scholarly journals Soil Water Characteristics of Gleysols in the Bamenda (Cameroon) Wetlands and Implications for Agricultural Management Strategies

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Alice Mufur Magha ◽  
Primus Azinwi Tamfuh ◽  
Lionelle Estelle Mamdem ◽  
Marie Christy Shey Yefon ◽  
Bertrand Kenzong ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Water ◽  
Total Variance ◽  
Organic Carbon Content ◽  
Available Water ◽  
High Soil ◽  
Local Soil ◽  
Very High

Water budgeting in agriculture requires local soil moisture information as crops depend mainly on moisture available at root level. The present paper aims to evaluate the soil moisture characteristics of Gleysols in the Bamenda (Cameroon) wetlands and to evaluate the link between soil moisture content and selected soil characteristics affecting crop production. The work was conducted in the field and laboratory, and data were analyzed by simple descriptive statistics. The main results showed that the soils had a silty clayey to clayey texture, high bulk density, high soil organic carbon content, and high soil organic carbon stocks. The big difference between moisture contents at wilting point and at field capacity testified to very high plant-available water content. Also, the soils displayed very high contents of readily available water and water storage contents. The soil moisture characteristics give sigmoid curves and enabled noting that the Gleysols attain their full water saturation at a range of 57.68 to 91.70% of dry soil. Clay and SOC contents show a significant positive correlation with most of the soil moisture characteristics, indicating that these soil properties are important for soil water retention. Particle density, coarse fragments, and sand contents correlated negatively with the soil moisture characteristics, suggesting that they decrease soil water-holding capacity. The principal component analysis (PCA) enabled reducing 17 variables described to only three principal components (PCs) explaining 73.73% of the total variance; the first PC alone expressed 45.12% of the total variance, associating clay, SOC, and six soil moisture characteristics, thus portraying a deep correlation between these eight variables. Construction of contoured ditches, deep tillage, and raised ridges management techniques during the rainy season while channeling water from nearby water bodies into the farmland, opportunity cropping, and usage of water cans and other irrigation strategies are used during the dry season to combat water constraints.

scholarly journals 3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Tobias Rentschler ◽  
Ulrike Werban ◽  
Mario Ahner ◽  
Thorsten Behrens ◽  
Philipp Gries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Organic Carbon Content ◽  
3D Mapping ◽  
Soil Organic Carbon Content

Combined use of Sentinel-2 images and Sentinel-1-derived moisture maps for soil organic carbon content mapping in croplands, South-western France

2021 ◽  
Author(s):  
Diego Urbina Salazar ◽  
Emmanuelle Vaudour ◽  
Nicolas Baghdadi ◽  
Eric Ceschia ◽  
Dominique Arrouays
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Soil Sampling ◽  
Organic Carbon Content ◽  
Spectral Models ◽  
Sentinel 2

<p>In terms of agronomy, soil organic carbon (SOC) content is important for crop growth and development. From the environmental viewpoint, SOC sequestration is essential to mitigate the emission of greenhouse gases into the atmosphere. The use of sensors for carbon monitoring over croplands is a key issue in recent works. Sentinel-1/2 (S1, S2) satellites acquire data with regular frequency (weekly) and high spatial resolution (10 and 20 meters). Previous studies have demonstrated their potential for quantification of soil attributes including topsoil organic carbon content on single dates. Soil surface roughness and soil moisture influence the performance of spectral models according to acquisition date, particularly surface soil moisture (SM), as shown by multidate models of predicted SOC content (Vaudour et al., 2021). Still, the sensitivity of Sentinel-1/2 to SM must be better understood and exploited for a given single date. A possible solution to determine the influence of SM on single date model performance consists of including it as a covariate.</p><p>In order to predict the topsoil SOC content over croplands in the Pyrenees region, France (22177 km²), this study addresses: (i) the influence of the Sentinel image date and that of the soil sampling year; (ii) the contribution of SM products derived from the Sentinel-1/2 data (El Hajj et al., 2017) in the spectral models.</p><p>The influence of the image date and soil sampling date was analyzed for springs 2017 and 2018. Clouds, shadows and NDVI (> 0.35) values were excluded from the images. Best single performances (RPD ≥ 1.3) were scored for soil sampling sets collected in 2016-2018. The same dates were analyzed using either SM maps, or signal values of VV and VH polarizations from S1 images. SM or polarization values were extracted for each sample and integrated into the partial least squares regression (PLSR) models, respectively. The best performance (RPD = 1.57) was obtained with SM as a covariate in 2017, with lowest mean SM throughout the map.</p><p> </p><p>References</p><p>El Hajj, M.; Baghdadi, N.; Zribi, M.; Bazzi, H. Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sensing <strong>2017</strong>, 9, 1292, doi:10.3390/rs9121292.</p><p>Vaudour, E.; Gomez, C.; Lagacherie, P.; Loiseau, T.; Baghdadi, N.; Urbina-Salazar, D.; Loubet, B.; Arrouays, D. Temporal Mosaicking Approaches of Sentinel-2 Images for Extending Topsoil Organic Carbon Content Mapping in Croplands. International Journal of Applied Earth Observation and Geoinformation <strong>2021</strong>, 96, 102277, doi:10.1016/j.jag.2020.102277.</p>

Reduction in the earthworm metabolomic response after phenanthrene exposure in soils with high soil organic carbon content

2013 ◽  
Vol 175 ◽  
pp. 75-81 ◽  
Author(s):  
Jennifer R. McKelvie ◽  
Melissa Whitfield Åslund ◽  
Magda A. Celejewski ◽  
André J. Simpson ◽  
Myrna J. Simpson
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Organic Carbon Content ◽  
Soil Organic Carbon Content ◽  
High Soil

scholarly journals 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

2021 ◽  
Vol 13 (24) ◽  
pp. 5115
Author(s):  
Diego Urbina-Salazar ◽  
Emmanuelle Vaudour ◽  
Nicolas Baghdadi ◽  
Eric Ceschia ◽  
Anne C. Richer-de-Forges ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Model Performance ◽  
Sampling Period ◽  
Soil Sampling ◽  
Organic Carbon Content ◽  
Least Squares Regression ◽  
Sentinel 2 ◽  
Emission Of Greenhouse Gases

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.

Space-time monitoring of soil organic carbon content across a semi-arid region of Australia

2021 ◽  
Vol 24 ◽  
pp. e00367
Author(s):  
Patrick Filippi ◽  
Stephen R. Cattle ◽  
Matthew J. Pringle ◽  
Thomas F.A. Bishop
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Arid Region ◽  
Space Time ◽  
Organic Carbon Content ◽  
Soil Organic Carbon Content ◽  
Semi Arid Region ◽  
Semi Arid

scholarly journals Influence of Slope Gradient and Aspect on Soil Organic Carbon Content in the Region of Niš, Serbia

2021 ◽  
Vol 13 (15) ◽  
pp. 8332
Author(s):  
Snežana Jakšić ◽  
Jordana Ninkov ◽  
Stanko Milić ◽  
Jovica Vasin ◽  
Milorad Živanov ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Slope Aspect ◽  
Available Phosphorus ◽  
Organic Carbon Content ◽  
Slope Gradient ◽  
Phosphorus And Potassium ◽  
Uncultivated Land

Topography-induced microclimate differences determine the local spatial variation of soil characteristics as topographic factors may play the most essential role in changing the climatic pattern. The aim of this study was to investigate the spatial distribution of soil organic carbon (SOC) with respect to the slope gradient and aspect, and to quantify their influence on SOC within different land use/cover classes. The study area is the Region of Niš in Serbia, which is characterized by complex topography with large variability in the spatial distribution of SOC. Soil samples at 0–30 cm and 30–60 cm were collected from different slope gradients and aspects in each of the three land use/cover classes. The results showed that the slope aspect significantly influenced the spatial distribution of SOC in the forest and vineyard soils, where N- and NW-facing soils had the highest level of organic carbon in the topsoil. There were no similar patterns in the uncultivated land. No significant differences were found in the subsoil. Organic carbon content was higher in the topsoil, regardless of the slope of the terrain. The mean SOC content in forest land decreased with increasing slope, but the difference was not statistically significant. In vineyards and uncultivated land, the SOC content was not predominantly determined by the slope gradient. No significant variations across slope gradients were found for all observed soil properties, except for available phosphorus and potassium. A positive correlation was observed between SOC and total nitrogen, clay, silt, and available phosphorus and potassium, while a negative correlation with coarse sand was detected. The slope aspect in relation to different land use/cover classes could provide an important reference for land management strategies in light of sustainable development.

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