High-Impact & Significant Research Publications
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The article 'Artificial intelligence for geoscience: Progress, challenges, and perspectives' by Zhang et al. (2024) was published in the Innovation Journal (IF: 33.2).
The article ‘Climate change: Strategies for mitigation and adaptation’ by Wang et al. (2023) published in The Innovation Journal by ScienceDirect (IF: 33.2) was the most read review article in this journal in 2023. The paper summarizes evidence of climate change in Earth’s spheres, discusses emission pathways and drivers of climate change, and analyzes the impact of climate change on environmental and human health. We also explore strategies for climate change mitigation and adaptation and highlight key challenges for reversing and adapting to global climate change. Read Paper
A high-profile publication in the Earth Science Review J., 2016 (IF: 9.724) of a global team of researchers, including Dr. Grunwald, synthesized a spectral dataset and measured soil data to model various critical soil indicators at global scale. Big data analysis was used to develop global soil maps/models for carbon, clay, and other soil properties demonstrating the impact and mitigation potential under various change stressors, including global climate change and land use change, both causing unprecedented soil degradation.
Viscarra Rossel, R.A., T. Behrens, E. Ben-Dor, D.J. Brown, J.A.M. Demattê, K.D. Shepherd, Z. Shi, B. Stenberg, A. Stevens, V. Adamchuk, H. Aïchi, B.G. Barthès, H.M. Bartholomeus, A.D. Bayer, M. Bernoux, K. Böttcher, L. Brodský, C.W. Du, A. Chappell, Y. Fouad, V. Genot, C. Gomez, S. Grunwald, A. Gubler, C. Guerrero, C.B. Hedley, M. Knadel, H.J.M. Morrás, M. Nocita, L. Ramirez-Lopez, P. Roudier, E.M.R. Campos, P. Sanborn, V.M. Sellitto, K.A. Sudduth, B.G. Rawlins, C. Walter, L.A. Winowiecki, S.Y. Hong and W. Ji. 2016. A global spectral library to characterize the world’s soil. Earth-Science Reviews 155, 198–230. doi:10.1016/j.earscirev.2016.01.012. Read Paper
This research paper won the Pedometrics 2016 Best Paper award of the International Union of Soil Sciences.
The articles “Integrating spectral indices into prediction models of soil phosphorus in a subtropical wetland“ by Rivero, Grunwald, Binford and Osborne (2009) published in Remote Sensing of Environment J. (IF: 9.085), “Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction …” by Xu, Smith, Grunwald, Abd-Elrahman and Wani (2017) in the ISPRS J. of Photogrammetry and Remote Sensing (IF: 7.319), and several other remote sensing-oriented publications demonstrate creativity through development of new methods in remote-sensing supported digital soil mapping and modeling.
Xu Y., S.E. Smith, S. Grunwald, A. Abd-Elrahman and S. Wani. 2017. Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields. ISPRS J. of Photogrammetry and Remote Sensing 123, 1-19. doi.org/10.1016/j.isprsjprs.2016.11.001. --- See Fig. 6 below. Read Paper
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Rivero R.G., S. Grunwald, M.W. Binford and T.Z. Osborne. 2009. Integrating spectral indices into prediction models of soil phosphorus in a subtropical wetland. Remote Sensing of Environment J. 113: 2389-2403. doi:10.1016/j.rse.2009.07.015. Read Paper
The journal article published in the Environmental Modeling and Software J. 2016 (IF: 4.807) provided the first high-resolution gridded soil organic carbon (SOC) assessment and uncertainty assessment for the State of Florida, USA. SOC is critical to sustain soil health and security, it enhances soil nutrient holding to reduce adverse impacts to the environment and mitigates the effects of global climate change. In this publication a new holistic soil-environmental modeling framework was presented using the STEP-AWBH model framework, geospatial technologies, machine learning algorithms/artificial intelligence (AI), and remote sensing. The soil carbon maps have been incorporated in the Florida Forever Project, Department of Environmental Protection, Florida’s premier conservation and recreation lands acquisition program.
Xiong X., S. Grunwald, D.B. Myers, J. Kim, W.G. Harris and N.B. Comerford.. 2014. Holistic environmental soil-landscape modeling of soil organic carbon. Environmental Modeling and Software J. 57: 202-215. doi: 10.1016/j.envsoft.2014.03.004. Read Paper
The journal article by Grunwald et al (2011) in the Soil Sci. Soc. Am. J. presented a new conceptual soil factorial modeling framework called STEP-AWBH that has been adopted widely in digital soil mapping (DSM) to predict soil properties and classes (though not all studies cite this paper and just use the STEP-AWBH approach).
STEP-AWBH space-time soil model framework:
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S: soil, T: topography, E: ecology, P: parent material, geology, A: atmosphere/climate, W: water/hydrology, B: biota, and H: human variables/factors that determine soil formation
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Integration of spatially- and temporally explicit soil forming factors (STEP-AWBH factors; soil-environmental covariates) into a quantitative model approach (e.g., machine learning, Bayesian, or regression model) to infer on soil properties/classes and their evolution
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The STEP-AWBH factors are populated using geospatial technologies including remote and soil proximal sensing, measurements, or available databases
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Explicitly incorporates anthropogenic forcings including social, cultural, and economic data
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Incorporates bio-, topo-, litho-, pedo- and hydrosphere
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Fuses empirical and process-based knowledge
Grunwald S., J.A. Thompson and J.L. Boettinger. 2011. Digital soil mapping and modeling at continental scales – finding solutions for global issues. Soil Sci. Soc. Am. J. (SSSA 75th Anniversary Special Paper) 75(4): 1201-1213. doi:10.1016/j.geoderma.2018.12.037. Read Paper
Grunwald has published several articles in the Science of the Total Environment J. (IF: 6.551), among them the article “Integrative environmental modeling of soil carbon fractions based on a new latent variable model approach” in 2019 (Adi and Grunwald). This study presents a new two-step regression technique (2Step-R) combining linear regressions (i.e., Ridge Regression—RR and Bayesian Linear Regression) and latent variable models (i.e., Partial Least Squares Regression—PLSR and Sparse Bayesian Infinite Factor—SBIF) for the integration of mixed type soil-environmental datasets. 2Step-R allows categorical (e.g., land use types or legacy soil maps) and continuous STEP-AWBH soil-environmental predictor variables to be integrated in the modeling process. Several soil carbon fractions and soil organic carbon were modeled using the 2Step-R approach that outperformed other machine learning methods. This soil modeling research complements numerous of Grunwald's pedometrics and digital soil mapping/modeling oriented research published in international soil journals such as Geoderma (IF: 4.848).
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Adi S.H. and S. Grunwald. 2020. Integrative environmental modeling of soil carbon fractions based on a new latent variable model approach. Science of the Total Environment, 134566: 1–15. doi:10.1016/j.scitotenv.2019.134566. Read Paper
Grunwald's research team has published extensively on soil spectral modeling using visible/near-infrared spectra (VNIR) and mid-infrared spectra (MIR) to estimate numerous soil properties. Soil spectroscopy (proximal soil sensing) serves as a rapid and cost-effective technology to characterize soils. Machine learning algorithms, such as Random Forest, to model soils usually outperform more traditional soil prediction methods.
Review article in Advances in Agronomy J. (IF: 5.279)
Grunwald S., G.M. Vasques and R.G. Rivero. 2015. Fusion of soil and remote sensing data to model soil properties. In: Sparks, D.L. (Ed.), Advances in Agronomy, Vol. 131, pp. 1–109. Read Paper
Knox N.M., S. Grunwald, M.L. McDowell, G.L. Bruland, D.B. Myers, W.G. Harris. 2015. Modelling soil carbon fractions with VNIR and MIR spectroscopy. Geoderma 239-240: 229-239. doi: 10.1016/j.geoderma.2014.10.019 --- Fig. 3 and 4 below. Read Paper
Soil spectral library U.S.A.:
Clingensmith, C. M., & Grunwald, S. (2022). Predicting soil properties and interpreting Vis-NIR models from across continental United States. Sensors, 22(3187), 1–17. https://doi.org/10.3390/s22093187.
Sensors J. IF 3.847.
Grunwald, Katsutoshi ("Toshi") Mizuta in collaboration with economists were instrumental to launch a new pedo-econometric approach. This approach fuses two disciplines: pedometrics and econometrics (Mizuta et al., 2021). The idea undergirding pedo-econometrics is the optimization of soil health to enhance soil quality, resilience, and sustainability using economic methods. The Data Envelopment Analysis (DEA) is a pedo-econometric method that allows to simultaneously model input-output interactions between multiple soil and environmental factors. Pedo-econometrics incorporates economic-oriented strategies into the soil modeling process that allow farmers, regulators, and decision-makers to guide soil management considering potential efforts (labor and costs) to sustain or improve soil functionality.
Mizuta K., S. Grunwald, M.A. Phillips, A.R. Bacon, W.P. Cropper Jr. and C.B. Moss. 2021. Emergence of the pedo-econometric approach. Frontiers in Soil Science J. Pedometrics Section. doi:10.3389/fsoil.2021.656591. Read Paper
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Mizuta K., S. Grunwald, M.A. Phillips, C.B. Moss, A.R. Bacon and W.P. Cropper Jr. 2021. Sensitivity analysis of metafrontier data envelopment analysis for soil carbon sequestration efficiency. Ecological Indicator J. 125. Article 107602. doi:10.1016/j.ecolind.2021.107602. Read Paper
