151 citations as recorded by crossref.

1. Three-Dimensional Mapping of Forest Soil Carbon Stocks Using SCORPAN Modelling and Relative Depth Gradients in the North-Eastern Lowlands of Germany A. Russ et al. 10.3390/app11020714
2. Integrating CEDGAN and FCNN for Enhanced Evaluation and Prediction of Plant Growth Environments in Urban Green Spaces Y. Wang et al. 10.3390/agronomy14050938
3. Monitoring regional soil organic matter content using a spatiotemporal model with time-series synthetic Landsat images M. Zhang et al. 10.1016/j.geodrs.2023.e00702
4. Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks X. Ge et al. 10.1016/j.jag.2022.102969
5. Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors Y. Yan et al. 10.3390/rs14133020
6. A CNN-LSTM Model for Soil Organic Carbon Content Prediction with Long Time Series of MODIS-Based Phenological Variables L. Zhang et al. 10.3390/rs14184441
7. Application of generalized linear geostatistical model for regional soil organic matter mapping: The effect of sampling density M. Zhang et al. 10.1016/j.geoderma.2023.116446
8. Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review P. Agyeman et al. 10.1007/s10653-020-00742-9
9. Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes P. Descombes et al. 10.1111/ecog.05117
10. A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information J. Wang et al. 10.1016/j.scitotenv.2023.166112
11. Using deep learning for multivariate mapping of soil with quantified uncertainty A. Wadoux 10.1016/j.geoderma.2019.05.012
12. Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging Z. Zhu et al. 10.1016/j.envpol.2023.122548
13. A modified radial basis function network integrating Mahalanobis distance and LASSO for soil mapping C. Wang et al. 10.1016/j.ecoinf.2023.102279
14. Digital Soil Mapping of Soil Organic Matter with Deep Learning Algorithms P. Zeng et al. 10.3390/ijgi11050299
15. Automated near-real-time mapping and monitoring of rice growth extent and stages in Selangor Malaysia . Fatchurrachman et al. 10.1016/j.rsase.2023.100993
16. Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran M. Emadi et al. 10.3390/rs12142234
17. Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm Y. Ellili-Bargaoui et al. 10.5194/soil-6-371-2020
18. Using a fuzzy logic approach to reveal soil-landscape relationships produced by digital soil maps in the humid tropical region of East Java, Indonesia D. Cahyana et al. 10.1016/j.geodrs.2021.e00468
19. Machine Learning With GA Optimization to Model the Agricultural Soil-Landscape of Germany: An Approach Involving Soil Functional Types With Their Multivariate Parameter Distributions Along the Depth Profile M. Ließ et al. 10.3389/fenvs.2021.692959
20. Digital mapping of the global soil δ15N at 0.1° × 0.1° resolution using random forest regression with climate classification Q. Zan et al. 10.1016/j.ecolind.2023.110974
21. Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran S. Mousavi et al. 10.1016/j.measurement.2022.111706
22. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison X. Zhu et al. 10.3390/su12052099
23. High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra J. Wagner et al. 10.1016/j.geoderma.2023.116652
24. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review S. Chen et al. 10.1016/j.geoderma.2021.115567
25. Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information C. Kirkwood et al. 10.1007/s11004-021-09988-0
26. Estimation of soluble solids content and fruit temperature in 'Rocha' pear using Vis-NIR spectroscopy and the SpectraNet–32 deep learning architecture J. Martins et al. 10.1016/j.postharvbio.2023.112281
27. Semi-automatic extraction of land degradation processes using multi sensor data by applying object based classification technique S. Raghubanshi et al. 10.1007/s12518-023-00503-0
28. Soil inorganic carbon distribution, stocks and environmental thresholds along a major climatic gradient M. Pfeiffer et al. 10.1016/j.geoderma.2023.116449
29. Improving digital soil mapping in Bogor, Indonesia using parent material information D. Cahyana et al. 10.1016/j.geodrs.2023.e00627
30. The Impact of COVID-19 Lockdown on People’s Mobility and Water Quality of Siak River, Indonesia S. Hasibuan et al. 10.2965/jwet.21-178
31. The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement M. Rybansky & J. Rada 10.3390/app12126178
32. Mapping Topsoil Total Nitrogen Using Random Forest and Modified Regression Kriging in Agricultural Areas of Central China L. Zhang et al. 10.3390/plants12071464
33. Digital mapping of potentially toxic elements enrichment in soils of Urmia Lake due to water level decline Z. Alvyar et al. 10.1016/j.scitotenv.2021.152086
34. Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France A. RICHER-de-FORGES et al. 10.1016/j.pedsph.2022.07.009
35. Estimation of soil properties from the EU spectral library using long short-term memory networks S. Singh & S. Kasana 10.1016/j.geodrs.2019.e00233
36. Multi-source data integration for soil mapping using deep learning A. Wadoux et al. 10.5194/soil-5-107-2019
37. Game theory interpretation of digital soil mapping convolutional neural networks J. Padarian et al. 10.5194/soil-6-389-2020
38. A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics W. Mendes et al. 10.1016/j.envpol.2021.118397
39. Semi-supervised learning for the spatial extrapolation of soil information R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2022.116094
40. Assessing human impacts on soil organic carbon change in the Lower Namoi Valley, Australia H. Jang et al. 10.1016/j.ancene.2023.100393
41. Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data D. Rukhovich et al. 10.3390/rs14092224
42. Free iron oxide content in tropical soils predicted by integrative digital mapping W. Mendes et al. 10.1016/j.still.2022.105346
43. Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review S. Lamichhane et al. 10.1016/j.geoderma.2019.05.031
44. Evaluation of Open Geotechnical Knowledge in Urban Environments for 3D Modelling of the City of Seville (Spain) C. Soriano-Cuesta et al. 10.3390/rs16010141
45. Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space R. Taghizadeh-Mehrjardi et al. 10.3390/rs12071095
46. Density of soil observations in digital soil mapping: A study in the Mayenne region, France T. Loiseau et al. 10.1016/j.geodrs.2021.e00358
47. Digital soil mapping and assessment for Australia and beyond: A propitious future R. Searle et al. 10.1016/j.geodrs.2021.e00359
48. Geospatial prediction of total soil carbon in European agricultural land based on deep learning D. Radočaj et al. 10.1016/j.scitotenv.2023.169647
49. A reliable Bayesian neural network for the prediction of reservoir thickness with quantified uncertainty L. Bao et al. 10.1016/j.cageo.2023.105409
50. Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions S. Mousavi et al. 10.1016/j.catena.2023.107392
51. Towards near real-time national-scale soil water content monitoring using data fusion as a downscaling alternative I. Fuentes et al. 10.1016/j.jhydrol.2022.127705
52. Self-supervised learning of Vision Transformers for digital soil mapping using visual data P. Tresson et al. 10.1016/j.geoderma.2024.117056
53. A spatial assessment of mercury content in the European Union topsoil C. Ballabio et al. 10.1016/j.scitotenv.2020.144755
54. Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization R. Agarwal et al. 10.1109/ACCESS.2021.3135536
55. Variability of soil mapping accuracy with sample sizes, modelling methods and landform types in a regional case study X. Sun et al. 10.1016/j.catena.2022.106217
56. Review on Crop Prediction Using Deep Learning Techniques M. Dharani et al. 10.1088/1742-6596/1767/1/012026
57. Geomorphometry today I. Florinsky 10.35595/2414-9179-2021-2-27-394-448
58. Soil carbon sequestration potential in global croplands J. Padarian et al. 10.7717/peerj.13740
59. Geotechnologies on the phosphorus stocks determination in tropical soils: General impacts on society J. Rosas et al. 10.1016/j.scitotenv.2024.173537
60. Simultaneously mapping the 3D distributions of multiple heavy metals in an industrial site using deep learning and multisource auxiliary data Y. Peng et al. 10.1016/j.jhazmat.2024.136000
61. Spatiotemporal modelling of soil organic matter changes in Jiangsu, China between 1980 and 2006 using INLA-SPDE X. Sun et al. 10.1016/j.geoderma.2020.114808
62. Uncovering the effects of Urmia Lake desiccation on soil chemical ripening using advanced mapping techniques F. Shahbazi et al. 10.1016/j.catena.2023.107440
63. Mapping soil profile depth, bulk density and carbon stock in Scotland using remote sensing and spatial covariates M. Aitkenhead & M. Coull 10.1111/ejss.12916
64. Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic E. Duarte et al. 10.1080/22797254.2022.2045226
65. Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil K. JOHN et al. 10.3390/land9120487
66. Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al. 10.1016/j.catena.2021.105299
67. Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra A. Descals et al. 10.3390/rs11212590
68. ML-based technologies in sustainable agro-food production and beyond: Tapping the (semi) arid landscape for bioactives-based product development T. Joshi et al. 10.1016/j.jafr.2024.101350
69. Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects O. Odebiri et al. 10.1007/s10661-021-09561-6
70. Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher  et al. 10.3389/fenvs.2021.809995
71. Modeling and digital mapping of shallow water table depth using satellite-based spectral and thermal data: Introducing a framework for digital shallow water table mapping M. Jeihouni et al. 10.1016/j.still.2024.106317
72. Rate of soil organic carbon sequestration in a millennium coastal soil chronosequence in northern Jiangsu, China C. Jiao et al. 10.1016/j.catena.2020.104627
73. Potential of soil minerals to sequester soil organic carbon H. Rodríguez-Albarracín et al. 10.1016/j.geoderma.2023.116549
74. Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model X. Zhu et al. 10.3389/feart.2024.1455124
75. Geochemical and mineralogical composition of the 2018 volcanic deposits of Mt. Anak Krakatau D. Fiantis et al. 10.1016/j.geodrs.2021.e00393
76. Assessing the improvement potentials of climate model partitioning and time-variant feature extraction for soil organic carbon prediction Y. Bao et al. 10.1016/j.catena.2024.108014
77. A Comparison between Sentinel-2 and Landsat 8 OLI Satellite Images for Soil Salinity Distribution Mapping Using a Deep Learning Convolutional Neural Network M. Kazemi Garajeh et al. 10.1080/07038992.2022.2056435
78. Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning P. Fu et al. 10.3390/rs16122217
79. An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran M. Garajeh et al. 10.1016/j.scitotenv.2021.146253
80. Mapping of Soil Organic Carbon Stocks Based on Aerial Photography in a Fragmented Desertification Landscape J. Lian et al. 10.3390/rs14122829
81. Assessment of different digital soil mapping methods for prediction of soil classes in the Shahrekord plain, Central Iran I. Esfandiarpour-Boroujeni et al. 10.1016/j.catena.2020.104648
82. Digital mapping of soil chemical properties with limited data in the Thung Kula Ronghai region, Thailand S. Srisomkiew et al. 10.1016/j.geoderma.2021.114942
83. Semantic Segmentation of Portuguese Agri-Forestry Using High-Resolution Orthophotos T. Morais et al. 10.3390/agronomy13112741
84. The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution D. Rukhovich et al. 10.3390/rs13010155
85. Deep learning for forest inventory and planning: a critical review on the remote sensing approaches so far and prospects for further applications A. Hamedianfar et al. 10.1093/forestry/cpac002
86. Machine learning-based digital mapping of soil organic carbon and texture in the mid-Himalayan terrain N. Rengma et al. 10.1007/s10661-023-11608-9
87. Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al. 10.1016/j.earscirev.2020.103359
88. An improved digital soil mapping approach to predict total N by combining machine learning algorithms and open environmental data A. Auzzas et al. 10.1007/s40808-024-02127-8
89. Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon M. Kılıç et al. 10.1371/journal.pone.0268658
90. Mapping Soil Organic Matter Content during the Bare Soil Period by Using Satellite Data and an Improved Deep Learning Network X. Xu & X. Zhai 10.3390/su15010323
91. Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units M. Ließ 10.3390/agriculture12111784
92. Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales T. Tan et al. 10.1021/acs.est.4c01172
93. Unveiling the explanatory power of environmental variables in soil organic carbon mapping: A global–local analysis framework Y. Wei et al. 10.1016/j.geoderma.2024.117011
94. Use of Topographic Models for Mapping Soil Properties and Processes X. Li et al. 10.3390/soilsystems4020032
95. From Rangelands to Cropland, Land-Use Change and Its Impact on Soil Organic Carbon Variables in a Peruvian Andean Highlands: A Machine Learning Modeling Approach M. Carbajal et al. 10.1007/s10021-024-00928-7
96. Balancing phosphorus fertilization for sustainable maize yield and soil test phosphorus management: A long-term study using machine learning . Jagdeep-Singh et al. 10.1016/j.fcr.2023.109169
97. Increment-averaged kriging: a comparison with depth-harmonized mapping of soil exchangeable sodium percentage in a cropping region of eastern Australia Y. Lai et al. 10.1016/j.geoderma.2019.114151
98. Proportional allocation with soil depth improved mapping soil organic carbon stocks M. Zhang et al. 10.1016/j.still.2022.105519
99. Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data C. Smith et al. 10.3390/rs12060928
100. Multivariate random forest for digital soil mapping S. van der Westhuizen et al. 10.1016/j.geoderma.2023.116365
101. Exploring the untapped potential of hand-feel soil texture data for enhancing digital soil mapping: Revealing hidden spatial patterns from field observations A. Eymard et al. 10.1016/j.geoderma.2023.116769
102. Predicting wetland soil properties using machine learning, geophysics, and soil measurement data D. Driba et al. 10.1007/s11368-024-03801-1
103. Machine learning techniques for estimating seismic site amplification in the Santiago basin, Chile J. Díaz et al. 10.1016/j.enggeo.2022.106764
104. History and interpretation of early soil and organic matter investigations in Deli, Sumatra, Indonesia B. Minasny et al. 10.1016/j.catena.2020.104909
105. Soil organic carbon prediction with terrain derivatives using geostatistics and sequential Gaussian simulation K. John et al. 10.1016/j.jssas.2021.04.005
106. Payments by modelled results: A novel design for agri-environmental schemes B. Bartkowski et al. 10.1016/j.landusepol.2020.105230
107. Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data A. Maxwell et al. 10.1029/2023EA002845
108. Teleconnections in spatial modelling T. Behrens et al. 10.1016/j.geoderma.2019.07.012
109. Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils A. Sakhaee et al. 10.3390/agriculture14081298
110. 3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase H. Gerke et al. 10.1002/jpln.202200239
111. Spatio-temporal mapping of soil water storage in a semi-arid landscape of northern Ghana – A multi-tasked ensemble machine-learning approach K. Nketia et al. 10.1016/j.geoderma.2021.115691
112. High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine . Fatchurrachman et al. 10.3390/rs14081875
113. Developing and testing of pedogenons in the lower Namoi valley, NSW, Australia H. Jang et al. 10.1016/j.geoderma.2022.116182
114. Convolutional Neural Network-Based Soil Water Content and Density Prediction Model for Agricultural Land Using Soil Surface Images D. Kim et al. 10.3390/app13052936
115. Increment-averaged kriging for 3-D modelling and mapping soil properties: Combining machine learning and geostatistical methods T. Orton et al. 10.1016/j.geoderma.2019.114094
116. Exploiting Soil and Remote Sensing Data Archives for 3D Mapping of Multiple Soil Properties at the Swiss National Scale F. Stumpf et al. 10.3390/rs16152712
117. Predicting soil properties in 3D: Should depth be a covariate? Y. Ma et al. 10.1016/j.geoderma.2020.114794
118. National-scale mapping of soil organic carbon stock in France: New insights and lessons learned by direct and indirect approaches Z. Chen et al. 10.1016/j.seh.2023.100049
119. Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area S. Yamaç et al. 10.1016/j.agwat.2020.106121
120. Mapping topsoil pH using different predictive models and covariate sets in Henan Province, Central China L. Zhang et al. 10.1016/j.ecoinf.2023.102290
121. A novel local-global dependency deep learning model for soil mapping Q. Li et al. 10.1016/j.geoderma.2023.116649
122. SpectraNet–53: A deep residual learning architecture for predicting soluble solids content with VIS–NIR spectroscopy J. Martins et al. 10.1016/j.compag.2022.106945
123. Modeling soil landscapes and soil textures using hyperscale terrain attributes S. Riza et al. 10.1016/j.geoderma.2021.115177
124. A new methodology for establishing an SOC content prediction model that is spatiotemporally transferable at multidecadal and intercontinental scales X. Meng et al. 10.1016/j.isprsjprs.2024.09.038
125. Digital mapping of soil moisture retention properties using solely satellite-based data and data mining techniques M. Jeihouni et al. 10.1016/j.jhydrol.2020.124786
126. A fine digital soil mapping by integrating remote sensing-based process model and deep learning method in Northeast China Y. Bao et al. 10.1016/j.still.2024.106010
127. Digital soil mapping and GlobalSoilMap. Main advances and ways forward D. Arrouays et al. 10.1016/j.geodrs.2020.e00265
128. Identifying soil provenance based on portable X-ray fluorescence measurements using similarity and inverse-mapping approaches – A case in the Lower Hunter Valley, Australia Y. Ma et al. 10.1016/j.geodrs.2021.e00368
129. Spatial prediction of soil properties using random forest, k-nearest neighbors and cubist approaches in the foothills of the Ural Mountains, Russia A. Suleymanov et al. 10.1007/s40808-023-01723-4
130. Comparison of Machine Learning-Based Prediction of Qualitative and Quantitative Digital Soil-Mapping Approaches for Eastern Districts of Tamil Nadu, India R. Kumaraperumal et al. 10.3390/land11122279
131. Prediction of Soil Organic Carbon Content in Complex Vegetation Areas Based on CNN-LSTM Model Z. Dong et al. 10.3390/land13070915
132. Estimating soil bacterial abundance and diversity in the Southeast Qinghai-Tibet Plateau Y. Yang et al. 10.1016/j.geoderma.2022.115807
133. Apparent ecosystem carbon turnover time: uncertainties and robust features N. Fan et al. 10.5194/essd-12-2517-2020
134. On the effectiveness of multi-scale landscape metrics in soil organic carbon mapping J. Wang et al. 10.1016/j.geoderma.2024.117026
135. BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. 10.5194/essd-16-2941-2024
136. Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2020.114552
137. An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning algorithms X. Meng et al. 10.1016/j.rse.2022.113166
138. The roles of sampling depth, lateral profile density and vertical sampling density in 3D digital soil mapping of soil organic carbon M. Zhang et al. 10.1016/j.geoderma.2024.116840
139. Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data N. Tziolas et al. 10.3390/rs12091389
140. Machine learning and multispectral data-based detection of soil salinity in an arid region, Central Iran V. Habibi et al. 10.1007/s10661-020-08718-z
141. Artificial neural network and spatial distribution of soil attributes for farming management in Western Desert, Egypt K. Darwish et al. 10.1007/s12517-022-10565-w
142. Machine learning and soil sciences: a review aided by machine learning tools J. Padarian et al. 10.5194/soil-6-35-2020
143. Grand Challenges in Pedometrics-AI Research S. Grunwald 10.3389/fsoil.2021.714323
144. Enhancement and analysis of hyperspectral satellite images for Soil Study and Behavior V. Malik et al. 10.1007/s11042-023-16729-4
145. Assessing the carbon sequestration potential and identifying influential factors of cultivated soils in Northeast China S. Wang et al. 10.1016/j.geodrs.2023.e00655
146. Continental-scale mapping of soil pH with SAR-optical fusion based on long-term earth observation data in google earth engine Y. Geng et al. 10.1016/j.ecolind.2024.112246
147. The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review R. Wang & W. Su 10.3390/agriculture14081225
148. Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon M. Goydaragh et al. 10.1016/j.catena.2021.105280
149. A review of spectral feature extraction and multi-feature fusion methods in predicting soil organic carbon X. Li et al. 10.1080/05704928.2024.2369570
150. Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales Y. Xia et al. 10.3389/fsoil.2022.890437
151. Modelling the extent of northern peat soil and its uncertainty with Sentinel: Scotland as example of highly cloudy region L. Poggio et al. 10.1016/j.geoderma.2019.03.017