scholarly journals Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands

2021 ◽  
Vol 96 ◽  
pp. 102277
Author(s):  
Emmanuelle Vaudour ◽  
Cécile Gomez ◽  
Philippe Lagacherie ◽  
Thomas Loiseau ◽  
Nicolas Baghdadi ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Carbon Content ◽  
Organic Carbon Content ◽  
Sentinel 2

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>

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 Soil age and soil organic carbon content shape biochemical responses to multiple freeze–thaw events in soils along a postmining agricultural chronosequence

2021 ◽  
Author(s):  
Christoph Rosinger ◽  
Michael Bonkowski
Keyword(s):  
Microbial Biomass ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Organic Carbon Content ◽  
Freeze Thaw ◽  
Biochemical Responses ◽  
Microbial Responses ◽  
Soil Age ◽  
Carbon Losses

AbstractFreeze–thaw (FT) events exert a great physiological stress on the soil microbial community and thus significantly impact soil biogeochemical processes. Studies often show ambiguous and contradicting results, because a multitude of environmental factors affect biogeochemical responses to FT. Thus, a better understanding of the factors driving and regulating microbial responses to FT events is required. Soil chronosequences allow more focused comparisons among soils with initially similar start conditions. We therefore exposed four soils with contrasting organic carbon contents and opposing soil age (i.e., years after restoration) from a postmining agricultural chronosequence to three consecutive FT events and evaluated soil biochgeoemical responses after thawing. The major microbial biomass carbon losses occurred after the first FT event, while microbial biomass N decreased more steadily with subsequent FT cycles. This led to an immediate and lasting decoupling of microbial biomass carbon:nitrogen stoichiometry. After the first FT event, basal respiration and the metabolic quotient (i.e., respiration per microbial biomass unit) were above pre-freezing values and thereafter decreased with subsequent FT cycles, demonstrating initially high dissimilatory carbon losses and less and less microbial metabolic activity with each iterative FT cycle. As a consequence, dissolved organic carbon and total dissolved nitrogen increased in soil solution after the first FT event, while a substantial part of the liberated nitrogen was likely lost through gaseous emissions. Overall, high-carbon soils were more vulnerable to microbial biomass losses than low-carbon soils. Surprisingly, soil age explained more variation in soil chemical and microbial responses than soil organic carbon content. Further studies are needed to dissect the factors associated with soil age and its influence on soil biochemical responses to FT events.

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