Reducing the Moisture Effect and Improving the Prediction of Soil Organic Matter With VIS-NIR Spectroscopy in Black Soil Area

Yang, Tan; Jiang, Qigang; Yu, Longfei; Liu, Huaxin; Zhang, Bo

doi:10.1109/access.2020.3048794

Cited by 12 publications

(3 citation statements)

References 62 publications

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“…The position of these absorption features is inconsistently reported in the literature, however, with reports of these features centred at 1400 nm [57], 1444 nm [54], 1450 nm [58], 1470 nm [16], 1900 nm [54], 1920 nm [13], 1930 nm [58] and 1944 nm [55]. The highresolution data presented here allows close inspection of the position of these features (Figure 6b,c).…”

Section: The Relationship Between Soil MC and Reflectance-new Datacontrasting

confidence: 52%

Relationships between Soil Moisture and Visible–NIR Soil Reflectance: A Review Presenting New Analyses and Data to Fill the Gaps

McGuirk,

Cairns

2024

Geotechnics

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The ability to precisely monitor soil moisture is highly valuable in industries including agriculture and civil engineering. As soil moisture is a spatially erratic and temporally dynamic variable, rapid, cost-effective, widely applicable, and practical techniques are required for monitoring soil moisture at all scales. If a consistent numerical relationship between soil moisture content and soil reflectance can be identified, then soil spectroscopic models may be used to efficiently predict soil moisture content from proximal soil reflectance and/or remotely sensed data. Previous studies have identified a general decrease in visible–NIR soil reflectance as soil moisture content increases, however, the strength, best wavelengths for modelling, and domain of the relationship remain unclear from the current literature. After reviewing the relevant literature and the molecular interactions between water and light in the visible–NIR (400–2500 nm) range, this review presents new analyses and interprets new 1 nm resolution soil reflectance data, collected at >20 moisture levels for ten soil samples. These data are compared to the results of other published studies, extending these as required for further interpretation. Analyses of this new high-resolution dataset demonstrate that linear models are sufficient to characterise the relationship between soil moisture and reflectance in many cases, but relationships are typically exponential. Equations generalising the relationship between soil MC and reflectance are presented for a number of wavelength ranges and combinations. Guidance for the adjustment of these equations to suit other soil types is also provided, to allow others to apply the solutions presented here and to predict soil moisture content in a much wider range of soils.

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Section: The Relationship Between Soil MC and Reflectance-new Datacontrasting

confidence: 52%

Relationships between Soil Moisture and Visible–NIR Soil Reflectance: A Review Presenting New Analyses and Data to Fill the Gaps

McGuirk,

Cairns

2024

Geotechnics

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“…SOC detection performance is particularly sensitive to soil moisture [52,53]. Therefore, methods using spectral transformations have been proposed for lab measurements [53,54] and via satellites using spectral indices that generally combine infrared bands near the water response to reduce the effect of moisture [18,22,49]. Table 5 displays RPD and RPIQ values > 1.5 from 2017 and 2018.…”

Section: Impact Of Soil Moisturementioning

confidence: 99%

Using Sentinel-2 Images for Soil Organic Carbon Content Mapping in Croplands of Southwestern France. The Usefulness of Sentinel-1/2 Derived Moisture Maps and Mismatches between Sentinel Images and Sampling Dates

et al. 2021

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In agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further studied through various approaches including remote sensing. In order to predict SOC content over croplands in southwestern France (area of 22,177 km²), this study addresses (i) the influence of the dates on which Sentinel-2 (S2) images were acquired in the springs of 2017–2018 as well as the influence of the soil sampling period of a set of samples collected between 2005 and 2018, (ii) the use of soil moisture products (SMPs) derived from Sentinel-1/2 satellites to analyze the influence of surface soil moisture on model performance when included as a covariate, and (iii) whether the spatial distribution of SOC as mapped using S2 is related to terrain-derived attributes. The influences of S2 image dates and soil sampling periods were analyzed for bare topsoil. The dates of the S2 images with the best performance (RPD ≥ 1.7) were 6 April and 26 May 2017, using soil samples collected between 2016 and 2018. The soil sampling dates were also analyzed using SMP values. Soil moisture values were extracted for each sample and integrated into partial least squares regression (PLSR) models. The use of soil moisture as a covariate had no effect on the prediction performance of the models; however, SMP values were used to select the driest dates, effectively mapping topsoil organic carbon. S2 was able to predict high SOC contents in the specific soil types located on the old terraces (mesas) shaped by rivers flowing from the southwestern Pyrénées.

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“…Previous research has delved into the effects of SMC on reflectance by rehydrating soil samples, demonstrating that SMC can impact the accuracy of SOM estimation [20,21]. SMC can interfere with SOM prediction by obscuring key soil spectral absorption features and distorting the overall spectral profile [22].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Organic Matter Based on Spectral Indices Combined with Water Removal Algorithm

Xu,

Liu,

Yan

et al. 2024

Remote Sensing

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Soil moisture strongly interferes with the spectra of soil organic matter (SOM) in the near-infrared region, which reduces the correlation between organic matter and spectra and decreases accuracy in the prediction of SOM. In this study, we explored the feasibility of two types of spectral indices, two- and three-band mixed (SI) and three-band spectral indices (SI3), and two water removal algorithms, direct standardization (DS) and external parameter orthogonalization (EPO), to estimate SOM in wet soils using a total of 192 soil samples at six water content gradients. The estimation accuracies of spectral indices combined with water removal algorithms were better than those of full spectral data combined with water removal algorithms: the prediction accuracies of SI-EPO (R2 = 0.735, RMSEp = 3.4102 g/kg) were higher than those of EPO (R2 = 0.63, RMSEp = 4.1021 g/kg), and those of SI-DS (R2 = 0.70, RMSEp = 3.7085 g/kg) were higher than those of DS (R2 = 0.61, RMSEp = 4.2806 g/kg); SI3-EPO (R2 = 0.752, RMSEp = 3.1344 g/kg) was better than SI-EPO; both EPO and DS effectively mitigated the influence of soil moisture, with EPO demonstrating superior performance in small-sample prediction scenarios. This study introduces a novel approach to counteract the impact of soil moisture on SOM estimation.

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Reducing the Moisture Effect and Improving the Prediction of Soil Organic Matter With VIS-NIR Spectroscopy in Black Soil Area

Cited by 12 publications

References 62 publications

Relationships between Soil Moisture and Visible–NIR Soil Reflectance: A Review Presenting New Analyses and Data to Fill the Gaps

Relationships between Soil Moisture and Visible–NIR Soil Reflectance: A Review Presenting New Analyses and Data to Fill the Gaps

Using Sentinel-2 Images for Soil Organic Carbon Content Mapping in Croplands of Southwestern France. The Usefulness of Sentinel-1/2 Derived Moisture Maps and Mismatches between Sentinel Images and Sampling Dates

Estimation of Soil Organic Matter Based on Spectral Indices Combined with Water Removal Algorithm

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