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Multitemporal satellite imagery analysis for soil organic carbon assessment in an agricultural farm in southeastern Brazil

dc.contributor.authorMinhoni, Renata Teixeira de Almeida [UNESP]
dc.contributor.authorScudiero, Elia
dc.contributor.authorZaccaria, Daniele
dc.contributor.authorSaad, João Carlos Cury [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionRiverside
dc.contributor.institutionU.S. Salinity Laboratory
dc.contributor.institutionUniversity of California
dc.date.accessioned2021-06-25T11:15:59Z
dc.date.available2021-06-25T11:15:59Z
dc.date.issued2021-08-25
dc.description.abstractSoil organic carbon (SOC) plays a crucial role for soil health. However, large datasets needed to accurately assess SOC at high resolution across scales are labor-intensive, time-consuming, and expensive. Ancillary geodata, including remote sensing spectral indices (RS-SIs) and topographic indicators (TIs), have been proposed as spatial covariates. Reported relationships between SOC and RS-SIs are erratic, possibly because single-date RS-SIs do not accurately capture SOC spatial variability due to transient confounding factors in the soil (e.g., moisture). However, multitemporal RS-SI data analysis may lead to noise reduction in SOC versus RS-SI relationships. This study aimed at: i) comparing single-date versus multitemporal RS-Sis derived from Sentinel-2 imagery for assessment of topsoil (0–0.2 m) SOC in two agricultural fields located in south-eastern Brazil; ii) comparing the performance of RS-SIs and TIs; iii) using adequate RS-SIs and TIs to compare sampling schemes defined on different collection grids; and iv) studying the temporal changes of SOC (0–0.2 m and 0.2–0.4 m). Results showed that: i) single-date RS-SIs were not reliable proxies for topsoil SOC at the study sites. For most of the tested RS-SIs, multitemporal data analysis produced accurate proxies for SOC; e.g., for the Normalized Difference Vegetation Index, the 4.5th multitemporal percentile predicted SOC with an R2 of 0.64; ii) The best TI was elevation (ranging from 643 to 684 m) with an R2 of 0.70; iii) The multitemporal SI and elevation maps indicated that the different sampling schemes were equally representative of the topsoil SOC's distribution across the entire area; and iv) From 2012 through 2019, topsoil SOC increased from 19.3 to 24.1 g kg−1. The ratio between SOC in the topsoil and subsoil (0.2–0.4 m) decreased from 1.7 to 1.1. Further testing of the proposed multitemporal RS-SI analysis is necessary to confirm its dependability for SOC assessment in Brazil and elsewhere.en
dc.description.affiliationSão Paulo State University São Paulo State University (UNESP) School of Agronomical Sciences, Campus Botucatu, Av. Universitária, 3780
dc.description.affiliationUniversity of California Riverside Department of Environmental Sciences, 900 University Ave.
dc.description.affiliationUnited States Department of Agriculture – Agricultural Research Service U.S. Salinity Laboratory, 450 West Big Springs Rd.
dc.description.affiliationDepartment of Land Air and Water Resources University of California
dc.description.affiliationUnespSão Paulo State University São Paulo State University (UNESP) School of Agronomical Sciences, Campus Botucatu, Av. Universitária, 3780
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdCNPq: 140676/2017-1
dc.identifierhttp://dx.doi.org/10.1016/j.scitotenv.2021.147216
dc.identifier.citationScience of the Total Environment, v. 784.
dc.identifier.doi10.1016/j.scitotenv.2021.147216
dc.identifier.issn1879-1026
dc.identifier.issn0048-9697
dc.identifier.scopus2-s2.0-85105695483
dc.identifier.urihttp://hdl.handle.net/11449/208671
dc.language.isoeng
dc.relation.ispartofScience of the Total Environment
dc.sourceScopus
dc.subjectCrop rotation
dc.subjectReduced tillage
dc.subjectRemote sensing
dc.subjectSentinel-2
dc.subjectSpectral indices
dc.subjectSustainable agriculture
dc.titleMultitemporal satellite imagery analysis for soil organic carbon assessment in an agricultural farm in southeastern Brazilen
dc.typeArtigo
dspace.entity.typePublication
unesp.departmentEngenharia Rural - FCApt

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