standard Lab Determinations

Table 1 sheds light on the basic statistics of topsoil SOC considered in the selected studies. The datasets of measured topsoil SOC contents refer to mineral soils with annual crop systems with an average value of ~15 g·kg−1 and a range of 30 g·kg−1 in median.
Table 1. Basics stats of topsoil SOC content values (top SOC, g·kg−1) across 46 study areas with full description of measured sampled sets [3][8][11][12][13][18][21][24][25][26][27][29][31][32][34][35][38][41][44][49][50][51][52][53][54][55][56][57][58][59][60], q1, first quartile; μ, mean; q3; third quartile; σ, standard deviation.
Statistic Min q1 µ q3 Max σ Median
Minimum top SOC 0.0 2.7 5.8 6.9 26.1 4.7 6.0
Maximum top SOC 10.0 21.8 82.7 115.8 439.0 102.0 37.3
Top SOC range 4.6 17.8 76.9 110.7 438.4 103.0 30.0
Average top SOC 1.7 12.6 17.4 19.6 50.0 9.5 15.1
Mineral and organic soils are usually processed separately, but the threshold between these categories is not uniform: at the scale of the USA, Wang et al. [61] chose to discriminate between mineral and organic soils using the threshold of 120 g·kg−1.
As the analytical methods used for SOC measurements are far from being homogeneous among laboratories and countries, and specifically in the context of the satellite-derived SOC studies, the basic stats displayed in Table 1 should be considered with caution. Dry combustion was used for 50% of the studies and wet oxidation for 30%, while analytical methods were simply not specified for the remaining studies. Historically, one of the most-used methods has been the Walkley–Black method [62] using wet oxidation. This method is still being used in numerous countries, and several modified methods based on the same principles have also been proposed, e.g., [63][64][65][66]. The underestimation of the total SOC content caused by a reduced wet oxidation of the more stable or “recalcitrant” fractions of SOC is the major limitation of this method.
The modern standard method, supposed to be the reference, is dry combustion coupled with an automated CHN analyzer. Automated dry combustion (ADC) involves measurements of SOC based on CO2 released from thermally oxidized soil [67]. Therefore, a recovery factor must be used to convert the results from the wet oxidation to the dry combustion method. The most frequently used correction factor is 1.33 [68]. However, a careful look at the literature [68][69][70][71][72] shows that the recovery factors cover a wide range (1 to 1.8) depending on climate, soil types, depth, texture, and the relative proportion of various SOC constituents. Moreover, CN analyzers determine total carbon, i.e., SOC and carbonates. Thus, the SOC content of calcareous samples is determined by subtracting carbon content from carbonates to total carbon content, e.g., [73].
Other standard methods that were not found among the satellite-derived SOC studies include various adaptations of the Mehlich method [74] that aim at extracting humic substances, or loss-on-ignition [67][75], the latter being mainly used for the organic horizons.
All methods have some drawbacks, and the less biased one is ADC, with some exceptions for very organic soils. One consequence is that when using SOC data, and especially when compiling legacy SOC data, metadata should include the laboratory method. Another consequence is that there is no universal factor to convert the results from one method to another, and that such a conversion needs to be locally adapted. Finally, any change of method between two dates may lead to false conclusions about SOC changes.
Moreover, over the past decade, laboratory spectroscopic measurements have been successfully tested to predict SOC contents over the visible near-infrared and shortwave infrared ranges [76][77][78][79], in combination with UV-visible fluorescence measurements [80] or restricted to the visible range only [81], or over the mid-infrared only (4000–400 cm−1) [82]. An emergent technology is also laser-induced breakdown spectroscopy, e.g., [83]. These low-cost technologies are good candidates to provide numerous training information for digital soil mapping (DSM). However, the uncertainty of these measurements often remains a limitation, if the aim is to detect small changes in SOC with time. One emerging promising method is analyzing SOC with Rock-Eval analyses [84], which would pave the way to both estimating total SOC and indicators of its sensibility to mineralization and its potential for long-term sequestration in soils.

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Subjects: Soil Science
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Emmanuelle Vaudour
,
Asa Gholizadeh
,
Fabio Castaldi
,
Mohammadmehdi Saberioon
,
Luboš Borůvka
,
Diego Urbina-Salazar
,
Youssef Fouad
,
Dominique Arrouays
,
Anne C. Richer-de-Forges
,
James Biney
,
Johanna Wetterlind
,
Bas Van Wesemael
View Times: 1.7K
Entry Collection: Environmental Sciences
Revisions: 2 times (View History)
Update Date: 28 Jun 2022
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