Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-1219-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1219-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
10 Feb 2022
Methods for assessment of models |
| 10 Feb 2022
| 10 Feb 2022
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Danilo César de Mello
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Gustavo Vieira Veloso
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Marcos Guedes de Lana
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Fellipe Alcantara de Oliveira Mello
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Av. Pádua Dias, 11, CP 9,
Piracicaba, SP 13418-900, Brazil
Raul Roberto Poppiel
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Av. Pádua Dias, 11, CP 9,
Piracicaba, SP 13418-900, Brazil
Diego Ribeiro Oquendo Cabrero
Geography Department of Federal University of Mato Grosso do Sul, Av. Ranulpho Marques Leal, no. 3484, Distrito Industrial CEP
79610-100 Três Lagoas/MS, Brazil
Luis Augusto Di Loreto Di Raimo
Department of Geology and Natural Resources, Institute of
Geosciences, University of Campinas, Rua Carlos Gomes, 250, Cidade
Universitária, CEP 13083-855, Campinas/SP, Brazil
Carlos Ernesto Gonçalves Reynaud Schaefer
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Elpídio Inácio Fernandes Filho
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Emilson Pereira Leite
Department of Geology and Natural Resources, Institute of
Geosciences, University of Campinas, Rua Carlos Gomes, 250, Cidade
Universitária, CEP 13083-855, Campinas/SP, Brazil
José Alexandre Melo Demattê
CORRESPONDING AUTHOR
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Av. Pádua Dias, 11, CP 9,
Piracicaba, SP 13418-900, Brazil
Related authors
Danilo César de Mello, Clara Glória Oliveira Baldi, Cássio Marques Moquedace, Isabelle de Angeli Oliveira, Gustavo Vieira Veloso, Lucas Carvalho Gomes, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Edgar Batista de Medeiros Júnior, Fabio Soares de Oliveira, José João Lelis Leal de Souza Souza, Tiago Ferreira, and José A. M. Demattê
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-2, https://doi.org/10.5194/gmd-2024-2, 2024
Preprint under review for GMD
Short summary
Short summary
The study explores Maritime Antarctica's geology, shaped by periglacial forces, using pioneering gamma-spectrometric and magnetic surveys on igneous rocks due to limited Antarctic surveys. Machine learning predicts radionuclide and magnetic content based on terrain features, linking their distribution to landscape processes, morphometrics, lithology, and pedogeomorphology. Inaccuracies arise due to complex periglacial processes and landscape complexities.
Danilo César de Mello, Tiago Osório Ferreira, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Luis Augusto Di Loreto Di Raimo, Diego Ribeiro Oquendo Cabrero, José João Lelis Leal de Souza, Elpídio Inácio Fernandes-Filho, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, and José A. M. Demattê
SOIL Discuss., https://doi.org/10.5194/soil-2022-17, https://doi.org/10.5194/soil-2022-17, 2022
Revised manuscript not accepted
Short summary
Short summary
We proposed a different method to evaluate different intensities of weathering in a heterogeneous area (soils, geology and relief) and small number of samples. We use combined data from three geophysical sensors, clustering and machine learning (nested-leave-one-out-cross-validation) to distinguish weathering intensities and assess the relationship of these variables with weathering, relief, geology, and soil types and attributes. and we obtained satisfactory performances of models evaluation.
Danilo César de Mello, Clara Glória Oliveira Baldi, Cássio Marques Moquedace, Isabelle de Angeli Oliveira, Gustavo Vieira Veloso, Lucas Carvalho Gomes, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Edgar Batista de Medeiros Júnior, Fabio Soares de Oliveira, José João Lelis Leal de Souza Souza, Tiago Ferreira, and José A. M. Demattê
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-2, https://doi.org/10.5194/gmd-2024-2, 2024
Preprint under review for GMD
Short summary
Short summary
The study explores Maritime Antarctica's geology, shaped by periglacial forces, using pioneering gamma-spectrometric and magnetic surveys on igneous rocks due to limited Antarctic surveys. Machine learning predicts radionuclide and magnetic content based on terrain features, linking their distribution to landscape processes, morphometrics, lithology, and pedogeomorphology. Inaccuracies arise due to complex periglacial processes and landscape complexities.
Danilo César de Mello, Tiago Osório Ferreira, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Luis Augusto Di Loreto Di Raimo, Diego Ribeiro Oquendo Cabrero, José João Lelis Leal de Souza, Elpídio Inácio Fernandes-Filho, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, and José A. M. Demattê
SOIL Discuss., https://doi.org/10.5194/soil-2022-17, https://doi.org/10.5194/soil-2022-17, 2022
Revised manuscript not accepted
Short summary
Short summary
We proposed a different method to evaluate different intensities of weathering in a heterogeneous area (soils, geology and relief) and small number of samples. We use combined data from three geophysical sensors, clustering and machine learning (nested-leave-one-out-cross-validation) to distinguish weathering intensities and assess the relationship of these variables with weathering, relief, geology, and soil types and attributes. and we obtained satisfactory performances of models evaluation.
Wartini Ng, Budiman Minasny, Wanderson de Sousa Mendes, and José Alexandre Melo Demattê
SOIL, 6, 565–578, https://doi.org/10.5194/soil-6-565-2020, https://doi.org/10.5194/soil-6-565-2020, 2020
Short summary
Short summary
The number of samples utilised to create predictive models affected model performance. This research compares the number of samples needed by a deep learning model to outperform the traditional machine learning models using visible near-infrared spectroscopy data for soil properties predictions. The deep learning model was found to outperform machine learning models when the sample size was above 2000.
Andre Carnieletto Dotto, Jose A. M. Demattê, Raphael A. Viscarra Rossel, and Rodnei Rizzo
SOIL, 6, 163–177, https://doi.org/10.5194/soil-6-163-2020, https://doi.org/10.5194/soil-6-163-2020, 2020
Short summary
Short summary
The objective of this study was to develop a soil grouping system based on spectral, climate, and terrain variables with the aim of developing a quantitative way to classify soils. To derive the new system, we applied the above-mentioned variables using cluster analysis and defined eight groups or "soil environment groupings" (SEGs). The SEG system facilitated the identification of groups with similar characteristics using not only soil but also environmental variables for their distinction.
Lars A. Meier, Patryk Krauze, Isabel Prater, Fabian Horn, Carlos E. G. R. Schaefer, Thomas Scholten, Dirk Wagner, Carsten W. Mueller, and Peter Kühn
Biogeosciences, 16, 2481–2499, https://doi.org/10.5194/bg-16-2481-2019, https://doi.org/10.5194/bg-16-2481-2019, 2019
Short summary
Short summary
James Ross Island offers the opportunity to study the undisturbed interplay of microbial activity and pedogenesis. Soils from two sites representing coastal and inland conditions were chosen and analyzed with a wide range of techniques to describe soil properties. We are able to show that coastal conditions go along with more intense weathering and therefore favor soil formation and that microbial communities are initially more affected by weathering and structure than by chemical parameters.
E. L. Poelking, C. E. R. Schaefer, E. I. Fernandes Filho, A. M. de Andrade, and A. A. Spielmann
Solid Earth, 6, 583–594, https://doi.org/10.5194/se-6-583-2015, https://doi.org/10.5194/se-6-583-2015, 2015
R. F. M. Michel, C. E. G. R. Schaefer, F. M. B. Simas, M. R. Francelino, E. I. Fernandes-Filho, G. B. Lyra, and J. G. Bockheim
Solid Earth, 5, 1361–1374, https://doi.org/10.5194/se-5-1361-2014, https://doi.org/10.5194/se-5-1361-2014, 2014
Related subject area
Earth and space science informatics
Checking the consistency of 3D geological models
The effect of lossy compression of numerical weather prediction data on data analysis: a case study using enstools-compression 2023.11
GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity
Random forests with spatial proxies for environmental modelling: opportunities and pitfalls
An improved global pressure and zenith wet delay model with optimized vertical correction considering the spatiotemporal variability in multiple height-scale factors
kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation
Remote sensing-based high-resolution mapping of the forest canopy height: some models are useful, but might they be even more if combined?
Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6
Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression
Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data
The 4D reconstruction of dynamic geological evolution processes for renowned geological features
Moving beyond post-hoc XAI: Lessons learned from dynamical climate modeling
Machine learning for numerical weather and climate modelling: a review
Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation
The Common Community Physics Package (CCPP) Framework v6
Causal deep learning models for studying the Earth system
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA
SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery
Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data
Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion
LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0
CSDMS: a community platform for numerical modeling of Earth surface processes
Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator
Turbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuaries
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Copula-based synthetic data augmentation for machine-learning emulators
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
A spatially explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)
S-SOM v1.0: a structural self-organizing map algorithm for weather typing
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China
Current status on the need for improved accessibility to climate models code
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time
A new end-to-end workflow for the Community Earth System Model (version 2.0) for the Coupled Model Intercomparison Project Phase 6 (CMIP6)
HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution
Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)
Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation
Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
Global hydro-climatic biomes identified via multitask learning
A run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applications
An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping
High-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)
Marion N. Parquer, Eric A. de Kemp, Boyan Brodaric, and Michael J. Hillier
Geosci. Model Dev., 18, 71–100, https://doi.org/10.5194/gmd-18-71-2025, https://doi.org/10.5194/gmd-18-71-2025, 2025
Short summary
Short summary
This is a proof-of-concept paper outlining a general approach to how 3D geological models would be checked to be geologically
reasonable. We do this with a consistency-checking tool that looks at geological feature pairs and their spatial, temporal, and internal polarity characteristics. The idea is to assess if geological relationships from a specific 3D geological model match what is allowed in the real world from the perspective of geological principles.
Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig
Geosci. Model Dev., 17, 8909–8925, https://doi.org/10.5194/gmd-17-8909-2024, https://doi.org/10.5194/gmd-17-8909-2024, 2024
Short summary
Short summary
Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5 to 150 times) without compromising the data's scientific value. We developed a user-friendly tool called
enstools-compressionthat makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du
Geosci. Model Dev., 17, 8455–8468, https://doi.org/10.5194/gmd-17-8455-2024, https://doi.org/10.5194/gmd-17-8455-2024, 2024
Short summary
Short summary
In geography, understanding how relationships between different factors change over time and space is crucial. This study implements two neural-network-based spatiotemporal regression models and an open-source Python package named Geographically Neural Network Weighted Regression to capture relationships between factors. This makes it a valuable tool for researchers in fields such as environmental science, urban planning, and public health.
Carles Milà, Marvin Ludwig, Edzer Pebesma, Cathryn Tonne, and Hanna Meyer
Geosci. Model Dev., 17, 6007–6033, https://doi.org/10.5194/gmd-17-6007-2024, https://doi.org/10.5194/gmd-17-6007-2024, 2024
Short summary
Short summary
Spatial proxies, such as coordinates and distances, are often used as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated the conditions under which their use is appropriate. We found that spatial proxies are not always beneficial and should not be used as a default approach without careful consideration. We also provide insights into the reasons behind their suitability, how to detect them, and potential alternatives.
Chunhua Jiang, Xiang Gao, Huizhong Zhu, Shuaimin Wang, Sixuan Liu, Shaoni Chen, and Guangsheng Liu
Geosci. Model Dev., 17, 5939–5959, https://doi.org/10.5194/gmd-17-5939-2024, https://doi.org/10.5194/gmd-17-5939-2024, 2024
Short summary
Short summary
With ERA5 hourly data, we show spatiotemporal characteristics of pressure and zenith wet delay (ZWD) and propose an empirical global pressure and ZWD grid model with a broader operating space which can provide accurate pressure, ZWD, zenith hydrostatic delay, and zenith tropospheric delay estimates for any selected time and location over globe. IGPZWD will be of great significance for the tropospheric augmentation in real-time GNSS positioning and atmospheric water vapor remote sensing.
Jan Linnenbrink, Carles Milà, Marvin Ludwig, and Hanna Meyer
Geosci. Model Dev., 17, 5897–5912, https://doi.org/10.5194/gmd-17-5897-2024, https://doi.org/10.5194/gmd-17-5897-2024, 2024
Short summary
Short summary
Estimation of map accuracy based on cross-validation (CV) in spatial modelling is pervasive but controversial. Here, we build upon our previous work and propose a novel, prediction-oriented k-fold CV strategy for map accuracy estimation in which the distribution of geographical distances between prediction and training points is taken into account when constructing the CV folds. Our method produces more reliable estimates than other CV methods and can be used for large datasets.
Nikola Besic, Nicolas Picard, Cédric Vega, Lionel Hertzog, Jean-Pierre Renaud, Fajwel Fogel, Agnès Pellissier-Tanon, Gabriel Destouet, Milena Planells-Rodriguez, and Philippe Ciais
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-95, https://doi.org/10.5194/gmd-2024-95, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The creation of advanced mapping models for forest attributes, utilizing remote sensing data and incorporating machine or deep learning methods, has become a key area of interest in the domain of forest observation and monitoring. This paper introduces a method where we blend and collectively interpret five models dedicated to estimating forest canopy height. We achieve this through Bayesian model averaging, offering a comprehensive approach to height estimation in forest ecosystems.
Lars Hoffmann, Kaveh Haghighi Mood, Andreas Herten, Markus Hrywniak, Jiri Kraus, Jan Clemens, and Mingzhao Liu
Geosci. Model Dev., 17, 4077–4094, https://doi.org/10.5194/gmd-17-4077-2024, https://doi.org/10.5194/gmd-17-4077-2024, 2024
Short summary
Short summary
Lagrangian particle dispersion models are key for studying atmospheric transport but can be computationally intensive. To speed up simulations, the MPTRAC model was ported to graphics processing units (GPUs). Performance optimization of data structures and memory alignment resulted in runtime improvements of up to 75 % on NVIDIA A100 GPUs for ERA5-based simulations with 100 million particles. These optimizations make the MPTRAC model well suited for future high-performance computing systems.
Mohamad Hakam Shams Eddin and Juergen Gall
Geosci. Model Dev., 17, 2987–3023, https://doi.org/10.5194/gmd-17-2987-2024, https://doi.org/10.5194/gmd-17-2987-2024, 2024
Short summary
Short summary
In this study, we use deep learning and a climate simulation to predict the vegetation health as it would be observed from satellites. We found that the developed model can help to identify regions with a high risk of agricultural drought. The main applications of this study are to estimate vegetation products for periods where no satellite data are available and to forecast the future vegetation response to climate change based on climate scenarios.
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
Short summary
Short summary
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024, https://doi.org/10.5194/gmd-17-1133-2024, 2024
Short summary
Short summary
The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked.
Jiateng Guo, Zhibin Liu, Xulei Wang, Lixin Wu, Shanjun Liu, and Yunqiang Li
Geosci. Model Dev., 17, 847–864, https://doi.org/10.5194/gmd-17-847-2024, https://doi.org/10.5194/gmd-17-847-2024, 2024
Short summary
Short summary
This study proposes a 3D and temporally dynamic (4D) geological modeling method. Several simulation and actual cases show that the 4D spatial and temporal evolution of regional geological formations can be modeled easily using this method with smooth boundaries. The 4D modeling system can dynamically present the regional geological evolution process under the timeline, which will be helpful to the research and teaching on the formation of typical and complex geological features.
Ryan O'Loughlin, Dan Li, and Travis O'Brien
EGUsphere, https://doi.org/10.5194/egusphere-2023-2969, https://doi.org/10.5194/egusphere-2023-2969, 2024
Short summary
Short summary
We draw from traditional climate modeling practices to make recommendations for AI-driven climate science. In particular, we show how component-level understanding–which is obtained when scientists can link model behavior to parts within the overall model–should guide the development and evaluation of AI models. Better understanding can lead to a stronger basis for trust in these models. We highlight several examples to demonstrate.
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, https://doi.org/10.5194/gmd-16-6433-2023, 2023
Short summary
Short summary
Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
Short summary
Short summary
We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
Short summary
Short summary
Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023, https://doi.org/10.5194/gmd-16-5113-2023, 2023
Short summary
Short summary
Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.
Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du
Geosci. Model Dev., 16, 2777–2794, https://doi.org/10.5194/gmd-16-2777-2023, https://doi.org/10.5194/gmd-16-2777-2023, 2023
Short summary
Short summary
We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
Short summary
Short summary
The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
Short summary
Short summary
A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
Geosci. Model Dev., 16, 1925–1936, https://doi.org/10.5194/gmd-16-1925-2023, https://doi.org/10.5194/gmd-16-1925-2023, 2023
Short summary
Short summary
Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
Short summary
Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans
Geosci. Model Dev., 15, 7933–7976, https://doi.org/10.5194/gmd-15-7933-2022, https://doi.org/10.5194/gmd-15-7933-2022, 2022
Short summary
Short summary
The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
Short summary
Short summary
The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022, https://doi.org/10.5194/gmd-15-5857-2022, 2022
Short summary
Short summary
We investigate and compare the theoretical and computational characteristics of several absorbing boundary conditions (ABCs) for the full-waveform inversion (FWI) problem. The different ABCs are implemented in an optimized computational framework called Devito. The computational efficiency and memory requirements of the ABC methods are evaluated in the forward and adjoint wave propagators, from simple to realistic velocity models.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Short summary
LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
Short summary
Short summary
There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Paul F. Baumeister and Lars Hoffmann
Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022, https://doi.org/10.5194/gmd-15-1855-2022, 2022
Short summary
Short summary
The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low-cost prefactor on GPUs compared to general-purpose processors (CPUs) enables future large retrieval campaigns for multi-channel data from infrared sounders aboard low-orbit satellites. The validated research software JURASSIC is available in the public domain.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
Short summary
Short summary
Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
Short summary
Short summary
The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
Short summary
Short summary
The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
Short summary
Short summary
We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
Short summary
Short summary
A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
Short summary
Short summary
We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
Short summary
Short summary
The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
Short summary
Short summary
This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
Short summary
Short summary
Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
Short summary
Short summary
This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all development teams and research centres to make public the models they use to produce scientific results.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
Short summary
Short summary
Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164, https://doi.org/10.5194/gmd-13-6149-2020, https://doi.org/10.5194/gmd-13-6149-2020, 2020
Short summary
Short summary
This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581, https://doi.org/10.5194/gmd-13-5567-2020, https://doi.org/10.5194/gmd-13-5567-2020, 2020
Short summary
Short summary
Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
Short summary
Short summary
Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
Short summary
Short summary
The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
Short summary
Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Gianandrea Mannarini and Lorenzo Carelli
Geosci. Model Dev., 12, 3449–3480, https://doi.org/10.5194/gmd-12-3449-2019, https://doi.org/10.5194/gmd-12-3449-2019, 2019
Short summary
Short summary
The VISIR ship-routing model is updated in order to deal with ocean currents.
The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628, https://doi.org/10.5194/gmd-12-613-2019, https://doi.org/10.5194/gmd-12-613-2019, 2019
Short summary
Short summary
We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
Short summary
Short summary
Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
Short summary
Short summary
Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539, https://doi.org/10.5194/gmd-11-2525-2018, https://doi.org/10.5194/gmd-11-2525-2018, 2018
Short summary
Short summary
Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427, https://doi.org/10.5194/gmd-11-2419-2018, https://doi.org/10.5194/gmd-11-2419-2018, 2018
Short summary
Short summary
For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
Cited articles
Agbu, P. A., Fehrenbacher, D. J., and Jansen, I. J.: Soil property
relationships with SPOT satellite digital data in east central Illinois,
Soil Sci. Soc. Am. J., 54, 807–812, 1990.
Alvares, C. A., Stape, J. L., Sentelhas, P. C., De Moraes Gonçalves, J.
L., and Sparovek, G.: Köppen's climate classification map for Brazil,
Meteorol. Z., 22, 711–728, https://doi.org/10.1127/0941-2948/2013/0507,
2013.
Amundson, R., Berhe, A. A., Hopmans, J. W., Olson, C., Sztein, A. E., and
Sparks, D. L.: Soil and human security in the 21st century, Science, 348, 6235, https://doi.org/10.1126/science.1261071, 2015.
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G.
B. M., Hong, S. Y., Lagacherie, P., Lelyk, G., McBratney, A. B., McKenzie,
N. J., Mendonca-Santos, M. d. L., Minasny, B., Montanarella, L., Odeh, I. O.
A., Sanchez, P. A., Thompson, J. A., and Zhang, G.-L.: GlobalSoilMap: Toward
a Fine-Resolution Global Grid of Soil Properties, Adv. Agron.,
125, 93–134, 2014.
Ayoubi, S., Abazari, P., and Zeraatpisheh, M.: Soil great groups
discrimination using magnetic susceptibility technique in a semi-arid
region, central Iran, Arab. J. Geosci., 11, 1–12,
https://doi.org/10.1007/s12517-018-3941-4, 2018.
Bai, W., Kong, L., and Guo, A.: Effects of physical properties on electrical
conductivity of compacted lateritic soil, J. Rock Mech. Geotech. Eng., 5,
406–411, https://doi.org/10.1016/j.jrmge.2013.07.003, 2013.
Ballabio, C., Panagos, P., and Monatanarella, L.: Mapping topsoil physical
properties at European scale using the LUCAS database, Geoderma, 261,
110–123, https://doi.org/10.1016/j.geoderma.2015.07.006, 2016.
Barbuena, D., de Souza Filho, C. R., Leite, E. P., Miguel, E., de Assis,
R. R., Xavier, R. P., Ferreira, F. J. F., and Paes de Barros, A. J.: Airborne
geophysical data analysis applied to geological interpretation in the Alta
Floresta Gold Province, MT, Rev. Bras. Geofis., 31, 169–186, 2013.
Batty, M. and Torrens, P. M.: Modelling complexity: the limits to
prediction, Cybergeo Eur. J. Geogr., https://doi.org/10.4000/cybergeo.1035, 2001.
Bauer, F. C.: Water flow paths in soils of an undisturbed and landslide
affected mature montane rainforest in South Ecuador, PhD thesis, University of Bayreuth, Germany, available at: https://epub.uni-bayreuth.de/395/ (last access: 2 February 2022), 2010.
Bazaglia Filho, O., Rizzo, R., Lepsch, I. F., Prado, H. D., Gomes, F. H., Mazza, J. A., and Demattê, J. A. M.: Comparação entre mapas de solos detalhados obtidos pelos métodos convencional e digital em uma área de geologia complexa, Rev. Bras. Cienc. Solo, 37, 1136–1148, available at: https://www.scielo.br/j/rbcs/a/cbGQmJwJ3LqpznTnM5zktXf/?format=pdf&lang=en, 2013.
Bazaglia Filho, O., Rizzo, R., Lepsch, I. F., do Prado, H., Gomes, F. H.,
Mazza, J. A., and Demattê, J. A. M.: Comparison between detailed digital
and conventional soil maps of an area with complex geology, Rev. Bras.
Cienc. Solo, 37, 1136–1148, https://doi.org/10.1590/s0100-06832013000500003,
2013.
Beamish, D.: Gamma ray attenuation in the soils of Northern Ireland, with
special reference to peat, J. Environ. Radioactiv., 115, 13–27,
https://doi.org/10.1016/j.jenvrad.2012.05.031, 2013.
Beamish, D.: Relationships between gamma-ray attenuation and soils in SW
England, Geoderma, 259–260, 174–186, https://doi.org/10.1016/j.geoderma.2015.05.018,
2015.
Beckett, P. H. T.: Soil variability: a review, Soils Fertil., 34, 1–15,
1971.
Bigham, J. M., Fitzpatrick, R. W., and Schulze, D. G.: Iron oxides, in: Soil mineralogy with environmental applications, 7th edn., edited by: Dixon, J. B. and Schulze, D. G., SSSA Book Series, 323–366, https://doi.org/10.2136/sssabookser7.c10, 2002.
Blundell, A., Dearing, J. A., Boyle, J. F., and Hannam, J. A.: Controlling
factors for the spatial variability of soil magnetic susceptibility across
England and Wales, Earth-Sci. Rev., 95, 158–188,
https://doi.org/10.1016/j.earscirev.2009.05.001, 2009.
Bockheim, J. G., Gennadiyev, A. N., Hartemink, A. E., and Brevik, E. C.: Soil-forming factors and Soil Taxonomy, Geoderma, 226, 231–237, 2014.
Breemen, N. and Buurman, P.: Soil Formation, 2nd edn., Laboratory of Soil
Science and Geology, Kluwer, New York, Boston, Dordrecht, London, Moscow, https://doi.org/10.1017/CBO9781107415324.004, 2003.
Brungard, C. W., Boettinger, J. L., Duniway, M. C., Wills, S. A., and Edwards Jr., T. C.: Machine learning for predicting soil classes in three semi-arid landscapes, Geoderma, 239, 68–83, 2015.
Camargo, L. A., Marques Júnior, J., Pereira, G. T., and Bahia de Souza, A. S. R.: Clay mineralogy and magnetic susceptibility of Oxisols in geomorphic
surfaces, Sci. Agric., 71, 244–256, https://doi.org/10.1590/S0103-90162014000300010,
2014.
Camargo, O. A., Moniz, A. C., Jorge, J. A., and Valadares, J. M. A. S.:
Métodos de análise química, mineralógica e física de
solos do Instituto Agronômico do estado de São Paulo, Bol.
téc., 106, 94, 1986.
Cardoso, R. and Dias, A. S.: Study of the electrical resistivity of
compacted kaolin based on water potential, Eng. Geol., 226, 1–11, https://doi.org/10.1016/j.enggeo.2017.04.007, 2017.
Clevers, J. G. P. W., Van Der Heijden, G. W. A. M., Verzakov, S., and
Schaepman, M. E.: Estimating grassland biomass using SVM band shaving of
hyperspectral data, Photogramm. Eng. Rem. S., 73, 1141–1148,
https://doi.org/10.14358/PERS.73.10.1141, 2007.
Coblinski, J. A., Inda, A. V., Demattê, J. A. M., Dotto, A. C.,
Gholizadeh, A., and Giasson, É.: Identification of minerals in
subtropical soils with different textural classes by VIS–NIR–SWIR
reflectance spectroscopy, Catena, 203, 105334, https://doi.org/10.1016/j.catena.2021.105334, 2021.
Correia, M. G., Leite, E. P., and de Souza Filho, C. R.: Comparação
de métodos de estimativa de profundidades de fontes magnéticas
utilizando dados aeromagnéticos da província mineral de
Carajás, Pará, Braz. J. Geophys., 28, 411–426, 2010.
Corwin, D. L., Lesch, S. M., Shouse, P. J., Soppe, R., and Ayars, J. E.:
Identifying Soil Properties that Influence Cotton Yield Using Soil Sampling
Directed by Apparent Soil Electrical Conductivity, 95, 352–364, 2003.
Cracknell, M. J. and Reading, A. M.: The upside of uncertainty:
Identification of lithology contact zones from airborne geophysics and
satellite data using random forests and support vector machines, Geophysics,
78, WB113–WB126, https://doi.org/10.1190/GEO2012-0411.1, 2013.
Da Costa, A. C. S., Bigham, J. M., Rhoton, F. E., and Traina, S. J.:
Quantification and characterization of maghemite in soils derived from
volcanic rocks in southern Brazil, Clay. Clay Miner., 47, 466–473,
https://doi.org/10.1346/CCMN.1999.0470408, 1999.
Damaceno, J. G., de Castro, D. L., Valcácio, S. N., and Souza, Z. S.:
Magnetic and gravity modeling of a Paleogene diabase plug in Northeast
Brazil, J. Appl. Geophys., 136, 219–230, https://doi.org/10.1016/j.jappgeo.2016.11.006,
2017.
Darst, B. F., Malecki, K. C., and Engelman, C. D.: Using recursive feature
elimination in random forest to account for correlated variables in high
dimensional data, BMC Genet., 19, 65, https://doi.org/10.1186/s12863-018-0633-8, 2018.
De Jong, E., Pennock, D. J., and Nestor, P. A.: Magnetic susceptibility of
soils in different slope positions in Saskatchewan, Canada, Catena, 40,
291–305, https://doi.org/10.1016/S0341-8162(00)00080-1, 2000.
de Mello, D. C., Demattê, J. A., Silvero, N. E., Di Raimo, L. A., Poppiel, R. R., Mello, F. A., Souza, A. B., Safanelli, J.
L., Resende, M. E. B., and Rizzo, R.: Soil magnetic susceptibility and its
relationship with naturally occurring processes and soil attributes in
pedosphere, in a tropical environment, Geoderma, 372, 114364,
https://doi.org/10.1016/j.geoderma.2020.114364, 2020.
De Souza Bahia, A. S. R., Marques, J., La Scala, N., Pellegrino Cerri, C. E.,
and Camargo, L. A.: Prediction and mapping of soil attributes using diffuse
reflectance spectroscopy and magnetic susceptibility, Soil Sci. Soc. Am. J.,
81, 1450–1462, 2017.
Demattê, J. A. M., Galdos, M. V, Guimarães, R. V, Genú, A. M.,
Nanni, M. R., and Zullo, J.: Quantification of tropical soil attributes
from ETM+/LANDSAT-7 data, Int. J. Remote Sens., 28, 3813–3829, 2007.
Demattê, J. A. M., Horák-Terra, I., Beirigo, R. M., da Silva Terra, F., Marques, K. P. P., Fongaro, C. T., Silva, A. C., and Vidal-Torrado, P.:
Genesis and properties of wetland soils by VIS-NIR-SWIR as a technique for
environmental monitoring, J. Environ. Manage., 197, 50–62,
https://doi.org/10.1016/j.jenvman.2017.03.014, 2017.
Demattê, J. A. M., Dotto, A. C., Paiva, A. F. S., Sato, M. V., Dalmolin,
R. S. D., do Socorro B. de Araújo, M., da Silva, E. B., Nanni, M. R., ten
Caten, A., Noronha, N. C., Lacerda, M. P. C., de Araújo Filho, J. C.,
Rizzo, R., Bellinaso, H., Francelino, M. R., Schaefer, C. E. G. R., Vicente,
L. E., dos Santos, U. J., de Sá Barretto Sampaio, E. V., Menezes, R. S.
C., de Souza, J. J. L. L., Abrahão, W. A. P., Coelho, R. M., Grego, C.
R., Lani, J. L., Fernandes, A. R., Gonçalves, D. A. M., Silva, S. H. G.,
de Menezes, M. D., Curi, N., Couto, E. G., dos Anjos, L. H. C., Ceddia, M.
B., Pinheiro, É. F. M., Grunwald, S., Vasques, G. M., Marques
Júnior, J., da Silva, A. J., de Vasconcelos Barreto, M. C., Nóbrega, G. N., da Silva, M. Z., de Souza, S. F., Valladares, G. S., Viana, J. H. M., da
Silva Terra, F., Horák-Terra, I., Fiorio, P. R., da Silva, R. C., Frade
Júnior, E. F., Lima, R. H. C., Alba, J. M. F., de Souza Junior, V. S.,
Brefin, M. D. L. M. S., Ruivo, M. D. L. P., Ferreira, T. O., Brait, M. A.,
Caetano, N. R., Bringhenti, I., de Sousa Mendes, W., Safanelli, J. L.,
Guimarães, C. C. B., Poppiel, R. R., Barros e Souza, A., Quesada, C. A., and do Couto, H. T. Z.: The Brazilian Soil Spectral Library (BSSL): A general
view, application and challenges, Geoderma, 354, 113793,
https://doi.org/10.1016/j.geoderma.2019.05.043, 2019.
Dickson, B. L. and Scott, K. M.: Interpretation of aerial gamma-ray surveys – adding the geochemical factors, AGSO J. Aust. Geol. Geophys., 17,
187–200, 1997.
Dobos, E.: The appliction of remote sensing and teain modeling to soil characterization, in: Innovative Soil-Plant Systems for sustainable Agricultural Practices, Organization for Economic, 328–348, ISBN 9789264099715, 2003.
Domsch, H. and Giebel, A.: Estimation of soil textural features from soil
electrical conductivity recorded using the EM38, Precis. Agric., 5,
389–409, https://doi.org/10.1023/B:PRAG.0000040807.18932.80, 2004.
Dragovic, S. and Onjia, A.: Classification of soil samples according to
geographic origin using gamma-ray spectrometry and pattern recognition
methods, Appl. Radiat. Isotopes, 65, 218–224,
https://doi.org/10.1016/j.apradiso.2006.07.005, 2007.
EMBRAPA: Documentos 132 Manual de Métodos de, Embrapa, 230, 1517–2627,
2011.
Farzamian, M., Monteiro Santos, F. A., and Khalil, M. A.: Application of EM38
and ERT methods in estimation of saturated hydraulic conductivity in
unsaturated soil, J. Appl. Geophys., 112, 175–189,
https://doi.org/10.1016/j.jappgeo.2014.11.016, 2015.
Ferreira, R. G., da Silva, D. D., Elesbon, A. A. A., Fernandes-Filho, E. I., Veloso, G. V., de Souza Fraga, M., and Ferreira, L. B.: Machine learning models for streamflow regionalization in a tropical watershed, J. Environ. Manage., 280, 111713, https://doi.org/10.1016/j.jenvman.2020.111713, 2021.
Fioriob, P. R.: Estimation of Soil Properties by Orbital and Laboratory
Reflectance Means and its Relation with Soil Classification, Open Remote
Sens. J., 2, 12–23, available at: https://www.researchgate.net/publication/252662765_Estimation_of_Soil_Properties_by_Orbital_and_Laboratory_Reflectance_Means_and_its_Relation_with_Soil_Classification (last access: 9 February 2022), 2013.
Fongaro, C. T., Demattê, J. A. M., Rizzo, R., Safanelli, J. L., De Sousa Mendes, W., Dotto, A. C., Vicente, L. E., Franceschini, M. H. D., and Ustin, S. L.: Improvement of clay and sand quantification based on a novel approach
with a focus on multispectral satellite images, Remote Sens., 10, 1555, https://doi.org/10.3390/rs10101555, 2018.
Frihy, O. E., Lotfy, M. F., and Komar, P. D.: Spatial variations in heavy
minerals and patterns of sediment sorting along the Nile Delta, Egypt,
Sediment. Geol., 97, 33–41, 1995.
Geonics, E. M.: EM38 Ground Conductivity Meter Operating Manual, Geonics
Ltd., Ontario Mississauga, ON, Canada, 32, 2002.
Greve, M. B. and Malone, B. P.: High-Resolution 3-D Mapping of Soil Texture
in Denmark, High‐resolution 3‐D mapping of soil texture in Denmark, Soil. Sci. Soc. Am. J. , 77, 860–876, 2013.
Grimley, D. A., Arruda, N. K., and Bramstedt, M. W.: Using magnetic
susceptibility to facilitate more rapid, reproducible and precise
delineation of hydric soils in the midwestern USA, Catena, 58, 183–213,
https://doi.org/10.1016/j.catena.2004.03.001, 2004.
Harris, J. R. and Grunsky, E. C.: Computers & Geosciences Predictive
lithological mapping of Canada's North using Random Forest classification applied to geophysical and geochemical data, Comput. Geosci., 80,
9–25, https://doi.org/10.1016/j.cageo.2015.03.013, 2015.
Heil, K. and Schmidhalter, U.: Theory and Guidelines for the Application of
the Geophysical Sensor EM38, Sensors, 19, 4293, 2019.
Hendrickx, J. M., Wraith, J. M., Corwin, D. L., and Kachanoski, R. G.: Miscible solute transport, in: Methods of soil analysis. Part 4. Physical methods, edited by: Dane, J. H. and Topp, G. C., SSSA Book Series 5, Madison, WI., 1253–1321, ISBN 9780891188414, 2002.
Henrique, S., Silva, G., Silva, E. A., Poggere, G. C., Linares, A., Junior,
P., Gabriele, M., Gonçalves, M., Roberto, L., Guilherme, G., and Curi,
N.: Soils and Plant Nutrition Modeling and prediction of sulfuric acid
digestion analyses data from PXRF spectrometry in tropical soils, Sci.
Agric., 77, https://doi.org/10.1590/1678-992X-2018-0132, 2018.
Heuvelink, G. B. M. and Webster, R.: Modelling soil variation: past,
present, and future, Geoderma, 100, 269–301, 2001.
Honeyborne, I., McHugh, T. D., Kuittinen, I., Cichonska, A., Evangelopoulos,
D., Ronacher, K., van Helden, P. D., Gillespie, S. H., Fernandez-Reyes, D.,
Walzl, G., Rousu, J., Butcher, P. D., and Waddell, S. J.: Profiling
persistent tubercule bacilli from patient sputa during therapy predicts
early drug efficacy, BMC Med., 14, 1–13, https://doi.org/10.1186/s12916-016-0609-3,
2016.
Hothorn, T.: CRAN task view: Machine learning & statistical learning, https://CRAN.R-project.org/view=MachineLearning (last access: 8 February 2022), 2021.
Hounkpatin, O. K. L., Op, F., Hipt, D., Yaovi, A., Welp, G., and Amelung, W.:
Catena Soil organic carbon stocks and their determining factors in the Dano
catchment (Southwest Burkina Faso), Catena, 166, 298–309,
https://doi.org/10.1016/j.catena.2018.04.013, 2018.
IUSS Working Group WRB: World reference base for soil resources 2014 –
International soil classification system for naming soils and creating
legends for soil maps, World Soil Resources Report, 106, 12–21, 2014.
Jafarzadeh, A. A., Pal, M., Servati, M., Fazeli Fard, M. H., and Ghorbani, M.
A.: Comparative analysis of support vector machine and artificial neural
network models for soil cation exchange capacity prediction, Int. J.
Environ. Sci. Te., 13, 87–96, https://doi.org/10.1007/s13762-015-0856-4, 2016.
Javadi, S. H., Munnaf, M. A., and Mouazen, A. M.: Fusion of Vis-NIR and XRF
spectra for estimation of key soil attributes, Geoderma, 385, 114851, https://doi.org/10.1016/j.geoderma.2020.114851, 2021.
Jenny, H.: Factors of soil formation: A system of quantitative pedology,
Dover Publication, New York, USA, 1994.
Jiménez, C., Benavides, J., Ospina-Salazar, D. I., Zúñiga, O.,
Ochoa, O., and Mosquera, C.: Relationship between physical properties and the
magnetic susceptibility in two soils of Valle del Cauca Relación entre
propiedades físicas y la susceptibilidad magnética en dos suelos
del Valle del Cauca, Cauca. Rev. Cienc. Agri., 34, 33–45, https://doi.org/10.22267/rcia.173402.70, 2017.
Johnston, M. A., Savage, M. J., Moolman, J. H., and du Plessis, H. M.:
Evaluation of Calibration Methods for Interpreting Soil Salinity from
Electromagnetic Induction Measurements, Soil Sci. Soc. Am. J., 61,
1627–1633, https://doi.org/10.2136/sssaj1997.03615995006100060013x, 1997.
Jung, Y., Lee, J., Lee, M., Kang, N., and Lee, I.: Probabilistic analytical
target cascading using kernel density estimation for accurate uncertainty
propagation, Struct. Multidiscip. O., 61, 2077–2095, 2020.
Kämpf, N. and Curi, N.: Óxidos de ferro: indicadores de ambientes
pedogênicos e geoquímicos, Tóp. ciênc. solo, 1, 107–138, 2000.
Karpachevskii, L O.: A book on the pedosphere of the earth
Eurasian Soil Sci., 44, 832–833, https://doi.org/10.1134/S1064229311070088, 2011.
Kuhn, M. and Johnson, K.: Applied predictive modeling, 26, Springer, New York, 13, ISBN 9781461468493, available at: https://link.springer.com/book/10.1007/978-1-4614-6849-3 (last access: 1 February 2022), 2013.
Lacoste, M., Lemercier, B., and Walter, C.: Regional mapping of soil parent
material by machine learning based on point data, Geomorphology, 133,
90–99, https://doi.org/10.1016/j.geomorph.2011.06.026, 2011.
Lagacherie, P., Arrouays, D., Bourennane, H., Gomez, C., Martin, M., and
Saby, N. P. A.: How far can the uncertainty on a Digital Soil Map be known?:
A numerical experiment using pseudo values of clay content obtained from
Vis-SWIR hyperspectral imagery, Geoderma, 337, 1320–1328, 2019.
Leng, X., Qian, X., Yang, M., Wang, C., Li, H., and Wang, J.: Leaf magnetic
properties as a method for predicting heavy metal concentrations in PM 2.5
using support vector machine: A case study in Nanjing, China, Environ.
Pollut., 242, 922–930, https://doi.org/10.1016/j.envpol.2018.07.007, 2018.
Lesch, S. M., Rhoades, J. D., Lund, L. J., and Corwin, D. L.: Mapping soil
salinity using calibrated electromagnetic measurements, Soil Sci. Soc. Am.
J., 56, 540–548, 1992.
Levi, M. R. and Rasmussen, C.: Covariate selection with iterative principal
component analysis for predicting physical soil properties, Geoderma, 219,
46–57, 2014.
Li, H., Wang, J., Wang, Q., Tian, C., Qian, X., and Leng, X.: Magnetic
Properties as a Proxy for Predicting Fine-Particle-Bound Heavy Metals in a
Support Vector Machine Approach, Environ. Sci. Technol., 51, 6927–6935,
https://doi.org/10.1021/acs.est.7b00729, 2017.
Liao, K., Xu, S., Wu, J., Zhu, Q., and An, L.: Using support vector machines
to predict cation exchange capacity of different soil horizons in Qingdao
City, China, J. Plant Nutr. Soil Sci., 177, 775–782, 2014.
Ließ, M., Glaser, B., and Huwe, B.: Uncertainty in the spatial prediction
of soil texture: Comparison of regression tree and Random Forest models,
Geoderma, 170, 70–79, https://doi.org/10.1016/j.geoderma.2011.10.010,
2012.
Lim, C. H. and Jackson, M. L.: Dissolution for total elemental analysis – Methods of Soil Analysis: Part 2 Chemical and Microbiological Properties, 9, 1–12, https://doi.org/10.2134/agronmonogr9.2.2ed.c1, 1983.
Loiseau, T., Richer-de-forges, A. C., Martelet, G., Bialkowski, A., Nehlig,
P., and Arrouays, D.: Could airborne gamma-spectrometric
data replace lithological maps as co-variates for digital soil mapping of
topsoil particle-size distribution? A case study in Western France,
Geoderma Reg., 22, e00295, https://doi.org/10.1016/j.geodrs.2020.e00295, 2020.
Malone, B. P., McBratney, A. B., Minasny, B., and Laslett, G. M.: Mapping
continuous depth functions of soil carbon storage and available water
capacity, Geoderma, 154, 138–152, https://doi.org/10.1016/j.geoderma.2009.10.007,
2009.
McFadden, M. and Scott, W. R.: Broadband soil susceptibility measurements
for EMI applications, J. Appl. Geophys., 90, 119–125,
https://doi.org/10.1016/j.jappgeo.2013.01.009, 2013.
McNeill, J. D.: Geonics EM38 ground conductivity meter,
Geonics Ltd., Mississauga, Ontario, Canada, Tech. Note TN-21, 1986.
McNeill, J. D.: Rapid, accurate mapping of soil salinity by electromagnetic
ground conductivity meters, in: Advances in measurement of soil physical properties: Bringing theory into practice, 30, edited by: Clarke Topp, G., Daniel Reynolds, W. and Green, R. E., 209–229, https://doi.org/10.2136/sssaspecpub30.c11, 1992.
Mello, D., Demattê, J. A. M., Silvero, N. E. Q., Di Raimo, L. A. D. L.,
Poppiel, R. R., Mello, F. A. O., Souza, A. B., Safanelli, J. L., Resende, M.
E. B., and Rizzo, R.: Soil magnetic susceptibility and its relationship with
naturally occurring processes and soil attributes in pedosphere, in a
tropical environment, Geoderma, 372, 114364,
https://doi.org/10.1016/j.geoderma.2020.114364, 2020.
Mello, D., Demattê, J. A. M., Alcantara de Oliveira Mello, F.,
Poppiel, R. R., Quiñonez Silvero, N. E., Safanelli, J. L.,
Barros e Souza, A., Di Loreto Di Raimo, L. A., Rizzo, R., Bispo
Resende, M. E., and Reynaud Schaefer, C. E. G. R.: Applied
gamma-ray spectrometry for evaluating tropical soil processes and
attributes, Geoderma, 381, 114736, https://doi.org/10.1016/j.geoderma.2020.114736, 2021.
Minty, B. R. S.: A Review of Airborne Gamma-Ray Spectrometric
Data-Processing Techniques, Australian Government Publishing Service, https://doi.org/10.1071/EG14110, 1988.
Montanarella, L., Pennock, D. J., McKenzie, N., Badraoui, M., Chude, V., Baptista, I., Mamo, T., Yemefack, M., Singh Aulakh, M., Yagi, K., Young Hong, S., Vijarnsorn, P., Zhang, G.-L., Arrouays, D., Black, H., Krasilnikov, P., Sobocká, J., Alegre, J., Henriquez, C. R., de Lourdes Mendonça-Santos, M., Taboada, M., Espinosa-Victoria, D., AlShankiti, A., AlaviPanah, S. K., Elsheikh, E. A. E. M., Hempel, J., Camps Arbestain, M., Nachtergaele, F., and Vargas, R.: World's soils are under threat, SOIL, 2, 79–82, https://doi.org/10.5194/soil-2-79-2016, 2016.
Mullins, C. E.: Magnetic susceptibility of the soil and its significance in
soil science–a review, J. Soil Sci., 28, 223–246, 1977.
Nanni, M. R. and Demattê, J. A. M.: Spectral Reflectance Methodology in
Comparison to Traditional Soil Analysis, Soil Sci. Soc. Am. J., 70,
393–407, https://doi.org/10.2136/sssaj2003.0285, 2006.
Narjary, B., Meena, M. D., Kumar, S., Kamra, S. K., Sharma, D. K., and
Triantafilis, J.: Digital mapping of soil salinity at various depths using
an EM38, Soil Use Manag., 35, 232–244, https://doi.org/10.1111/sum.12468, 2019.
Nawar, S., Buddenbaum, H., Hill, J., Kozak, J., and Mouazen, A. M.:
Estimating the soil clay content and organic matter by means of different
calibration methods of vis-NIR diffuse reflectance spectroscopy, Soil
Till. Res., 155, 510–522, https://doi.org/10.1016/j.still.2015.07.021, 2016.
Neogi, S. and Dauwels, J.: Factored Latent-Dynamic Conditional Random Fields for single and multi-label sequence modeling, Pattern Recogn., 122, 108236, https://doi.org/10.1016/j.patcog.2021.108236, 2022.
O'Rourke, S. M., Stockmann, U., Holden, N. M., McBratney, A. B., and Minasny,
B.: An assessment of model averaging to improve predictive power of portable
vis-NIR and XRF for the determination of agronomic soil properties,
Geoderma, 279, 31–44, https://doi.org/10.1016/j.geoderma.2016.05.005, 2016.
Pansu, M. and Gautheyrou, J.: Handbook of Soil Analysis – Mineralogical,
Organic and Inorganic Methods, 1st edn., Springer, Netherlands, https://doi.org/10.1007/978-3-540-31211-6, 2006.
Perlich, C.: Learning Curves in Machine Learning, RC24756 (W0903-020), March 5, 2009, Computer Science, available at: https://dominoweb.draco.res.ibm.com/reports/rc24756.pdf (last access: 3 February 2022), 2010.
Pozza, L. E. and Field, D. J.: The science of soil Security and food
security, Soil Secur., 1, 100002, https://doi.org/10.1016/j.soisec.2020.100002, 2020.
Priori, S., Fantappiè, M., Bianconi, N., Ferrigno, G., Pellegrini, S.,
and Costantini, E. A. C.: Field-Scale Mapping of Soil Carbon Stock with
Limited Sampling by Coupling Gamma-Ray and Vis-NIR Spectroscopy, Soil Sci.
Soc. Am. J., 80, 954–964, https://doi.org/10.2136/sssaj2016.01.0018, 2016.
R Core Team: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, available at: http://r.meteo.uni.wroc.pl/web/packages/dplR/vignettes/intro-dplR.pdf (last access: 1 February 2022), 2015.
Radiation Solutions: Spectrum stabilization and calibration for the RSI RS-125 and
RS-230 handheld spectrometers, Appendix D, 57, available at: https://www.aseg.org.au/sites/default/files/RS-125%20RS-230_User_Manual%20%28GR%29.pdf
(last access: 3 February 2022), 2009.
Reinhardt, N. and Herrmann, L.: Gamma-ray spectrometry as versatile tool in
soil science: A critical review, J. Plant Nutr. Soil Sci., 182, 9–27,
https://doi.org/10.1002/jpln.201700447, 2019.
Rhoades, J. D., Chanduvi, F., and Lesch, S. M.: Soil salinity assessment:
Methods and interpretation of electrical conductivity measurements, Food and Agriculture Organization of the United Nations, ISBN 9251042810, 1999.
Richards, L. A.: Diagnosis and improvement of saline and alkali soils, 78, 154, LWW, 1954.
Rochette, P., Jackson, M., and Aubourg, C.: Rock magnetism and the
interpretation of magnetic susceptibility, Rev. Geophys., 30, 209–226,
1992.
Rytky, S. J. O., Tiulpin, A., Frondelius, T., Finnilä, M. A. J.,
Karhula, S. S., Leino, J., Pritzker, K. P. H., Valkealahti, M., Lehenkari,
P., Joukainen, A., Kröger, H., Nieminen, H. J., and Saarakkala, S.:
Automating three-dimensional osteoarthritis histopathological grading of
human osteochondral tissue using machine learning on contrast-enhanced
micro-computed tomography, Osteoarthr. Cartilage, 28, 1133–1144,
https://doi.org/10.1016/j.joca.2020.05.002, 2020.
Sales, Support and Costomisation: Terraplus KT-10 v2 User Manual – User's Guide ver. 2.1, available at: https://www.aseg.org.au/sites/default/files/KT-10%20User%20Manual%20%28GR%29.pdf
(last access: 3 February 2022) (last access: 2 February 2022), 2021.
Sarmast, M., Farpoor, M. H., and Esfandiarpour Boroujeni, I.: Magnetic
susceptibility of soils along a lithotoposequence in southeast Iran, Catena,
156, 252–262, https://doi.org/10.1016/j.catena.2017.04.019, 2017.
Schaetzl, J. R. and Anderson, S.: Soil Genesis and Geomorphology, 1st edn.
Cambridge University Press, New York, USA, ISBN 9780521812016, 2005.
Schuler, U., Erbe, P., Zarei, M., Rangubpit, W., Surinkum, A., Stahr, K., and
Herrmann, L.: A gamma-ray spectrometry approach to field separation of
illuviation-type WRB reference soil groups in northern Thailand, J. Plant
Nutr. Soil Sci., 174, 536–544, https://doi.org/10.1002/jpln.200800323, 2011.
Schwertmann, U. and Taylor, R. M.: Iron oxides, in: Minerals in Soil Environments, 1st edn., 379–438, ISBN 9780891187875, 1989.
Shenggao, L.: Lithological factors affecting magnetic susceptibility of
subtropical soils, Zhejiang Province, China, Catena, 40, 359–373,
https://doi.org/10.1016/S0341-8162(00)00092-8, 2000.
Silva, E. B., Giasson, É., Dotto, A. C., Caten, A. T., Demattê, J. A. M., Bacic, I. L. Z., and Veiga, M. D.: A Regional Legacy Soil Dataset for Prediction of Sand and Clay
Content with Vis-Nir-Swir in Southern Brazil, Rev. Bras. Cienc. Solo, 43, 1–20, 2019.
Silvero, N. E. Q., Di Raimo, L. A. D. L., Pereira, G. S., de Magalhães, L. P., da Terra, F. S., Dassan, M. A. A., Salazar, D. F. U., and Demattê,
J. A. M.: Effects of water, organic matter, and iron forms in mid-IR spectra
of soils: Assessments from laboratory to satellite-simulated data, Geoderma,
375, 114480, https://doi.org/10.1016/j.geoderma.2020.114480, 2020.
Siqueira, D. S., Marques, J., Matias, S. S. R., Barrón, V., Torrent, J.,
Baffa, O., and Oliveira, L. C.: Correlation of properties of Brazilian
Haplustalfs with magnetic susceptibility measurements, Soil Use Manage.,
26, 425–431, https://doi.org/10.1111/j.1475-2743.2010.00294.x, 2010.
Taylor, M. J., Smettem, K., Pracilio, G., and Verboom, W.: Relationships
between soil properties and high-resolution radiometrics, central eastern
Wheatbelt, Western Australia, Explor. Geophys., 33, 95–102, https://doi.org/10.1071/EG02095, 2018.
Teixeira, P. C., Donagemma, G. K., Fontana, A., and Teixeira, W. G.: Manual
de métodos de análise de solo, Embrapa, Rio de Janeiro, Brazil, 573 pp., ISBN 9788570357717, 2017.
Terra, F. S., Demattê, J. A. M., and Viscarra Rossel, R. A.: Proximal
spectral sensing in pedological assessments: vis–NIR spectra for soil
classification based on weathering and pedogenesis, Geoderma, 318,
123–136, https://doi.org/10.1016/j.geoderma.2017.10.053, 2018.
Triantafilis, J., Lesch, S. M., La Lau, K., and Buchanan, S. M.: Field level
digital soil mapping of cation exchange capacity using electromagnetic
induction and a hierarchical spatial regression model, Aust. J. Soil Res.,
47, 651–663, https://doi.org/10.1071/SR08240, 2009.
Valaee, M., Ayoubi, S., Khormali, F., Lu, S. G., and Karimzadeh, H. R.: Using
magnetic susceptibility to discriminate between soil moisture regimes in
selected loess and loess-like soils in northern Iran, J. Appl. Geophys.,
127, 23–30, https://doi.org/10.1016/j.jappgeo.2016.02.006, 2016.
Vašát, R., Kode, R., Klement, A., and Brodský, L.: Combining reflectance spectroscopy and the digital elevation model for soil oxidizable
carbon estimation, Geoderma, 303, 133–142, https://doi.org/10.1016/j.geoderma.2017.05.018,
2017.
Viana, J. H. M., Couceiro, P. R. C., Pereira, M. C., Fabris, J. D.,
Fernandes Filho, E. I., Schaefer, C., Rechenberg, H. R., Abrahão, W. A.
P., and Mantovani, E. C.: Occurrence of magnetite in the sand fraction of an
Oxisol in the Brazilian savanna ecosystem, developed from a magnetite-free
lithology, Soil Res., 44, 71–83, 2006.
Veloso, G. V., de Mello, D. C., Guedes Lana, M., Alcantara de Oliveira Mello, F., Poppiel, R. R., Ribeiro Oquendo Cabrero, D., Di Raimo, L. A., Gonçalves Reynaud Schaefer, C. E., Fernandes-Filho, E. I., Pereira Leite, E., and Melo Demattê, J. A.: Data and script for “A new methodological framework for geophysical sensors combinations associated with machine learning algorithms to understand soil attributes” (v.1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.5733366, 2021.
Viscarra Rossel, R. A., Webster, R., and Kidd, D.: Mapping gamma radiation
and its uncertainty from weathering products in a Tasmanian landscape with a
proximal sensor and random forest kriging, Earth Surf. Proc. Landforms,
39, 735–748, https://doi.org/10.1002/esp.3476, 2014.
Wilford, J. and Minty, B.: Chapter 16 The Use of Airborne Gamma-ray Imagery
for Mapping Soils and Understanding Landscape Processes, Dev. Soil Sci.,
31, 207–610, https://doi.org/10.1016/S0166-2481(06)31016-1, 2006.
Wilford, J. and Thomas, M.: Modelling soil-regolith thickness in complex
weathered landscapes of the central Mt Lofty Ranges, South Australia, ISBN 9780415621557, 2012.
Wilford, J. R., Bierwirth, P. E., and Craig, M. A.: Application of airborne gamma-ray spectrometry in soil/regolith mapping and applied geomorphology, AGSO J. Aust. Geol. Geophys., 17, 201–216, 1997.
Wong, M. T. F. and Harper, R. J.: Use of on-ground gamma-ray spectrometry to
measure plant-available potassium and other topsoil attributes, Aust. J.
Soil Res., 37, 267–277, https://doi.org/10.1071/S98038, 1999.
Xu, D., Zhao, R., Li, S., Chen, S., Jiang, Q., Zhou, L., and Shi, Z.:
Multi-sensor fusion for the determination of several soil properties in the
Yangtze River Delta, China, Eur. J. Soil Sci., 70, 162–173, 2019.
Zare, E., Li, N., Khongnawang, T., Farzamian, M., and Triantafilis, J.: Identifying potential leakage zones in an irrigation supply channel by mapping soil properties using electromagnetic induction, inversion modelling and a support vector machine, Soil Systems, 4, 25, https://doi.org/10.3390/soilsystems4020025, 2020.
Zhang, Y. and Hartemink, A. E.: Data fusion of vis – NIR and PXRF spectra
to predict soil physical and chemical properties, Eur. J. Soil Sci., 71, 316–333, https://doi.org/10.1111/ejss.12875, 2020.
Short summary
We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
We used soil parent material, terrain attributes, and geophysical data from the soil surface to...
