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1. SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction

Author

Kakhani, Nafiseh, Rangzan, Moien, Jamali, Ali, Attarchi, Sara, Alavipanah, Seyed Kazem, Mommert, Michael, Tziolas, Nikolaos, and Scholten, Thomas

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Soil Organic Carbon (SOC) constitutes a fundamental component of terrestrial ecosystem functionality, playing a pivotal role in nutrient cycling, hydrological balance, and erosion mitigation. Precise mapping of SOC distribution is imperative for the quantification of ecosystem services, notably carbon sequestration and soil fertility enhancement. Digital soil mapping (DSM) leverages statistical models and advanced technologies, including machine learning (ML), to accurately map soil properties, such as SOC, utilizing diverse data sources like satellite imagery, topography, remote sensing indices, and climate series. Within the domain of ML, self-supervised learning (SSL), which exploits unlabeled data, has gained prominence in recent years. This study introduces a novel approach that aims to learn the geographical link between multimodal features via self-supervised contrastive learning, employing pretrained Vision Transformers (ViT) for image inputs and Transformers for climate data, before fine-tuning the model with ground reference samples. The proposed approach has undergone rigorous testing on two distinct large-scale datasets, with results indicating its superiority over traditional supervised learning models, which depends solely on labeled data. Furthermore, through the utilization of various evaluation metrics (e.g., RMSE, MAE, CCC, etc.), the proposed model exhibits higher accuracy when compared to other conventional ML algorithms like random forest and gradient boosting. This model is a robust tool for predicting SOC and contributes to the advancement of DSM techniques, thereby facilitating land management and decision-making processes based on accurate information., Comment: Accepted for publication in IEEE Transactions on Geoscience and Remote Sensing (TGRS)

Published
2023

2. Inductive biases in deep learning models for weather prediction

Author

Thuemmel, Jannik, Karlbauer, Matthias, Otte, Sebastian, Zarfl, Christiane, Martius, Georg, Ludwig, Nicole, Scholten, Thomas, Friedrich, Ulrich, Wulfmeyer, Volker, Goswami, Bedartha, and Butz, Martin V.

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract

Deep learning has gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the last few years, achieving forecast skills comparable to established numerical weather prediction models with comparatively lesser computational costs. In order to train accurate, reliable, and tractable DLWP models with several millions of parameters, the model design needs to incorporate suitable inductive biases that encode structural assumptions about the data and the modelled processes. When chosen appropriately, these biases enable faster learning and better generalisation to unseen data. Although inductive biases play a crucial role in successful DLWP models, they are often not stated explicitly and their contribution to model performance remains unclear. Here, we review and analyse the inductive biases of state-of-the-art DLWP models with respect to five key design elements: data selection, learning objective, loss function, architecture, and optimisation method. We identify the most important inductive biases and highlight potential avenues towards more efficient and probabilistic DLWP models.

Published
2023

4. Contributors

Author

Abdollahi, Khodayar, primary, Abolhasani, Azam, additional, Ahmadisharaf, Amin, additional, Aliramaee, Ramyar, additional, Alizadeh, Fereshteh, additional, Amirian-Chakan, Alireza, additional, Asgari, Shamsollah, additional, Bateni, Sayed M., additional, Cerdà, Artemi, additional, Chen, Rui, additional, Choubin, Bahram, additional, Emami, Sayed Naeim, additional, Escriva-Saneugenio, Francisco, additional, Eskandari Damaneh, Hadi, additional, Eskandari Damaneh, Hamed, additional, Ferreira, Carla S.S., additional, Galehban, Eslam, additional, Gomez, Christopher, additional, Hamzehpour, Nikoo, additional, Hazbavi, Zeinab, additional, He, Binbin, additional, Heddam, Salim, additional, Heydari Alamdarloo, Esmaeil, additional, Iranmanesh, Yaghoub, additional, Jaafari, Abolfazl, additional, Javidan, Raana, additional, Kalantari, Zahra, additional, Kapović Solomun, Marijana, additional, Kašanin-Grubin, Milica, additional, Keesstra, Saskia D., additional, Khosravi, Hassan, additional, Khosravi, Khabat, additional, Kornejady, Aiding, additional, Lee, Saro, additional, Liao, Zhanmang, additional, Liu, Xiangzhuo, additional, Mafi-Gholami, Davood, additional, Maleki, Sedigheh, additional, Manesh, Maliheh Behrang, additional, Melesse, Assefa, additional, Mirzaee, Somayeh, additional, Mohamadzadeh, Neda, additional, Mohammadi, Farnoush, additional, Mosavi, Amir, additional, Naderi, Nastaran, additional, Naghibi, Amir, additional, Nekoeimehr, Mohammad, additional, Pajouhesh, Mehdi, additional, Panahi, Mahdi, additional, Pasandi, Mehrdad, additional, Quan, Xingwen, additional, Rahmati, Omid, additional, Rezaie, Fatemeh, additional, Scholten, Thomas, additional, Shirani, Kourosh, additional, Shirazi, Minoo Haghani, additional, Singh, Shailesh K., additional, Soleimani, Freidoon, additional, Soleimanpour, Seyed Masoud, additional, Syaripudin Nur, Arip, additional, Taghizadeh-Mehrjardi, Ruhollah, additional, Valjerević, Aleksandar, additional, Veysi, Shadman, additional, Yousefi, Saleh, additional, Yue, Chao, additional, and Zeraatpisheh, Mojtaba, additional

Published
2024
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12. Latent State Inference in a Spatiotemporal Generative Model

Author

Karlbauer, Matthias, Menge, Tobias, Otte, Sebastian, Lensch, Hendrik P. A., Scholten, Thomas, Wulfmeyer, Volker, and Butz, Martin V.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Knowledge about the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed interventions. Inferring these factors from time series data without supervision remains an open challenge. Here, we focus on spatiotemporal processes, including wave propagation and weather dynamics, for which we assume that universal causes (e.g. physics) apply throughout space and time. A recently introduced DIstributed SpatioTemporal graph Artificial Neural network Architecture (DISTANA) is used and enhanced to learn such processes, requiring fewer parameters and achieving significantly more accurate predictions compared to temporal convolutional neural networks and other related approaches. We show that DISTANA, when combined with a retrospective latent state inference principle called active tuning, can reliably derive location-respective hidden causal factors. In a current weather prediction benchmark, DISTANA infers our planet's land-sea mask solely by observing temperature dynamics and, meanwhile, uses the self inferred information to improve its own future temperature predictions., Comment: As submitted to and accepted by the 30th International Conference on Artificial Neural Networks (ICANN)

Published
2020

13. Inferring, Predicting, and Denoising Causal Wave Dynamics

Author

Karlbauer, Matthias, Otte, Sebastian, Lensch, Hendrik P. A., Scholten, Thomas, Wulfmeyer, Volker, and Butz, Martin V.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. It implements a grid or mesh of locally parameterizable laterally connected network modules. DISTANA is specifically designed to identify the causality behind spatially distributed, non-linear dynamical processes. We show that DISTANA is very well-suited to denoise data streams, given that re-occurring patterns are observed, significantly outperforming alternative approaches, such as temporal convolution networks and ConvLSTMs, on a complex spatial wave propagation benchmark. It produces stable and accurate closed-loop predictions even over hundreds of time steps. Moreover, it is able to effectively filter noise -- an ability that can be improved further by applying denoising autoencoder principles or by actively tuning latent neural state activities retrospectively. Results confirm that DISTANA is ready to model real-world spatio-temporal dynamics such as brain imaging, supply networks, water flow, or soil and weather data patterns., Comment: As accepted by the 29th International Conference on Artificial Neural Networks (ICANN20)

Published
2020

14. Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Author

Emadi, Mostafa, Taghizadeh-Mehrjardi, Ruhollah, Cherati, Ali, Danesh, Majid, Mosavi, Amir, and Scholten, Thomas

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning, 68T05
Abstract

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty., Comment: 30pages, 9 figures

Published
2020
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15. A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction

Author

Karlbauer, Matthias, Otte, Sebastian, Lensch, Hendrik P. A., Scholten, Thomas, Wulfmeyer, Volker, and Butz, Martin V.

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

We introduce a distributed spatio-temporal artificial neural network architecture (DISTANA). It encodes mesh nodes using recurrent, neural prediction kernels (PKs), while neural transition kernels (TKs) transfer information between neighboring PKs, together modeling and predicting spatio-temporal time series dynamics. As a consequence, DISTANA assumes that generally applicable causes, which may be locally modified, generate the observed data. DISTANA learns in a parallel, spatially distributed manner, scales to large problem spaces, is capable of approximating complex dynamics, and is particularly robust to overfitting when compared to other competitive ANN models. Moreover, it is applicable to heterogeneously structured meshes., Comment: 8 pages, 4 figures, video on https://www.youtube.com/watch?v=4VHhHYeWTzo

Published
2019

20. Böden und Bodenbildung

Author

Fritzsch, Dagmar, Kühn, Peter, Pietsch, Dana, Röpke, Astrid, Scholten, Thomas, Thiemeyer, Heinrich, Stolz, Christian, editor, and Miller, Christopher E., editor

Published
2022
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25. Carbon–biodiversity relationships in a highly diverse subtropical forest

Author

Schuldt, Andreas, Liu, Xiaojuan, Buscot, François, Bruelheide, Helge, Erfmeier, Alexandra, He, Jin‐Sheng, Klein, Alexandra‐Maria, Ma, Keping, Scherer‐Lorenzen, Michael, Schmid, Bernhard, Scholten, Thomas, Tang, Zhiyao, Trogisch, Stefan, Wirth, Christian, Wubet, Tesfaye, Staab, Michael, Schuldt, Andreas, Liu, Xiaojuan, Buscot, François, Bruelheide, Helge, Erfmeier, Alexandra, He, Jin‐Sheng, Klein, Alexandra‐Maria, Ma, Keping, Scherer‐Lorenzen, Michael, Schmid, Bernhard, Scholten, Thomas, Tang, Zhiyao, Trogisch, Stefan, Wirth, Christian, Wubet, Tesfaye, and Staab, Michael

Abstract

Carbon‐focused climate mitigation strategies are becoming increasingly important in forests. However, with ongoing biodiversity declines we require better knowledge of how much such strategies account for biodiversity. We particularly lack information across multiple trophic levels and on established forests, where the interplay between carbon stocks, stand age, and tree diversity might influence carbon–biodiversity relationships. Using a large dataset (>4600 heterotrophic species of 23 taxonomic groups) from secondary, subtropical forests, we tested how multitrophic diversity and diversity within trophic groups relate to aboveground, belowground, and total carbon stocks at different levels of tree species richness and stand age. Our study revealed that aboveground carbon, the key component of climate‐based management, was largely unrelated to multitrophic diversity. By contrast, total carbon stocks — that is, including belowground carbon — emerged as a significant predictor of multitrophic diversity. Relationships were nonlinear and strongest for lower trophic levels, but nonsignificant for higher trophic level diversity. Tree species richness and stand age moderated these relationships, suggesting long‐term regeneration of forests may be particularly effective in reconciling carbon and biodiversity targets. Our findings highlight that biodiversity benefits of climate‐oriented management need to be evaluated carefully, and only maximizing aboveground carbon may fail to account for biodiversity conservation requirements.

Published
2024

26. Microbial impact on initial soil formation in arid and semiarid environments under simulated climate change

Author

Rodríguez, Victoria, Bartholomäus, Alexander, Witzgall, Kristina, Riveras-Muñoz, Nicolás, Oses, Romulo, Liebner, Susanne, Kallmeyer, Jens, Rach, Oliver, Mueller, Carsten W., Seguel, Oscar, Scholten, Thomas, Wagner, Dirk, Rodríguez, Victoria, Bartholomäus, Alexander, Witzgall, Kristina, Riveras-Muñoz, Nicolás, Oses, Romulo, Liebner, Susanne, Kallmeyer, Jens, Rach, Oliver, Mueller, Carsten W., Seguel, Oscar, Scholten, Thomas, and Wagner, Dirk

Abstract

The microbiota is attributed to be important for initial soil formation under extreme climate conditions, but experimental evidence for its relevance is scarce. To fill this gap, we investigated the impact of in situ microbial communities and their interrelationship with biocrust and plants compared to abiotic controls on soil formation in initial arid and semiarid soils. Additionally, we assessed the response of bacterial communities to climate change. Topsoil and subsoil samples from arid and semiarid sites in the Chilean Coastal Cordillera were incubated for 16 weeks under diurnal temperature and moisture variations to simulate humid climate conditions as part of a climate change scenario. Our findings indicate that microorganism-plant interaction intensified aggregate formation and stabilized soil structure, facilitating initial soil formation. Interestingly, microorganisms alone or in conjunction with biocrust showed no discernible patterns compared to abiotic controls, potentially due to water-masking effects. Arid soils displayed reduced bacterial diversity and developed a new community structure dominated by Proteobacteria, Actinobacteriota, and Planctomycetota, while semiarid soils maintained a consistently dominant community of Acidobacteriota and Proteobacteria. This highlighted a sensitive and specialized bacterial community in arid soils, while semiarid soils exhibited a more complex and stable community. We conclude that microorganism-plant interaction has measurable impacts on initial soil formation in arid and semiarid regions on short time scales under climate change. Additionally, we propose that soil and climate legacies are decisive for the present soil microbial community structure and interactions, future soil development, and microbial responses.

Published
2024

27. Latent State Inference in a Spatiotemporal Generative Model

Author

Karlbauer, Matthias, Menge, Tobias, Otte, Sebastian, Lensch, Hendrik P. A., Scholten, Thomas, Wulfmeyer, Volker, Butz, Martin V., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Farkaš, Igor, editor, Masulli, Paolo, editor, Otte, Sebastian, editor, and Wermter, Stefan, editor

Published
2021
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28. The effect of mosses on the relocation of SOC and total N due to soil erosion and percolation in a disturbed temperate forest.

Author

Gall, Corinna, Nebel, Martin, Scholten, Thomas, and Seitz, Steffen

Subjects
DISSOLVED organic matter, SOIL erosion, LOGGING, SOIL percolation, NUTRIENT cycles, MOSSES
Abstract

Forests cover one-third of the global land and are important components of carbon and nitrogen cycling. Anthropogenic disturbances, such as forest road systems or skid trails for timber harvesting, can dramatically change the nutrient cycling in these ecosystems. Skid trails increase soil erosion and thus the displacement of soil organic carbon (SOC) and total nitrogen (Nt). Additionally, runoff transports high amounts of dissolved organic carbon (DOC), which can have a negative impact on aquatic ecosystems. One of the most important countermeasures against soil erosion is the quick recolonization of vegetation. To date, the extent to which natural vegetation succession influences the relocation of SOC and Nt, and in particular the role of mosses in this context, has not been well investigated. This study investigates the influence of natural vegetation succession and in particular of mosses on the displacement process of SOC and Nt as well as DOC caused by soil erosion. To this end, we combine the results of a field study using in-situ rainfall simulations with small-scale runoff plots in skid trails of the Schönbuch Nature Park in southwestern Germany with the results of ex-situ rainfall simulation experiments with infiltration boxes containing the substrate from the respective skid trails. The eroded sediments of skid trails were on average enriched in SOC by 16% and in Nt by 35% compared to the original soil, which lead to a decrease of the C/N ratio in sediments. As vegetation succession progressed, the displacement of SOC and Nt was reduced, confirmed by a negative correlation between the enrichment ratios of SOC (ERSOC), Nt and total vegetation cover. However, mosses tended to reduce ERSOC more than vascular plants. Additionally, mosses significantly decreased DOC concentration in surface runoff compared to bare soils, while no difference in DOC concentration in percolated water could be observed. Future research should explore the role of mosses in the storage of SOC and Nt in the soil and their impact on soil stability. Thus, utilizing mosses could potentially minimize environmental impacts from soil disturbances in forests. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Rainfall redistribution in subtropical Chinese forests changes over 22 years.

Author

Zhang, Wanjun, Scholten, Thomas, Seitz, Steffen, Zhang, Qianmei, Chu, Guowei, Wang, Linhua, Xiong, Xin, and Liu, Juxiu

Subjects
HYDROLOGIC cycle, FOREST succession, BROADLEAF forests, MIXED forests, RAINWATER, THROUGHFALL
Abstract

Rainfall redistribution through the vegetation canopy plays a key role in the hydrological cycle. Although there have been studies on the heterogeneous patterns of rainfall redistribution in some ecosystems, the understanding of this process in different stages of forest succession remains insufficient. Therefore, this study investigated the change tendency in rainfall redistribution and rainwater chemistry in a subtropical forest succession in South China, based on 22 years (2001–2022) of rainfall monitoring (740 valid events). Results showed that, at the event scale, both the throughfall ratio and the stemflow ratio in pine forest (PF) were higher than in mixed forest (MF) and broadleaf forest (BF). At the interannual scale, throughfall and stemflow of forests experienced an initial decrease followed by a subsequent increase over the entire measurement period (except stemflow of the pine forest), which reflects the trend in open rainfall. The variability in throughfall showed an increase from MF to PF to BF, and the variability in stemflow likewise showed an increase from MF to PF to BF. Changes in throughfall and stemflow in the BF are thus higher than those in the MF and PF over time. Furthermore, important differences in rainwater chemistry fluxes among the three forest types were found, changing in varying order over time. On average, total nitrogen (TN) and total phosphorus (TP) fluxes of throughfall increased from BF to MF to PF, while the potassium (K+) flux of throughfall showed a decrease from BF to MF to PF. Stemflow chemical fluxes varied less among forest types and over time, although tree species most importantly affected varying stemflow chemistry. These results show important changes in patterns of rainfall redistribution over time and that characteristic variations are driven by rainfall and forest factors. Therefore, this study provides insight into long-term rainfall redistribution processes by linking changes in rainfall spectra with a typical subtropical forest succession sequence. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Assessing the Role of Environmental Covariates and Pixel Size in Soil Property Prediction: A Comparative Study of Various Areas in Southwest Iran.

Author

Khosravani, Pegah, Baghernejad, Majid, Taghizadeh-Mehrjardi, Ruhollah, Mousavi, Seyed Roohollah, Moosavi, Ali Akbar, Fallah Shamsi, Seyed Rashid, Shokati, Hadi, Kebonye, Ndiye M., and Scholten, Thomas

Subjects
SHEAR strength of soils, DIGITAL soil mapping, DIGITAL elevation models, LAND use planning, SOIL mapping, SOIL classification
Abstract

(1) Background: The use of multiscale prediction or the optimal scaling of predictors can enhance soil maps by applying pixel size in digital soil mapping (DSM). (2) Methods: A total of 200, 50, and 129 surface soil samples (0–30 cm) were collected by the CLHS method in three different areas, namely, the Marvdasht, Bandamir, and Lapuee plains in southwest Iran. Then, four soil properties—soil organic matter (SOM), bulk density (BD), soil shear strength (SS), and mean weighted diameter (MWD)—were measured at each sampling point as representative attributes of soil physical and chemical quality. This study examined different-scale scenarios ranging from resampling the original 30 m digital elevation model and remote sensing indices to various pixel sizes, including 60 × 60, 90 × 90, 120 × 120, and up to 2100 × 2100 m. (3) Results: After evaluating 22 environmental covariates, 11 of them were identified as the most suitable candidates for predicting soil properties based on recursive feature elimination (RFE) and expert opinion methods. Furthermore, among different pixel size scenarios for SOM, BD, SS, and MWD, the highest accuracy was achieved at 1200 × 1200 m (R2 = 0.35), 180 × 180 m (R2 = 0.67), 1200 × 1200 m (R2 = 0.42), and 2100 × 2100 m (R2 = 0.34), respectively, in Marvdasht plain. (4) Conclusions: Adjusting the pixel size improves the capture of soil property variability, enhancing mapping precision and supporting effective decision making for crop management, irrigation, and land use planning. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils.

Author

Sakhaee, Ali, Scholten, Thomas, Taghizadeh-Mehrjardi, Ruhollah, Ließ, Mareike, and Don, Axel

Subjects
MACHINE learning, DIGITAL soil mapping, AGRICULTURE, SOIL classification, CARBON in soils
Abstract

Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial. Three ensemble machine learning models were trained to obtain spatial predictions of the C/N ratio, MAOM, and POM in German agricultural topsoil (0–10 cm). Parameter optimization and model evaluation were performed using nested cross-validation. Additionally, a modification to the regressor chain was applied to capture and interpret the interactions among the C/N ratio, MAOM, and POM. The ensemble models yielded mean absolute percent errors (MAPEs) of 8.2% for the C/N ratio, 14.8% for MAOM, and 28.6% for POM. Soil type, pedo-climatic region, hydrological unit, and soilscapes were found to explain 75% of the variance in MAOM and POM, and 50% in the C/N ratio. The modified regressor chain indicated a nonlinear relationship between the C/N ratio and SOM due to the different decomposition rates of SOM as a result of variety in its nutrient quality. These spatial predictions enhance the understanding of soil properties' distribution in Germany. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Assessment of Land Suitability Potential Using Ensemble Approaches of Advanced Multi-Criteria Decision Models and Machine Learning for Wheat Cultivation.

Author

Nabiollahi, Kamal, Kebonye, Ndiye M., Molani, Fereshteh, Tahari-Mehrjardi, Mohammad Hossein, Taghizadeh-Mehrjardi, Ruhollah, Shokati, Hadi, and Scholten, Thomas

Subjects
MACHINE learning, MULTIPLE criteria decision making, UNCERTAINTY (Information theory), TOPSIS method, GEOMORPHOLOGICAL mapping
Abstract

Land suitability assessment, as an important process in modern agriculture, involves the evaluation of numerous aspects such as soil properties, climate, relief, hydrology and socio-economic aspects. The aim of this study was to evaluate the suitability of soils for wheat cultivation in the Gavshan region, Iran, as the country is facing the task of becoming self-sufficient in wheat. Various methods were used to evaluate the land, such as multi-criteria decision-making (MCDM), which is proving to be important for land use planning. MCDM and machine learning (ML) are useful for decision-making processes because they use complicated spatial data and methods that are widely available. Using a geomorphological map, seventy soil profiles were selected and described, and ten soil properties and wheat yields were determined. Three MCDM approaches, including the technique of preference ordering by similarity to the ideal solution (TOPSIS), gray relational analysis (GRA), and simple additive weighting (SAW), were used and evaluated. The criteria weights were extracted using Shannon's entropy method. Random forest (RF) model and auxiliary variables (remote sensing data, terrain data, and geomorphological maps) were used to represent the land suitability values. Spatial autocorrelation analysis as a statistical method was applied to analyze the spatial variability of the spatial data. Slope, CEC (cation exchange capacity), and OC (organic carbon) were the most important factors for wheat cultivation. The spatial autocorrelation between the key criteria (slope, CEC, and OC) and wheat yield confirmed these results. These results also showed a significant correlation between the land suitability values of TOPSIS, GRA, and SAW and wheat yield (0.74, 0.72, and 0.57, respectively). The spatial distribution of land suitability values showed that the areas classified as good according to TOPSIS and GRA were larger than those classified as moderate and weak according to the SAW approach. These results were also confirmed by the autocorrelation of the MCDM techniques with wheat yield. In addition, the RF model showed its effectiveness in processing complex spatial data and improved the accuracy of land suitability assessment. In this study, by integrating advanced MCDM techniques and ML, an applicable land evaluation approach for wheat cultivation was proposed, which can improve the accuracy of land suitability and be useful for considering sustainability principles in land management. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Water’s path from moss to soil: A multi-methodological study on water absorption and evaporation of soil-moss combinations

Author

Thielen Sonja M., Gall Corinna, Ebner Martin, Nebel Martin, Scholten Thomas, and Seitz Steffen

Subjects
biological soil crusts, bryophytes, ecohydrology, moss structure, moss hydrology, rainfall interception, Hydraulic engineering, TC1-978
Abstract

Mosses are often overlooked; however, they are important for soil-atmosphere interfaces with regard to water exchange. This study investigated the influence of moss structural traits on maximum water storage capacities (WSCmax) and evaporation rates, and species-specific effects on water absorption and evaporation patterns in moss layers, moss-soil-interfaces and soil substrates using biocrust wetness probes. Five moss species typical for Central European temperate forests were selected: field-collected Brachythecium rutabulum, Eurhynchium striatum, Oxyrrhynchium hians and Plagiomnium undulatum; and laboratory-cultivated Amblystegium serpens and Oxyrrhynchium hians.

Published
2021
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35. List of contributors

Author

Adirosi, Elisa, primary, Alberoni, Pier Paolo, additional, Anagnostou, Emmanouil N., additional, Anagnostou, Marios, additional, Anagnostou, Marios N., additional, Battaglia, Alessandro, additional, Beauchamp, James, additional, Biscaro, Thiago Souza, additional, Bochenek, Bogdan, additional, Borg, Erik, additional, Borrmann, Stephan, additional, Bringi, V.N., additional, Brocca, Luca, additional, Camplani, Andrea, additional, Casella, Daniele, additional, Cauteruccio, Arianna, additional, Cecchini, Micael Amore, additional, Cecil, Daniel, additional, Chen, Ying-Wen, additional, D’Adderio, Leo Pio, additional, Diehl, Karoline, additional, Dietrich, Peter, additional, Dietrich, Stefano, additional, Federico, Stefano, additional, Gastaldo, Thomas, additional, Gatlin, Patrick N., additional, Ikuta, Yasutaka, additional, Jurczyk, Anna, additional, Kalnay, Eugenia, additional, Kalogiros, John, additional, Kanemaru, Kaya, additional, Katsafados, Petros, additional, Katsanos, Dimitrios, additional, Kidd, Chris, additional, Klepp, Christian, additional, Kondo, Keiichi, additional, Kotsuki, Shunji, additional, Kucera, Paul A., additional, Lanza, Luca G., additional, Lien, Guo-Yuan, additional, Linkowska, Joanna, additional, Lombardo, Federico, additional, Maggioni, Viviana, additional, Manzato, Agostino, additional, Massari, Christian, additional, Matsui, Toshihisa, additional, Mattos, Enrique Vieira, additional, Mazzoglio, Paola, additional, Mentzafou, Angeliki, additional, Merino, Andrés, additional, Michaelides, Silas, additional, Mitra, Subir Kumar, additional, Miyoshi, Takemasa, additional, Morsy, Mona, additional, Mroz, Kamil, additional, Navarro, Andrés, additional, Nystuen, Jeffrey A., additional, Okamoto, Kozo, additional, Oliveira, Rômulo Augusto Jucá, additional, Otop, Irena, additional, Otsuka, Shigenori, additional, Ośródka, Katarzyna, additional, Paccagnella, Tiziana, additional, Panegrossi, Giulia, additional, Papadopoulos, Anastasios, additional, Pappa, Aikaterini, additional, Pasierb, Magdalena, additional, Poli, Virginia, additional, Porcù, Federico, additional, Rahman, Khalil Ur, additional, Retalis, Adrianos, additional, Ringerud, Sarah, additional, Sanò, Paolo, additional, Sapiano, Mathew Raymond Paul, additional, Satoh, Masaki, additional, Scholten, Thomas, additional, Shang, Songhao, additional, Sharifi, Ehsan, additional, Sherief, Youssef, additional, Spyrou, Christos, additional, Szakáll, Miklós, additional, Szturc, Jan, additional, Terasaki, Koji, additional, Theis, Alexander, additional, Thurai, Merhala, additional, Tokay, Ali, additional, Tomita, Hirofumi, additional, Torcasio, Rosa Claudia, additional, Tymvios, Filippos, additional, Varlas, George, additional, Vila, Daniel Alejandro, additional, Vulpiani, Gianfranco, additional, Wang, Nai-Yu, additional, Yano, Jun-Ichi, additional, and Yashiro, Hisashi, additional

Published
2022
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37. Microbial impact on initial soil formation in arid and semiarid environments under simulated climate change

Author

Rodríguez, Victoria, primary, Bartholomäus, Alexander, additional, Witzgall, Kristina, additional, Riveras-Muñoz, Nicolás, additional, Oses, Romulo, additional, Liebner, Susanne, additional, Kallmeyer, Jens, additional, Rach, Oliver, additional, Mueller, Carsten W., additional, Seguel, Oscar, additional, Scholten, Thomas, additional, and Wagner, Dirk, additional

Published
2024
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45. Random Forest-Based Soil Moisture Estimation Using Sentinel-2, Landsat-8/9, and UAV-Based Hyperspectral Data.

Author

Shokati, Hadi, Mashal, Mahmoud, Noroozi, Aliakbar, Abkar, Ali Akbar, Mirzaei, Saham, Mohammadi-Doqozloo, Zahra, Taghizadeh-Mehrjardi, Ruhollah, Khosravani, Pegah, Nabiollahi, Kamal, and Scholten, Thomas

Subjects
SOIL moisture, CLIMATE change adaptation, RANDOM forest algorithms, DRONE aircraft, NOISE control, THEMATIC mapper satellite
Abstract

Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from the CoSpectroCam sensor (CSC, licensed to AgriWatch BV, Enschede, The Netherlands) mounted on an unmanned aerial vehicle (UAV) in Iran. The model included nine bands from Landsat-8/9, 11 bands from Sentinel-2, and 1252 bands from the CSC (covering the wavelength range between 420 and 850 nm). The relative feature importance and band sensitivity to SM variations were analyzed. In addition, four indices, including the perpendicular index (PI), ratio index (RI), difference index (DI), and normalized difference index (NDI) were calculated from the different bands of the datasets, and their sensitivity to SM was evaluated. The results showed that the PI exhibited the highest sensitivity to SM changes in all datasets among the four indices considered. Comparisons of the performance of the datasets in SM estimation emphasized the superior performance of the UAV hyperspectral data (R2 = 0.87), while the Sentinel-2 and Landsat-8/9 data showed lower accuracy (R2 = 0.49 and 0.66, respectively). The robust performance of the CSC data is likely due to its superior spatial and spectral resolution as well as the application of preprocessing techniques such as noise reduction and smoothing filters. The lower accuracy of the multispectral data from Sentinel-2 and Landsat-8/9 can also be attributed to their relatively coarse spatial resolution compared to the CSC, which leads to pixel non-uniformities and impurities. Therefore, employing the CSC on a UAV proves to be a valuable technology, providing an effective link between satellite observations and ground measurements. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Impacts of species richness on productivity in a large-scale subtropical forest experiment

Author

Huang, Yuanyuan, Chen, Yuxin, Castro-Izaguirre, Nadia, Baruffol, Martin, Brezzi, Matteo, Lang, Anne, Li, Ying, Härdtle, Werner, von Oheimb, Goddert, Yang, Xuefei, Liu, Xiaojuan, Pei, Kequan, Both, Sabine, Yang, Bo, Eichenberg, David, Assmann, Thorsten, Bauhus, Jürgen, Behrens, Thorsten, Buscot, François, Chen, Xiao-Yong, Chesters, Douglas, Ding, Bing-Yang, Durka, Walter, Erfmeier, Alexandra, Fang, Jingyun, Fischer, Markus, Guo, Liang-Dong, Guo, Dali, Gutknecht, Jessica L. M., He, Jin-Sheng, He, Chun-Ling, Hector, Andy, Hönig, Lydia, Hu, Ren-Yong, Klein, Alexandra-Maria, Kühn, Peter, Liang, Yu, Li, Shan, Michalski, Stefan, Scherer-Lorenzen, Michael, Schmidt, Karsten, Scholten, Thomas, Schuldt, Andreas, Shi, Xuezheng, Tan, Man-Zhi, Tang, Zhiyao, Trogisch, Stefan, Wang, Zhengwen, Welk, Erik, Wirth, Christian, Wubet, Tesfaye, Xiang, Wenhua, Yu, Mingjian, Yu, Xiao-Dong, Zhang, Jiayong, Zhang, Shouren, Zhang, Naili, Zhou, Hong-Zhang, Zhu, Chao-Dong, Zhu, Li, Bruelheide, Helge, Ma, Keping, Niklaus, Pascal A., and Schmid, Bernhard

Published
2018

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