Spatial Estimation of Soil Organic Matter Content Using Remote Sensing Data in Southern Tunisia

2018 ◽  
pp. 215-217 ◽  
Author(s):  
Emna Medhioub ◽  
Moncef Bouaziz ◽  
Samir Bouaziz
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Organic Matter Content ◽  
Remote Sensing Data ◽  
Matter Content ◽  
Soil Organic Matter Content ◽  
Sensing Data ◽  
Spatial Estimation ◽  
Southern Tunisia

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>

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>

Spatial Estimation of Soil Total Nitrogen Using Cokriging with Predicted Soil Organic Matter Content

2009 ◽  
Vol 73 (5) ◽  
pp. 1676-1681 ◽  
Author(s):  
Chunfa Wu ◽  
Jiaping Wu ◽  
Yongming Luo ◽  
Limin Zhang ◽  
Stephen D. DeGloria
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Total Nitrogen ◽  
Organic Matter Content ◽  
Matter Content ◽  
Soil Organic Matter Content ◽  
Soil Total Nitrogen ◽  
Spatial Estimation

scholarly journals Soil nitrogen supply of peat grasslands estimated by degree days and soil organic matter content

2020 ◽  
Vol 117 (3) ◽  
pp. 351-365
Author(s):  
J. Pijlman ◽  
G. Holshof ◽  
W. van den Berg ◽  
G. H. Ros ◽  
J. W. Erisman ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Nitrogen ◽  
Organic Matter Content ◽  
Matter Content ◽  
Nitrogen Supply ◽  
Degree Days ◽  
Soil Organic Matter Content ◽  
Soil Nitrogen Supply

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.

scholarly journals Effect of Clay, Soil Organic Matter, and Soil pH on Initial and Residual Weed Control with Flumioxazin

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1326
Author(s):  
Calvin F. Glaspie ◽  
Eric A. L. Jones ◽  
Donald Penner ◽  
John A. Pawlak ◽  
Wesley J. Everman
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Weed Control ◽  
Soil Ph ◽  
Organic Matter Content ◽  
Amaranthus Retroflexus ◽  
Matter Content ◽  
Weed Species ◽  
Soil Organic Matter Content ◽  
Field Soils

Greenhouse studies were conducted to evaluate the effects of soil organic matter content and soil pH on initial and residual weed control with flumioxazin by planting selected weed species in various lab-made and field soils. Initial control was determined by planting weed seeds into various lab-made and field soils treated with flumioxazin (71 g ha−1). Seeds of Echinochloa crus-galli (barnyard grass), Setaria faberi (giant foxtail), Amaranthus retroflexus (redroot pigweed), and Abutilon theophrasti (velvetleaf) were incorporated into the top 1.3 cm of each soil at a density of 100 seeds per pot, respectively. Emerged plants were counted and removed in both treated and non-treated pots two weeks after planting and each following week for six weeks. Flumioxazin control was evaluated by calculating percent emergence of weeds in treated soils compared to the emergence of weeds in non-treated soils. Clay content was not found to affect initial flumioxazin control of any tested weed species. Control of A. theophrasti, E. crus-galli, and S. faberi was reduced as soil organic matter content increased. The control of A. retroflexus was not affected by organic matter. Soil pH below 6 reduced flumioxazin control of A. theophrasti, and S. faberi but did not affect the control of A. retroflexus and E. crus-galli. Flumioxazin residual control was determined by planting selected weed species in various lab-made and field soils 0, 2, 4, 6, and 8 weeks after treatment. Eight weeks after treatment, flumioxazin gave 0% control of A. theophrasti and S. faberi in all soils tested. Control of A. retroflexus and Chenopodium album (common lambsquarters) was 100% for the duration of the experiment, except when soil organic matter content was greater than 3% or the soil pH 7. Eight weeks after treatment, 0% control was only observed for common A. retroflexus and C. album in organic soil (soil organic matter > 80%) or when soil pH was above 7. Control of A. theophrasti and S. faberi decreased as soil organic matter content and soil pH increased. Similar results were observed when comparing lab-made soils to field soils; however, differences in control were observed between lab-made organic matter soils and field organic matter soils. Results indicate that flumioxazin can provide control ranging from 75–100% for two to six weeks on common weed species.

scholarly journals Assessment of Potato Farmland Soil Nutrient Based on MDS-SQI Model in the Loess Plateau

2021 ◽  
Vol 13 (7) ◽  
pp. 3957
Author(s):  
Yingying Xing ◽  
Ning Wang ◽  
Xiaoli Niu ◽  
Wenting Jiang ◽  
Xiukang Wang
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Fertility ◽  
Soil Quality ◽  
Soil Nutrients ◽  
Organic Matter Content ◽  
Soil Nutrient ◽  
Matter Content ◽  
Soil Organic Matter Content ◽  
Farmland Soil

Soil nutrients are essential nutrients provided by soil for plant growth. Most researchers focus on the coupling effect of nutrients with potato yield and quality. There are few studies on the evaluation of soil nutrients in potato fields. The purpose of this study is to investigate the soil nutrients of potato farmland and the soil vertical nutrient distributions, and then to provide a theoretical and experimental basis for the fertilizer management practices for potatoes in Loess Plateau. Eight physical and chemical soil indexes were selected in the study area, and 810 farmland soil samples from the potato agriculture product areas were analyzed in Northern Shaanxi. The paper established the minimum data set (MDS) for the quality diagnosis of the cultivated layer for farmland by principal component analysis (PCA), respectively, and furthermore, analyzed the soil nutrient characteristics of the cultivated layer adopted soil quality index (SQI). The results showed that the MDS on soil quality diagnosis of the cultivated layer for farmland soil included such indicators as the soil organic matter content, soil available potassium content, and soil available phosphorus content. The comprehensive index value of the soil quality was between 0.064 and 0.302. The SPSS average clustering process used to classify SQI was divided into three grades: class I (36.2%) was defined as suitable soil fertility (SQI < 0.122), class II (55.6%) was defined as moderate soil fertility (0.122 < SQI < 0.18), and class III (8.2%) was defined as poor soil fertility (SQI > 0.186). The comprehensive quality of the potato farmland soils was generally low. The proportion of soil nutrients in the SQI composition ranged from large to small as the soil available potassium content = soil available phosphorus content > soil organic matter content, which became the limiting factor of the soil organic matter content in this area. This study revolves around the 0 to 60 cm soil layer; the soil fertility decreased gradually with the soil depth, and had significant differences between the respective soil layers. In order to improve the soil nutrient accumulation and potato yield in potato farmland in northern Shaanxi, it is suggested to increase the fertilization depth (20 to 40 cm) and further study the ratio of nitrogen, phosphorus, and potassium fertilizer.

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