Estimating soil organic carbon from soil reflectance: a review

Ladoni, Moslem; Bahrami, H A; Alavipanah, S K; Norouzi, Ali Akbar

doi:10.1007/s11119-009-9123-3

Cited by 138 publications

(91 citation statements)

References 98 publications

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“…It was reported that there are necessarily functional linkages among plant biomass, plant residues and SOM. SOM correlates closely with soil quality (Reeves, 1997;Al-Kaisi et al, 2005), which influences plant available nutrients and the ability of soil in absorbing plant nutrients (Chen et al, 2005;Ladoni et al, 2010). Both of them primarily determine plant photosynthetic capacity (Chapin et al, 2011;Janssens et al, 2001) and plant biomass over long term periods.…”

Section: Biological Basis Of the Rersmmentioning

confidence: 99%

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Gao

et al. 2015

Ecological Modelling

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a b s t r a c tEcosystem respiration (R e ) is rarely quantified from remote sensing data because satellite technique is incapable of observing the key processes associated with soil respiration. In this study, we develop a Remote Sensing Model for R e (ReRSM) by assuming that one part of R e is derived from current photosynthate with the respiratory rate coupling closely with gross primary production (GPP), and the other part of R e is derived from reserved ecosystem organic matter (including plant biomass, plant residues and soil organic matter) with the respiratory rate responding strongly to temperature change. The ReRSM is solely driven by the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI) and the Land Surface Temperature (LST) from MODIS data. Multi-year eddy CO 2 flux data of five vegetation types in Northern China and the Tibetan Plateau (including temperate mixed forest, temperate steppe, alpine shrubland, alpine marsh and alpine meadow-steppe) were used for model parameterization and validation. In most cases, the simulated R e agreed well with the observed R e in terms of seasonal and interannual variation irrespective of vegetation types. The ReRSM could explain approximately 93% of the variation in the observed R e across five vegetation types, with the root mean square error (RMSE) of 0.04 mol C m −2 d −1 and the modeling efficiency (EF) of 0.93. Model comparison showed that the performance of the ReRSM was comparable with that of the RECO in the studied five vegetation types, while the former had much fewer parameters than the latter. The ReRSM parameters showed good linear relationships with the mean annual satellite indices. With these linear functions, the ReRSM could explain approximately 90% of the variation in the observed R e across five vegetation types, with the RMSE of 0.05 mol C m −2 d −1 and the EF of 0.89. These analyses indicated that the ReRSM is a simple and alternative approach in R e estimation and has the potential of estimating spatial R e . However, the performance of ReRSM in other vegetation types or regions still needs a further study.

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Section: Biological Basis Of the Rersmmentioning

confidence: 99%

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Gao

et al. 2015

Ecological Modelling

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“…The main properties modulating the reflectance of bare ground are the different mineral components (the underlying parent material) and soil organic matter (SOM) (Ladoni et al, 2010). The relationship between SOM and reflectance is often assumed to be linear, with SOM being negatively correlated to reflectance up to a saturation limit of around 5% SOM in the top soil layer (Curran, 1985;Ladoni et al, 2010). The maximum change in VIS and NIR reflectance due to SOM (a · C lim , in Eq.…”

Section: Dynamic Background Albedomentioning

confidence: 99%

The effect of a dynamic background albedo scheme on Sahel/Sahara precipitation during the mid-Holocene

2011

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Abstract.We have implemented a new albedo scheme that takes the dynamic behaviour of the surface below the canopy into account, into the land-surface scheme of the MPI-ESM. The standard (static) scheme calculates the seasonal canopy albedo as a function of leaf area index, whereas the background albedo is a gridbox constant derived from satellite measurements. The new (dynamic) scheme additionally models the background albedo as a slowly changing function of organic matter in the ground and of litter and standing dead biomass covering the ground. We use the two schemes to investigate the interactions between vegetation, albedo and precipitation in the Sahel/Sahara for two time-slices: preindustrial and mid-Holocene. The dynamic scheme represents the seasonal cycle of albedo and the correspondence between annual mean albedo and vegetation cover in a more consistent way than the static scheme. It thus gives a better estimate of albedo change between the two time periods. With the introduction of the dynamic scheme, precipitation is increased by 30 mm yr −1 for the pre-industrial simulation and by about 80 mm yr −1 for the mid-Holocene simulation. The present-day dry bias in the Sahel of standard ECHAM5 is thus reduced and the sensitivity of precipitation to mid-Holocene external forcing is increased by around one third. The locations of mid-Holocene lakes, as estimated from reconstructions, lie south of the modelled desert border in both mid-Holocene simulations. The magnitude of simulated rainfall in this area is too low to fully sustain lakes, however it is captured better with the dynamic scheme. The dynamic scheme leads to increased vegetation variability in the remaining desert region, indicating a higher frequency of green spells, thus reaching a better agreement with the vegetation distribution as derived from pollen records.

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“…Soil organic carbon (SOC) has considerable influence on soil quality and plant growth, and it governs various physical, chemical and biological processes in the soil environment [1,2]. A rapid and economical method for estimating SOC content can improve environmental monitoring, modeling and precision agriculture [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

et al. 2014

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With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e., Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model (i.e., SVMR without SPA) and SPA-PLSR model (i.e., SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R 2 V = 0.73, RMSE V = 2.78 g·kg and RPD V = 1.63). Most of the spectral bands used by the SPA-SVMR model over the near-infrared region were important wavelengths for SOC content estimation. This study demonstrated that the combination of SPA and SVMR is feasible and reliable for estimating SOC content from the VIS/NIR spectra of soils in regions with multiple soil and land-use types.

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Estimating soil organic carbon from soil reflectance: a review

Cited by 138 publications

References 98 publications

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

The effect of a dynamic background albedo scheme on Sahel/Sahara precipitation during the mid-Holocene

Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

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