Proximal field Vis-NIR spectroscopy of soil organic carbon: A solution to clear obstacles related to vegetation and straw cover

Rodionov, Andrei; Pätzold, Stefan; Welp, Gerhard; Pude, Ralf; Amelung, Wulf

doi:10.1016/j.still.2016.05.008

Cited by 17 publications

(9 citation statements)

References 37 publications

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“…Attempts to create samples with variable soil moisture content have also been made, though they are somewhat time consuming [73,80,143]. Additional factors that could interfere with in situ measurements are ambient conditions such as temperature, wind, and precipitation [144,145], soil roughness that could be altered by field operations, stones, plant residues and movement during spectral acquisition, i.e., vibrations [112,113,121]. Moreover, it should be noted that in-field measurements are usually performed in the top layer of the soil, with an exception being studies where the sensor penetrates the soil, which may impact the model's quality because laboratory reference measurements concern samples taken from a layer.…”

Section: Discussionmentioning

confidence: 99%

“…Although bare soil conditions are the most favorable for in situ measurements, in real field conditions, the presence of either green vegetation or straw cover is very common, and may lead to overestimations of SOC [120]. Rodionov et al [121] aimed, in their study using the same spectra, to distinguish SOC from photosynthetic and nonphotosynthetic vegetation. They conducted an experiment under laboratory conditions to estimate the effect of vegetation fractional cover.…”

Section: Photosynthetic and Nonphotosynthetic Vegetation Affecting Inmentioning

confidence: 99%

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From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

et al. 2020

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Rapid and cost-effective soil properties estimations are considered imperative for the monitoring and recording of agricultural soil condition for the implementation of site-specific management practices. Conventional laboratory measurements are costly and time-consuming, and, therefore, cannot be considered appropriate for large datasets. This article reviews laboratory and proximal sensing spectroscopy in the visible and near infrared (VNIR)–short wave infrared (SWIR) wavelength region for soil organic carbon and soil organic matter estimation as an alternative to analytical chemistry measurements. The aim of this work is to report the progress made in the last decade on data preprocessing, calibration approaches, and system configurations used for VNIR-SWIR spectroscopy of soil organic carbon and soil organic matter estimation. We present and compare the results of over fifty selective studies and discuss the factors that affect the accuracy of spectroscopic measurements for both laboratory and in situ applications.

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Section: Discussionmentioning

confidence: 99%

Section: Photosynthetic and Nonphotosynthetic Vegetation Affecting Inmentioning

confidence: 99%

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

et al. 2020

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“…Regarding SOC, RF variable importance analysis of PSR+ 3500 full spectrum indicated that the regions of the most significant wavelengths were found mostly centered in the visible region. This probably occurs due to the effect that SOC have in the soil's color as a result of absorptions from chromophores, as the literature review reveals [5,65,66]. More specifically, it has been observed that the yellow and red colors of the soil are due to the presence of hematite and goethite [67].…”

Section: Reference Spectral Measurements Compared To the Mems Sensor Spectral Measurementsmentioning

confidence: 99%

Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation

Karyotis

Angelopoulou

Tziolas

et al. 2021

Land

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Soil properties estimation with the use of reflectance spectroscopy has met major advances over the last decades. Their non-destructive nature and their high accuracy capacity enabled a breakthrough in the efficiency of performing soil analysis against conventional laboratory techniques. As the need for rapid, low cost, and accurate soil properties’ estimations increases, micro electro mechanical systems (MEMS) have been introduced and are becoming applicable for informed decision making in various domains. This work presents the assessment of a MEMS sensor (1750–2150 nm) in estimating clay and soil organic carbon (SOC) contents. The sensor was first tested under various experimental setups (different working distances and light intensities) through its similarity assessment (Spectral Angle Mapper) to the measurements of a spectroradiometer of the full 350–2500 nm range that was used as reference. MEMS performance was evaluated over spectra measured from 102 samples in laboratory conditions. Models’ calibrations were performed using random forest (RF) and partial least squares regression (PLSR). The results provide insights that MEMS could be employed for soil properties estimation, since the RF model demonstrated solid performance over both clay (R2 = 0.85) and SOC (R2 = 0.80). These findings pave the way for supporting daily agriculture applications and land related policies through the exploration of a wider set of soil properties.

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“…Further, CAI can indicate presence of pre-crop residues (Aguilar et al 2012). The CAI was derived from Vis-NIR spectra as CAI = 0.5•(R 2025 + R 2215 ) − R 2110 from the sums of reflectance factors R at the wavebands 2000-2050 nm (R 2025 ), 2089-2130 nm (R 2110 ) and 2190-2240 nm (R 2215 ), respectively (Rodionov et al 2016).…”

Section: Calculation Of Cellulose Absorption Index (Cai)mentioning

confidence: 99%

Predicting plant available phosphorus using infrared spectroscopy with consideration for future mobile sensing applications in precision farming

et al. 2019

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Phosphorus (P) fertilisation recommendations rely primarily on soil content of plant available P (P avl) that vary spatially within farm fields. Spatially optimized P fertilisation for precision farming requires reliable, rapid and non-invasive P avl determination. This laboratory study aimed to test and to compare visible-near infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy for P avl prediction with emphasis on future application in precision agriculture. After calibration with the conventional calcium acetate lactate (CAL) extraction method, limitations of Vis-NIRS and MIRS to predict P avl were evaluated in loess topsoil samples from different fields at six localities. Overall calibration with 477 (Vis-NIRS) and 586 (MIRS) samples yielded satisfactory model performance (R 2 0.70 and 0.72; RPD 1.8 and 1.9, respectively). Local Vis-NIRS models yielded better results with R 2 up to 0.93 and RPD up to 3.8. For MIRS, results were comparable. However, an overall model to predict P avl on independent test data partly failed. Sampling date, pre-crop harvest residues and fertilising regime affected model transferability. Varying transferability could partly be explained after deriving the cellulose absorption index from the Vis-NIR spectra. In 62 (Vis-NIRS) and 67% (MIRS) of all samples, prediction matched the correct P avl content class. Rapid discrimination between high, optimal and low P classes could be carried out on many samples from single fields thus marking an improvement over the common practice. However, P avl determination by means of IR spectroscopy is not yet satisfactory for determination of precision fertilizer dosage. For introduction into agricultural practice, a standardized sampling protocol is recommended to help achieve reliable spectroscopic P avl prediction.

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Proximal field Vis-NIR spectroscopy of soil organic carbon: A solution to clear obstacles related to vegetation and straw cover

Cited by 17 publications

References 37 publications

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation

Predicting plant available phosphorus using infrared spectroscopy with consideration for future mobile sensing applications in precision farming

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