{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:18:06Z","timestamp":1774505886983,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001772","name":"University of New England","doi-asserted-by":"publisher","award":["International Postgraduate Research Award (IPRA)"],"award-info":[{"award-number":["International Postgraduate Research Award (IPRA)"]}],"id":[{"id":"10.13039\/501100001772","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although algorithms are well developed to predict soil organic carbon (SOC), selecting appropriate covariates to improve prediction accuracy is an ongoing challenge. Terrain attributes and remote sensing data are the most common covariates for SOC prediction. This study tested the predictive performance of nine different combinations of topographic variables and multi-season remotely sensed data to improve the prediction of SOC in the cultivated lands of a middle mountain catchment of Nepal. The random forest method was used to predict SOC contents, and the quantile regression forest for quantifying the prediction uncertainty. Prediction of SOC contents was improved when remote sensing data of multiple seasons were used together with the terrain variables. Remote sensing data of multiple seasons capture the dynamic conditions of surface soils more effectively than using an image of a single season. It is concluded that the use of remote sensing images of multiple seasons instead of a snapshot of a single period may be more effective for improving the prediction of SOC in a digital soil mapping framework. However, an image with the right timing of cropping season can provide comparable results if a parsimonious model is preferred.<\/jats:p>","DOI":"10.3390\/rs13234772","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4772","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Use of Multi-Seasonal Satellite Images to Predict SOC from Cultivated Lands in a Montane Ecosystem"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3497-1302","authenticated-orcid":false,"given":"Sushil","family":"Lamichhane","sequence":"first","affiliation":[{"name":"School of Environmental and Rural Science, University of New England, Armidale, NSW 2350, Australia"},{"name":"National Soil Science Research Centre, Nepal Agricultural Research Council, Khumaltar, Lalitpur 44700, Nepal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1365-7015","authenticated-orcid":false,"given":"Kabindra","family":"Adhikari","sequence":"additional","affiliation":[{"name":"Grassland, Soil & Water Research Laboratory, USDA\u2014Agricultural Research Service, Temple, TX 76502, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9205-756X","authenticated-orcid":false,"given":"Lalit","family":"Kumar","sequence":"additional","affiliation":[{"name":"EastCoast Geospatial Consultants, Armidale, NSW 2350, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.5194\/bg-12-2119-2015","article-title":"Modeling the impact of agricultural land use and management on US carbon budgets","volume":"12","author":"Drewniak","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geosus.2020.03.001","article-title":"Global pattern and change of cropland soil organic carbon during 1901\u20132010: Roles of climate, atmospheric chemistry, land use and management","volume":"1","author":"Ren","year":"2020","journal-title":"Geogr. Sustain."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.geoderma.2012.01.038","article-title":"Land degradation impact on soil carbon losses through water erosion and CO2 emissions","volume":"177\u2013178","author":"McHunu","year":"2012","journal-title":"Geoderma"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.geoderma.2015.11.010","article-title":"Farm-scale soil carbon auditing","volume":"265","author":"McBratney","year":"2016","journal-title":"Geoderma"},{"key":"ref_5","first-page":"102277","article-title":"Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands","volume":"96","author":"Vaudour","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"142661","DOI":"10.1016\/j.scitotenv.2020.142661","article-title":"Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images","volume":"755","author":"Zhou","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Vaudour, E., Gomez, C., Loiseau, T., Baghdadi, N., Loubet, B., Arrouays, D., Ali, L., and Lagacherie, P. (2019). The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands. Remote Sens., 11.","DOI":"10.3390\/rs11182143"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"137703","DOI":"10.1016\/j.scitotenv.2020.137703","article-title":"Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran","volume":"721","author":"Fathololoumi","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0016-7061(03)00223-4","article-title":"On digital soil mapping","volume":"117","author":"McBratney","year":"2003","journal-title":"Geoderma"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1111\/j.1365-2389.2008.01092.x","article-title":"Airborne radiometric survey data and a DTM as covariates for regional scale mapping of soil organic carbon across Northern Ireland","volume":"60","author":"Rawlins","year":"2009","journal-title":"Eur. J. Soil Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.geoderma.2015.12.003","article-title":"Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran","volume":"266","author":"Nabiollahi","year":"2016","journal-title":"Geoderma"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Peng, Y., Xiong, X., Adhikari, K., Knadel, M., Grunwald, S., and Greve, M.H. (2015). Modeling soil organic carbon at regional scale by combining multi-spectral images with laboratory spectra. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0142295"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., and Bochtis, D. (2019). Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review. Remote Sens., 11.","DOI":"10.3390\/rs11060676"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.isprsjprs.2018.11.026","article-title":"Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands","volume":"147","author":"Castaldi","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2018.09.015","article-title":"Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging","volume":"218","author":"Gholizadeh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, X., Ding, J., Liu, J., Ge, X., and Zhang, J. (2021). Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang. Remote Sens., 13.","DOI":"10.3390\/rs13040769"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"113896","DOI":"10.1016\/j.geoderma.2019.113896","article-title":"Prediction of soil organic matter using multi-temporal satellite images in the Songnen Plain, China","volume":"356","author":"Dou","year":"2019","journal-title":"Geoderma"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/S0034-4257(00)00146-2","article-title":"Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems","volume":"74","author":"Hill","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_19","unstructured":"Kauth, R.J., and Thomas, G. (July, January 29). The tasselled cap\u2014A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the LARS Symposia, West Lafayette, IN, USA."},{"key":"ref_20","unstructured":"ESRI (2021, June 30). Tasseled Cap Function. Available online: https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/help\/analysis\/raster-functions\/tasseled-cap-function.htm."},{"key":"ref_21","unstructured":"Tadono, T., Takaku, J., Tsutsui, K., Oda, F., and Nagai, H. (2015, January 26\u201331). Status of \u201cALOS World 3D (AW3D)\u201d global DSM generation. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1002\/joc.5669","article-title":"Spatio-temporal variability of extreme precipitation in Nepal","volume":"38","author":"Talchabhadel","year":"2018","journal-title":"Int. J. Climatol."},{"key":"ref_23","unstructured":"NARC\/AFACI (2015). 3rd Annual Technical Report on Agro-Meteorological Information for the Adaptation to Climate Change in Nepal, NARC\/AFACI\u2014AMIS Project."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115041","DOI":"10.1016\/j.geoderma.2021.115041","article-title":"Updating the national soil map of Nepal through digital soil mapping","volume":"394","author":"Lamichhane","year":"2021","journal-title":"Geoderma"},{"key":"ref_25","unstructured":"(2019). Exelis Visual Information Solutions, ENVI, 5.5; L3 Harris Geospatial."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1016\/j.cageo.2005.12.009","article-title":"A conditioned Latin hypercube method for sampling in the presence of ancillary information","volume":"32","author":"Minasny","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_27","unstructured":"Brus, D. (2010, January 1\u20136). Design-based and model-based sampling strategies for soil monitoring. Proceedings of the 19th World Congress of Soil Science, Solutions for a Changing World, Brisbane, Australia."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1111\/j.1365-2389.2011.01364.x","article-title":"Sampling for validation of digital soil maps","volume":"62","author":"Brus","year":"2011","journal-title":"Eur. J. Soil Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1097\/00010694-193401000-00003","article-title":"Estimation of soil organic carbon by the chromic acid titration method","volume":"37","author":"Walkley","year":"1934","journal-title":"Soil Sci."},{"key":"ref_30","unstructured":"Sparks, D.L., Page, A.L., Helmke, P.A., and Loeppert, R.H. (1996). Total Carbon, Organic Carbon, and Organic Matter. Methods of Soil Analysis Part 3\u2014Chemical Methods, Soil Science Society of America, American Society of Agronomy."},{"key":"ref_31","first-page":"e290","article-title":"\u00dcber die Bestimmung des Wassers, des Humus, des Schwefels, der in den collo\u00efdalen Silikaten gebundenen Kiesels\u00e4ure, des Mangans usw im Ackerboden","volume":"37","year":"1890","journal-title":"Die Landwirthschaftlichen Versuchs-Stationen"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.5194\/gmd-8-1991-2015","article-title":"System for automated geoscientific analyses (SAGA) v. 2.1. 4","volume":"8","author":"Conrad","year":"2015","journal-title":"Geosci. Model Dev. Discuss."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4038","DOI":"10.1109\/JSTARS.2019.2938388","article-title":"Derivation of Tasseled Cap Transformation Coefficients for Sentinel-2 MSI At-Sensor Reflectance Data","volume":"12","author":"Shi","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pasqualotto, N., Delegido, J., Van Wittenberghe, S., Rinaldi, M., and Moreno, J. (2019). Multi-crop green LAI estimation with a new simple Sentinel-2 LAI Index (SeLI). Sensors, 19.","DOI":"10.3390\/s19040904"},{"key":"ref_35","unstructured":"Lindsay, J.B. (2018). WhiteboxTools User Manual, Geomorphometry and Hydrogeomatics Research Group, University of Guelph."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1080\/136588197242266","article-title":"Modelling topographic variation in solar radiation in a GIS environment","volume":"11","author":"Kumar","year":"1997","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.geoderma.2019.05.031","article-title":"Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review","volume":"352","author":"Lamichhane","year":"2019","journal-title":"Geoderma"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Hounkpatin, O.K.L., Welp, G., and Thiel, M. (2017). High resolution mapping of soil properties using Remote Sensing variables in south-western Burkina Faso: A comparison of machine learning and multiple linear regression models. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0170478"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.geoderma.2018.09.006","article-title":"Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran","volume":"338","author":"Zeraatpisheh","year":"2019","journal-title":"Geoderma"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e5518","DOI":"10.7717\/peerj.5518","article-title":"Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables","volume":"6","author":"Hengl","year":"2018","journal-title":"PeerJ"},{"key":"ref_42","first-page":"983","article-title":"Quantile regression forests","volume":"7","author":"Meinshausen","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.geoderma.2016.12.017","article-title":"Using quantile regression forest to estimate uncertainty of digital soil mapping products","volume":"291","author":"Vaysse","year":"2017","journal-title":"Geoderma"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e00387","DOI":"10.1016\/j.geodrs.2021.e00387","article-title":"Digital soil mapping of soil organic carbon stocks in Western Ghats, South India","volume":"25","author":"Dharumarajan","year":"2021","journal-title":"Geoderma Reg."},{"key":"ref_45","unstructured":"Meinshausen, N. (2017). Quantregforest: Quantile Regression Forests, Available online: https:\/\/cran.r-project.org\/web\/packages\/quantregForest\/index.html."},{"key":"ref_46","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, The R Foundation for Statistical Computing."},{"key":"ref_47","unstructured":"Yigini, Y., Olmedo, G.F., Reiter, S., Baritz, R., Viatkin, K., and Vargas, R. (2018). Soil Organic Carbon Mapping Cookbook, FAO. [2nd ed.]."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4540","DOI":"10.1038\/s41467-020-18321-y","article-title":"Spatial validation reveals poor predictive performance of large-scale ecological mapping models","volume":"11","author":"Ploton","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/biomet\/37.1-2.17","article-title":"Notes on continuous stochastic phenomena","volume":"37","author":"Moran","year":"1950","journal-title":"Biometrika"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S2095-3119(16)61337-0","article-title":"Tillage, crop residue, and nutrient management effects on soil organic carbon in rice-based cropping systems: A review","volume":"16","author":"Ghimire","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Marschner, P., and Rengel, Z. (2007). Composition and Cycling of Organic Carbon in Soil. Nutrient Cycling in Terrestrial Ecosystems, Springer.","DOI":"10.1007\/978-3-540-68027-7"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.geoderma.2019.02.007","article-title":"Effect of cultivation history on soil organic carbon status of arable land in northeastern China","volume":"342","author":"Wang","year":"2019","journal-title":"Geoderma"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"43","DOI":"10.5194\/isprs-archives-XLI-B2-43-2016","article-title":"Spectral color indices based geospatial modeling of soil organic matter in Chitwan district, Nepal","volume":"XLI-B2","author":"Mandal","year":"2016","journal-title":"ISPRS\u2014Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Hengl, T., Heuvelink, G.B.M., Kempen, B., Leenaars, J.G.B., Walsh, M.G., Shepherd, K.D., Sila, A., MacMillan, R.A., De Jesus, J.M., and Tamene, L. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0125814"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.geodrs.2014.11.003","article-title":"Evaluating Digital Soil Mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France)","volume":"4","author":"Vaysse","year":"2015","journal-title":"Geoderma Reg."},{"key":"ref_56","first-page":"12","article-title":"Estimation of Temperature over Nepal","volume":"14","author":"Nayava","year":"1982","journal-title":"Himal. Rev."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Geiger, R., Aron, R.H., and Todhunter, P. (1995). The Climate near the Ground, Vieweg. [5th ed.].","DOI":"10.1007\/978-3-322-86582-3"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4772\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:35:35Z","timestamp":1760168135000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4772"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,25]]},"references-count":57,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234772"],"URL":"https:\/\/doi.org\/10.3390\/rs13234772","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,25]]}}}