Modelling of soil organic matter of arable soils with optical remote sensing data: the impact of soil surface state

2020 ◽  
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Surface State ◽  
Organic Matter Content ◽  
Remote Sensing Data ◽  
Matter Content ◽  
Optical Remote Sensing ◽  
Arable Soils ◽  
Sensing Data ◽  
Sentinel 2

<p>The study presents the analysis of effect of changes of the open surface of arable soils occuring due to the influence of agricultural practices or natural factors (mainly, precipitation) on the possibility of assessment of organic matter content in the arable layer with optical remote sensing data.</p><p>The object of the research was gray forest arable soil of a test field located in the Yasnogorsky district of the Tula region. In 2019, the field was complete fallow.</p><p>During field work conducted on the test field on 15.08.2019, the spectral reflectance of the surface of arable soils and a wetter subsurface horizon was measured at 30 points. At the same points, 30 mixed samples of the arable horizon were collected for laboratory estimation of organic matter content.</p><p>Spectral reflectance was measured using a HandHeld-2 field spectroradiometer, which operates in the range 325–1050 nm with a step of 1 nm.</p><p>Proximal sensing data were smoothed with Savitzky-Golley function and recalculated into Sentinel-2 bands using Gaussian function.</p><p>We also chose seven Sentinel-2 scenes for 2019 for the studied region: 2.04.2019, 17.04.2019, 20.04.2019, 5.05.2019; 6.06.2019, 19.06.2019, 28.08.2019. Atmospheric correction for chosen scenes was performed with Sen2Cor model in SNAP. Aftewords we extracted reflectance values at points, where we collected spectral data and soil samples in the field.</p><p>Then we calculated a number of spectral indices and ratios for both proximal and Sentinel-2 data which were further used in regression modelling. Models were cross-validated by bootstrapping.</p><p>At field scale, difference in moisture content did not significantly affect the accuracy and quality of the models. R<sup>2</sup>adjcv of model for dry surface layer was a bit higher than in case of model for wet subsurface layer (0.77 vs. 0.72). RMSEPcv and RPIQ for both cases were very close (0.71 and 0.71; 2.09 and 2.12).</p><p>When we used models developed based on proximal sensing data to calculate OM content with Sentinel-2 data at different acquisition dates, we found that the accuracy of OM prediction varied. In some cases RMSE was higher than 7 % and predicted OM content was two times higher than actual.</p><p>Models developed based only on Sentinel-2 data for different acquisition dates, varied in accuracy, quality and informative bands. R<sup>2</sup>adjcv of most models was about 0.72-0.83, RPIQ was 2.09-2.07, and RMSEPcv was in the range of 0.56-0.77 %.</p><p>Therefore changes in surface state of arable soils result in a situation when for each state we have different model. That imposes restrictions on further use of such models for remote evaluation and monitoring of organic matter content in arable soils. To deal with this problem, it is necessary to account for soil surface state when developing models for properties of arable soils based on optical remote sensing data.</p><p>The research was funded by the Ministry of Science and Higher Education of Russia (contract № 05.607.21.0302). </p>

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

Characterizing sown biodiverse pastures using remote sensing data with machine learning

2020 ◽  
Author(s):  
Tiago G. Morais ◽  
Pedro Vilar ◽  
Marjan Jongen ◽  
Nuno R. Rodrigues ◽  
Ivo Gama ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Organic Matter ◽  
Estimation Error ◽  
Organic Matter Content ◽  
Remote Sensing Data ◽  
Matter Content ◽  
Nitrogen And Phosphorus ◽  
Soil Organic Matter Content ◽  
Sentinel 2

<p>In Portugal, beef cattle are commonly fed with a mixture of grazing and forages/concentrate feed. Sown biodiverse permanent pastures rich in legumes (SBP) were introduced to provide quality animal feed and offset concentrate consumption. SBP also sequester large amounts of carbon in soils. They use biodiversity to promote pasture productivity, supporting a more than doubling in sustainable stocking rate, with several potential environmental co-benefits besides carbon sequestration in soils.<br>Here, we develop and test the combination of remote sensing and machine learning approaches to predict the most relevant production parameters of plant and soil. For the plants, we included pasture yield, nitrogen and phosphorus content, and species composition (legumes, grasses and forbs). In the soil, we included soil organic matter content, as well as nitrogen and phosphorus content. For soils, hyperspectral data were obtained in the laboratory using previously collected soil samples (in near-infrared wavelengths). Remotely sensed multispectral data was acquired from the Sentinel-2 satellite. We also calculated several vegetation indexes. The machine learning algorithms used were artificial neural networks and random forests regressions. We used data collected in late winter/spring from 14 farms (more than 150 data samples) located in the Alentejo region, Portugal.<br>The models demonstrated a good prediction capacity with r-squared (r2) higher than in 0.70 for most of the variables and both spectral datasets. Estimation error decreases with proximity of the spectral data acquisition, i.e. error is lower using hyperspectral datasets than Sentinel-2 data. Further, results not shown systematic overestimation and/or underestimation. The fit is particularly accurate for yield and organic matter, higher than 0.80. Soil organic matter content has the lowest standard estimation error (3 g/kg soil – average SOM: 20 g/kg soil), while the legumes fraction has the highest estimation error (20% legumes fraction).<br>Results show that a move towards automated monitoring (combining proximal or remote sensing data and machine learning methods) can lead to expedited and low-cost methods for mapping and assessment of variables in sown biodiverse pastures.</p>

Arable soil surface status as a factor affecting the quality of soil properties detection based on remote or proximal sensing technologies

2021 ◽  
Author(s):  
Elena Prudnikova ◽  
Igor Savin ◽  
Gretelerika Vindeker
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Surface State ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Arable Soil ◽  
Optical Remote Sensing ◽  
Arable Soils ◽  
Proximal Sensing ◽  
Sensing Data

<p>Due to short wavelengths, optical remote sensing data provides information about the properties of very thin soil surface layer. This is especially crucial for arable soils as their surface experiences intense impact of agricultural practices and natural conditions. In temperate zone atmospheric precipitation is one of the main natural factors affecting the surface state of arable soils. It causes the breakdown of soil surface aggregates and the redistribution of formed soil material resulting in surface sealing and the formation of soil crust.</p><p>We studied the properties of soil crust and its impact on the detection of soil properties on arable soils of European part of Russia.</p><p>Our research showed that the properties of soil surface crust (texture, mineralogical composition, organic matter content, content of microelements, spectral reflectance) differed from the properties of the rest of arable horizon. That discrepancy negatively impacted the performance and reproducibility of the models developed for the detection of arable soil properties and their monitoring on the basis of optical remote sensing data.</p><p>We found that the performance of the models for the detection of soil fertility indicators based on Sentinel-2 data varied depending on the acquisition date. Optimal dates were different for different fertility indicators. Introduction of information on soil surface state (% of crust and shadows/cracks) at different acquisition dates as predictors in the models developed based on Sentinel-2 data allowed improving their performance and stability.</p><p>Therefore, soil surface state is an important factor which should be considered when developing models for the detection and monitoring of arable soil properties based on optical remote sensing data or proximal sensing of soil surface. Usage of laboratory soil spectra libraries instead of field spectral data leads to less precise prediction models.</p><p>The research was supported by the Ministry of science and higher education of Russia (agreement No 075-15-2020-909), and RUDN University Strategic Academic Leadership Program.</p>

scholarly journals Effect of Open Soil Surface Patterns on Soil Detectability Based on Optical Remote Sensing Data

Proceedings ◽  
2018 ◽  
Vol 2 (7) ◽  
pp. 357 ◽  
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Spectral Variation ◽  
Least Squares Regression ◽  
Arable Soils ◽  
Sensing Data ◽  
Surface Patterns ◽  
Soil Reflectance

Arable soils are subjected to the altering influence of agricultural and natural processes determining surface feedback patterns therefore affecting their ability to reflect light. However, remote soil mapping and monitoring usually ignore information on surface state at the time of data acquisition. Conducted research demonstrates the contribution of surface feedback dynamics to soil reflectance and its relationship with soil properties. Analysis of variance showed that the destruction surface patterns accounts for 71% of spectral variation. The effect of surface smoothing on the relationships between soil reflectance and its properties varies. In the case of organic matter and medium and coarse sand particles, correlation decreases with the removement of surface structure. For particles of fine sand and coarse silt, grinding changes spectral areas of high correlation. Partial least squares regression models also demonstrated variations in complexity, R2cv and RMSEPcv. Field dynamics of surface feedback patterns of arable soils causes 22–46% of soil spectral variations depending on the growing season and soil type. The directions and areas of spectral changes seem to be soil-specific. Therefore, surface feedback patterns should be considered when modelling soil properties on the basis of optical remote sensing data to ensure reliable and reproducible results.

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

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