Estimate of carbon stock in the soil via diffuse reflectance spectroscopy (vis/nir) air and orbital remote sensing

Authors

  • Ohana Cristina Oliveira Faria Postgraduate Program in Tropical Agriculture, Universidade Federal de Mato Grosso, Cuiabá, MT https://orcid.org/0000-0002-3407-6869
  • Gilmar Nunes Torres Santos Lab Participações Comércio e Indústria Aeroespacial S. A., Rio de Janeiro, RJ https://orcid.org/0000-0003-4307-415X
  • Luis Augusto Di Loreto Di Raimo Postgraduate Program in Tropical Agriculture, Universidade Federal de Mato Grosso, Cuiabá, MT https://orcid.org/0000-0003-0681-7647
  • Eduardo Guimarães Couto Department of Soil and Agricultural Engineering, Universidade Federal de Mato Grosso, Cuiabá, MT https://orcid.org/0000-0002-5271-9709

DOI:

https://doi.org/10.1590/1983-21252023v36n320rc

Keywords:

Precision agriculture. Carbon stock monitoring. Remote sensors.

Abstract

Current procedures for determining soil organic carbon (SOC) content are costly, time-consuming, and generate polluting chemical waste. Therefore, developing new protocols using aerial and orbital remote sensing and diffuse reflectance spectroscopy (DRS) for digitally mapping the stock of soil organic carbon (CS) is essential for promoting actions of research and monitoring SOC in Brazilian soils. Given this, three areas of commercial plots in the region of the Middle North of Mato Grosso were studied, where sampling was carried out for the determination of SOC in the layer from 0 to 30 cm, evaluated by the dry combustion method and estimated through DRS in the visible to near-infrared region - Vis-NIR-SWIR/350-2500 nm). To obtain the images by aerial remote sensing, the Carcará II® Unmanned Aerial Vehicle was used, with a MicaSense® multispectral camera (RGB + NIR + RedEdge) attached. The orbital sensors used were the Sentinel 2® and Planet® satellites. This study showed that soil carbon stock values could be predicted using different modeling approaches based on field and laboratory spectral measurements. Predictive models to estimate SOC can be established using remote and near sensing, thus allowing a better understanding of spatial patterns of SOC in crop fields.

Downloads

Download data is not yet available.

References

ALVARENGA, B. S. et al. Avaliação de técnicas de processamento digital de imagens para a estimativa de áreas de arroz irrigado: um estudo de caso no município de Santa Vitória do Palmar-RS. Simpósio Brasileiro de Sensoriamento Remoto, 12: 3961-3966, 2005.

ANGELOPOULOU, T. et al. Remote sensing techniques for soil organic carbon estimation: A review. Remote Sensing, 11: 1-18, 2019.

ASNER, G. P. et al. Conservation assessment of the Peruvian Andes and Amazon based on mapped forest functional diversity. Biological Conservation, 210: 80-88, 2017.

BALLABIO, C. et al. Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression. Geoderma, 355: 1-14, 2019.

BANGELESA, F. et al. Predicting soil organic carbon content using hyperspectral remote sensing in a degraded mountain landscape in lesotho. Applied and Environmental Soil Science, 2020: 1-11, 2020.

BAYER, C. et al. Carbon sequestration in two Brazilian Cerrado soils under no-till. Soil and Tillage Research: 86: 237-245, 2006.

BAYER, A. et al. A comparison of feature-based MLR and PLS regression techniques for the prediction of three soil constituents in a degraded South African ecosystem. Applied and Environmental Soil Science, 2012: 1-20, 2012.

BINEY, J. K. M. et al. Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery. Remote Sensing, 13: 1-19, 2021.

BEN-DOR, E.; BANIN, A. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal, 59, 364-372, 1995.

BONFATTI, B. R. et al. Digital mapping of soil carbon in a viticultural region of Southern Brazil. Geoderma, 261: 204-221, 2016.

BOSER, B. E. et al. A training algorithm for optimal margin classifiers. In: COLT '92: Proceedings of the fifth annual workshop on Computational learning theory, 5º Annual Workshop, 1992, Pittsburgh, Proccedings… New York, ACM, 1992, s/v, p. 144-152.

CLARK, R. N.; RENCZ, A. N. Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual of remote sensing, 3: 3-58, 1999.

CSILLIK, O. et al. Monitoring tropical forest carbon stocks and emissions using Planet satellite data. Scientific Reports, 9: 1-12, 2019.

DEMATTÊ, J. A. M.; TOLEDO, A. M. A.; SIMÕES, M. S. Metodologia para reconhecimento de três solos por sensores: laboratorial e orbital. Revista Brasileira de Ciência do Solo, 28: 877-889, 2004.

DEMATTÊ, J. A. et al. Spectral regionalization of tropical soils in the estimation of soil attributes. Revista Ciência Agronômica, 47: 589-598, 2016.

DOTTO, A. C. et al. A systematic study on the application of scatter-corrective and spectral- derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra. Geoderma, 314: 262–274, 2018.

DOTTO, A. C. et al. A Quantification Tool for Soil Properties Using Spectroscopic Data in R. Revista Brasileira de Ciência do Solo, 43: 1-15, 2019.

DOTTO, A. C. et al. Mapeamento digital de atributos: granulometria e matéria orgânica do solo utilizando espectroscopia de reflectância difusa. Revista Brasileira de Ciência do Solo, 38: 1663-1671, 2014.

DUNCAN, C. et al. Satellite remote sensing to monitor mangrove forest resilience and resistance to sea level rise. Methods in Ecology and Evolution, 9: 1837-1852, 2018.

ESA - Agência Espacial Europeia. Introduzindo o Sentinel-2. Disponível em: https://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel2/Introducing_Sentinel-2>. Acesso em: 4 abr. 2022.

FATOYINBO, T. et al. Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta. Environmental Research Letters, 13: 1-13, 2018.

GILLIOT, J. M. et al. Estimation des teneurs en carbone organique des sols agricoles par télédétection par drone. Revue Française de Photogrammétrie et de Télédétection, s/v.: 105-115, 2017.

GUO, L. et al. Mapping soil organic carbon stock by hyperspectral and time-series multispectral remote sensing images in low-relief agricultural areas. Geoderma, 398: 1-16, 2021.

GUO, L. et al. Exploring the Sensitivity of Sampling Density in Digital Mapping of Soil Organic Carbon and Its Application in Soil Sampling. Remote Sensing, 10: 1-27, 2018.

HE, X. et al. Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images. CATENA, 205: 1-13, 2021.

HUANG, H.; ROY, D. P. Characterization of Planetscope-0 Planetscope-1 surface reflectance and normalized difference vegetation index continuity. Science of Remote Sensing, 3: 1-16, 2021.

IPCC, 2022: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 p.

JABER, S. M.; AL-QINNA, M. I. Soil organic carbon modeling and mapping in a semi-arid environment using thematic mapper data. Photogrammetric Engineering & Remote Sensing, 77: 709-719, 2011.

JIANG, Q. et al. Estimation of soil organic carbon and total nitrogen in different soil layers using VNIR spectroscopy: Effects of spiking on model applicability. Geoderma, 293: 54-63, 2017.

KOVACEVIC, M. et al. Geological units classification of multispectral images by using support vector machines. In: International Conference on Intelligent Networking and Collaborative Systems (INCOS), 2009, Barcelona, Proccedings… New York, IEEE, 2009, s/v, p. 267-272

MCCARTY, G. W. et al. Infrared sensors to map soil carbon in agricultural ecosystems. In: VISCARRA-ROSSEL, R. A. et al. (Eds.). Proximal Soil Sensing. Progress in Soil Science 1 Springer, Dordrecht, 2010, cap 14, p. 165-176.

MAIA, S. M. F. et al. Changes in soil organic carbon storage under different agricultural management systems in the Southwest Amazon Region of Brazil. Soil and Tillage Research, 106: 177-184, 2010.

MIRANDA, E. et al. Long-Term Changes in Soil Carbon Stocks in the Brazilian Cerrado Under Commercial Soybean. Land Degradation & Development, 27: 1586-1594, 2016.

MINASNY, B. et al. Soil carbon 4 per mille. Geoderma, 292: 59-86, 2017.

MOURA-BUENO, J. M. et al. Erosão em áreas de encosta com solos frágeis e sua relação com a cobertura do solo. Revista Scientia Agrária, 19: 102-112, 2018.

PLANET. Education and Research - Satellite Imagery Solutions. 2022. Disponível em: https://www.planet.com/markets/education-and-research/. Acesso em 19 dez. 2022.

PAUSTIAN, K. et al. Quantifying carbon for agricultural soil management: From the current status toward a global soil information system. Carbon Management, 10: 567-587, 2019.

POPPIEL, R. R. ET AL. Bridging the gap between soil spectroscopy and traditional laboratory: Insights for routine implementation. Geoderma, 425, 01-14, 2022.

R CORE TEAM. R a language and environment for statistical computing. Version 3.6.0. Vienna, Austria, 2017.

RAMIREZ-LOPEZ, L. et al. Sampling optimal calibration sets in soil infrared spectroscopy. Geoderma, 226, 140-150, 2014.

RAMIREZ-LOPEZ, L. et al. The spectrum-based learner: A new local approach for modeling soil Vis–NIR spectra of complex datasets. Geoderma, 195: 268-279, 2013.

ROCHA, A. et al. Caracterização da variabilidade climática em Diamantino/MT-Brasil no período de 1987 a 2017. Enciclopédia Biosfera, 15: 69-80, 2018.

SANTOS, H. et al. Sistema brasileiro de classificação de solos. 4. ed. Rio de Janeiro, RJ: Embrapa Solos, 353 p, 2013.

SAYES, W.; MOUAZEN, A. M.; RAMON, H. Potencial for onsite and online analysis of pig manire using visible and near infrared reflectance spectroscopy. Biosystems Engineering, 91: 393-402, 2005.

SMITH, W. N. et al. Crop residue removal effects on soil carbon: Measured and inter-model comparisons. Agriculture, Ecosystems & Environment, 161: 27-38, 2012.

SUMMERS, D. et al. Visible nearinfrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators, 11: 123-131. 2011.

VAN DER MEER, F. D.; VAN DER WERFF, H. M. A.; VAN RUITENBEEK, F. J. A. Potential of ESA's Sentinel-2 for geological applications. Remote Sensing of Environment, 148: 124-133, 2014.

VAN VUUREN, J. A. J.; MEYER, J. H.; CLAASSENS, A. S. Potential use of near infrared reflectance monitoring in precision agriculture. Communications in Soil Science and Plant Analysis, 37: 2171-2184, 2006.

VERRA, VCS METHODOLOGY. VM0042 - Methodology for Improved Agricultural Land Management, 42: 1-113, 2020.

VISCARRA-ROSSEL, R. A.; BEHRENS, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 158: 46-54, 2010.

VISCARRA-ROSSEL, R. A. et al Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century. European Journal of Soil Science, 73: 1-9, 2022.

VISCARRA-ROSSEL, et al. A global spectral library to characterize the world’s soil. Earth-Science Reviews, 155, 198-230, 2016.

WIESMEIER, M. et al. Soil organic carbon storage as a key function of soils-A review of drivers and indicators at various scales. Geoderma, 333, 149-162, 2019.

WIGHT, J. P.; ASHWORTH, A. J.; ALLEN, F. L. Organic substrate, clay type, texture, and water influence on NIR carbon measurements. Geoderma, 261: 36-43, 2016.

ZHANG, Y. et al. Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China. Remote Sensing, 11: 1-26, 2019.

Downloads

Published

18-07-2023

Issue

Section

Agricultural Engineering