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

Renata Teixeira de Almeida Minhoni; Elia Scudiero; Daniele Zaccaria; João Carlos Cury Saad

doi:10.1016/j.scitotenv.2021.147216

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

Sci Total Environ. 2021 Aug 25:784:147216. doi: 10.1016/j.scitotenv.2021.147216. Epub 2021 Apr 21.

Authors

Renata Teixeira de Almeida Minhoni¹, Elia Scudiero², Daniele Zaccaria³, João Carlos Cury Saad⁴

Affiliations

¹ São Paulo State University, São Paulo State University (UNESP), School of Agronomical Sciences, Campus Botucatu, Av. Universitária, 3780, Botucatu, SP 18610-034, Brazil. Electronic address: renata.minhoni@unesp.br.
² University of California, Riverside, Department of Environmental Sciences, 900 University Ave., Riverside, CA 92521, USA; United States Department of Agriculture - Agricultural Research Service, U.S. Salinity Laboratory, 450 West Big Springs Rd., Riverside, CA 92507, USA. Electronic address: elia.scudiero@ucr.edu.
³ Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA. Electronic address: dzaccaria@ucdavis.edu.
⁴ São Paulo State University, São Paulo State University (UNESP), School of Agronomical Sciences, Campus Botucatu, Av. Universitária, 3780, Botucatu, SP 18610-034, Brazil. Electronic address: joao.saad@unesp.br.

PMID: 34088055
DOI: 10.1016/j.scitotenv.2021.147216

Abstract

Soil 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.

Keywords: Crop rotation; Reduced tillage; Remote sensing; Sentinel-2; Spectral indices; Sustainable agriculture.