97 citations as recorded by crossref.

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3. Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation J. Lu et al. https://doi.org/10.3390/rs13173502
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25. High-Resolution Digital Soil Maps of Forest Soil Nitrogen across South Korea Using Three Machine Learning Algorithms Y. An et al. https://doi.org/10.3390/f14061141
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43. High resolution middle eastern soil attributes mapping via open data and cloud computing R. Poppiel et al. https://doi.org/10.1016/j.geoderma.2020.114890
44. Importance ranking of data and model uncertainties in quantile regression forest-based spatial predictions when data are sparse, imprecise and clustered J. Rohmer https://doi.org/10.1016/j.ecoinf.2025.103459
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46. Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning G. Feng et al. https://doi.org/10.1155/2022/3287117
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49. Soil texture prediction with automated deep convolutional neural networks and population-based learning O. Omondiagbe et al. https://doi.org/10.1016/j.geoderma.2023.116521
50. Evaluating the quality of soil legacy data used as input of digital soil mapping models P. Lagacherie et al. https://doi.org/10.1111/ejss.13463
51. A Study on Digital Soil Mapping Based on Multi-Attention Convolutional Neural Networks: A Case Study in Heilongjiang Province Y. Liu et al. https://doi.org/10.3390/agriculture15212273
52. Evaluation of projected soil organic carbon stocks under future climate and land cover changes in South Africa using a deep learning approach O. Odebiri et al. https://doi.org/10.1016/j.jenvman.2022.117127
53. A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information J. Wang et al. https://doi.org/10.1016/j.scitotenv.2023.166112
54. Proximal and remote sensor data fusion for 3D imaging of infertile and acidic soil J. Wang et al. https://doi.org/10.1016/j.geoderma.2022.115972
55. Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping S. Chen et al. https://doi.org/10.1016/j.geoderma.2023.116467
56. Mapping soil organic carbon distribution across South Africa's major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks O. Odebiri et al. https://doi.org/10.1016/j.scitotenv.2022.161150
57. Evaluation of digital soil mapping projection in soil organic carbon change modeling T. Zhang et al. https://doi.org/10.1016/j.ecoinf.2023.102394
58. A generative deep neural network as an alternative to co-kriging H. Rakotonirina et al. https://doi.org/10.1016/j.acags.2024.100198
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61. Multi-Modal Vision Transformer for high-resolution soil texture prediction of German agricultural soils using remote sensing imagery L. Wittstruck et al. https://doi.org/10.1016/j.rse.2025.114985
62. Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al. https://doi.org/10.1016/j.catena.2021.105299
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66. Soil parent material prediction through satellite multispectral analysis on a regional scale at the Western Paulista Plateau, Brazil F. Mello et al. https://doi.org/10.1016/j.geodrs.2021.e00412
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