نقشه‌برداری رقومی تغییرات سطحی و زیرسطحی کربن آلی و شوری خاک در بخشی از اراضی دشت قزوین (مطالعه موردی: مناطق آبیک و نظر آباد)

نوع مقاله : مقالات پژوهشی

نویسندگان

• گردآفرین رضایی ¹
• فریدون سرمدیان ²
• علی محمدی ترکاشوند ³
• جواد سید محمدی ⁴
• مریم مرعشی علی آبادی ⁵

¹ دانشجوی دکتری پیدایش و رده بندی و ارزیابی خاک، گروه علوم و مهندسی خاک، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی ، تهران، ایران.

² استاد گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

³ استاد گروه علوم و مهندسی خاک،واحد علوم و تحقیقات، دانشگاه آزاد اسلامی ، تهران، ایران.

⁴ استادیار پژوهش، موسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران.

⁵ استادیار گروه علوم و مهندسی خاک، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی ، تهران، ایران

https://doi.org/10.22067/jsw.2023.80369.1238

چکیده

آگاهی از توزیع مکانی شوری و کربن­ آلی خاک منجر به دستیابی به اطلاعات ارزشمندی می­گردد که در اتخاذ تصمیمات مدیریتی برای فعالیت­های کشاورزی موثر است. این مطالعه با هدف بررسی تغییرات مکانی قابلیت هدایت الکتریکی (EC) به عنوان بیانگر وضعیت شوری خاک و کربن­آلی خاک (SOC) در دو لایه سطحی (صفر تا30 سانتی­متر) و زیرسطحی (30-60 سانتی­متر) با استفاده از چهار الگوریتم یادگیری ماشین جنگل تصادفی (RF)، درخت تصمیم (DTr)، رگرسیون بردار پشتیبان (SVR) و شبکه عصبی مصنوعی (ANN) در دشت قزوین انجام شد. بدین منظور، از 278 خاکرخ مطالعاتی نمونه­برداری گردید و پس از عبور از الک دو میلی­متر، مقادیر EC و SOC آنها اندازه­گیری گردید. متغیرهای کمکی مستخرج از مدل رقومی ارتفاع و ماهواره لندست 8، شامل ارتفاع (Elevation)، تابش پخشیدگی (Diffuse)، شاخص همواری کف دره با درجه تفکیک بالا (MrVBF)، شاخص تفاوت پوشش گیاهی نرمال شده (NDVI)، شاخص خیسی ساگا (SWI) و شاخص جهت باد (WE) به­عنوان نمایندگان عوامل خاکسازی مورد استفاده قرار گرفتند. نتایج نشان داد که مدل RF برای پیش­بینی EC خاک در لایه سطحی با R² برابر با 74/0 و RMSE 36/0 و nRMSE 07/0وهمچنین پیش­بینی کربن­آلی در هر دو لایه سطحی و زیر سطحی با R² به­ترتیب برابر با 90/0 و 80/0 و به دنبال آن مدل DTr برای پیش­بینی شوری خاک در لایه زیرسطحی با R² 77/0 و RMSE/ 9/0 و nRMSE 17/0 نسبت به سایر مدل­ها دارای صحت بالاتری بودند. همچنین، متغیرهای توپوگرافی شامل ارتفاع، شدت تابش پخشیدگی و شاخص همواری کف دره با درجه تفکیک بالا بیشترین ارتباط را با تغییرات شوری و کربن­آلی خاک در هر دولایه سطحی و زیرسطحی داشتند. به طور کلی مدل­های RF و DTr به همراه متغیرهای توپوگرافی توانستند تغییرات شوری و کربن­آلی سطحی و زیرسطحی خاک را با صحت قابل قبول در منطقه مورد مطالعه ارائه نمایند؛ که نقشه­های تهیه شده می­توانند برای اعمال تصمیم­­های مدیریتی لازم در مورد خاک­های  منطقه مورد استفاده قرار گیرند.

کلیدواژه‌ها

• تغییرات سطحی و عمقی
• متغیرهای محیطی
• مدلسازی رقومی
• نقشه‌برداری ویژگی‌های خاک

20.1001.1.20084757.1402.37.2.9.0

موضوعات

• علوم خاک

عنوان مقاله [English]

Digital Mapping of Surface and Subsurface Soil Organic Carbon and Soil Salinity Variation in a Part of Qazvin Plain (Case Study: Abyek and Nazarabad Regions)

نویسندگان [English]

• Gordafarin Rezaie ¹
• F. Sarmadian ²
• Ali Mohammadi Torkashvand ³
• J. Seyedmohammadi ⁴
• Maryam Marashi Aliabadi ⁵

¹ Ph.D. student of Soil Genesis, Classification and Soil Evaluation of the soil science department.Islamic Azad University, Science and Research Branch, Tehran, Iran.

² Professor of Science and soil Engineering Department, Faculty of Agricultural , University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

³ Professor of the soil science department, Science and Research Branch, Islamic Azad University, Tehran, Iran

⁴ Assistant professor, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

⁵ Assistant professor of the soil science department, Science and Research Branch, Islamic Azad University, Tehran, Iran.

چکیده [English]

Introduction
Knowledge of the spatial distribution of soil salinity and soil organic carbon (SOC) leads to obtaining valuable information that is effective in decision-making for agricultural activities. More than a third of the world's land is affected by salt, which threatens the growth and production of crops, and prevents the development of sustainable agriculture. The high electrical conductivity (EC) content in soils poses significant challenges in arid and semi-arid regions, greatly impacting agricultural production. Saline and sodic soils often exhibit high levels of sodium which is a key characteristic. The presence of sodium ions leads to the destabilization of soil aggregates and the dispersion of soil particles resulting in the closure of soil pores. Consequently, unfavorable changes occur in the soil physical, chemical, and biological properties increasing its susceptibility to water and wind erosion. Additionally, high sodium levels can lead to the decomposition of soil organic carbon (SOC). SOC is crucial for water retention, cation exchange, and nutrient availability, making its reduction in agricultural soils a significant threat to sustainable soil management. Therefore, the investigation of soils in terms of EC and SOC contents and their spatial distribution is of great importance to support decision-makers in agricultural development planning to reduce challenges related to food security in arid and semi-arid regions.
Materials and Methods
This study was conducted with the aim of investigating the EC and SOC in topsoil (0-30 cm) and subsoil (30-60 cm) layers using four machine learning (ML) algorithms namely, random forest (RF), decision tree (DTr), support vector regression (SVR) and artificial neural network (ANN) performed in Qazvin Plain. The study area includes a part of agricultural lands and natural areas of Alborz and Qazvin provinces, between the Nazarabad and Abyek cities in Iran. This region with an area of 60,000 hectares is located at latitude 35° 54´ to 36° 54´ to the north and 50° 15´ to 50° 39´ to the east. This research was carried out in four stages including (i) soil sampling and measuring the physical and chemical properties of the soil and preparation of environmental covariates from a digital elevation model (DEM) with spatial resolution 12.5 m and Landsat 8 satellite imagery with spatial resolution 30 m by SAGA GIS and ENVI software, (ii) spatial modeling of soil EC and SOC in the topsoil and subsoil layers by the RF, SVR, ANN, and DTr ML algorithms, (iii) evaluating the efficiency of the ML algorithms and determining the relative importance of environmental covariates, and (iv) preparation of spatial prediction maps of EC and SOC in the topsoil (0-30 cm) and subsoil (30-60 cm) layers in the study area.
Results and Discussion
         The result of the spatial prediction maps of EC showed that the studied area has non-saline to very saline soils up to a depth of 60 cm. It is also possible that the EC equivalent shows a decreasing trend in soil salinity with a depth from 6.05 to 5.55 ds/m from the topsoil to the subsoil layer. The highest amount of SOC was observed in the surface layer equal to 3.3%. Globally SOC content decreased from the surface (average of 0.84%) to depth (average of 0.4%). The high spatial variability of SOC showed that the soils of the study area are affected by management activity.
 Environmental covariates were extracted as a proxy of topography and remote sensing indices including elevation, diffuse Insolation (Diffuse), Multi-Resolution Index of Valley Bottom Flatness (MrVBF), Normalized Differences Vegetation Index (NDVI), SAGA wetness index (SWI) and wind Effect (WE) were used as representatives of soil formation factors. The topography parameters, including the elevation, diffuse insolation, and Multi-Resolution Index of Valley Bottom Flatness, were most closely related to EC and SOC variations in each topsoil and subsoil layer. Elevation can be justified around 50% and 35% of EC and 28.56% and 29.47% of SOC variations in the topsoil and subsoil layers, respectively, followed by the diffuse variable can succeed to justified 19.7% and 25.1% of EC and 27.28% and 27.67% of SOC spatial variations in the topsoil and subsoil layers, respectively.
The results confirmed that the RF was recognized as outperforming the ML model for predicting EC in the topsoil (R2 =0.74, RMSE =0.36, and nRMSE= 0.07), as well as predicting SOC in topsoil and subsoil layers (R2= 90 and R2=0.80), followed by the DTr for predicting EC (R2 0.77, RMSE/0.9, and nRMSE 0.17) in the subsoil layer in comparison other models.
 Conclusion
       The RF (Random Forest) and DTr (Decision Tree) models incorporating topographic parameters demonstrated satisfactory accuracy in predicting the variation of topsoil and subsoil electrical conductivity (EC) and soil organic carbon (SOC) in the study area. Topography plays a crucial role in soil formation, and elevation-based topographic attributes are commonly used as key predictors in digital soil mapping projects. The variability in topography influences water flow and sedimentation processes which, in turn, affects soil development and the spatial distribution of soil properties. The resulting soil maps can be valuable tools for decision-making programs related to soil management in the region.

کلیدواژه‌ها [English]

• Environmental covariates
• Soil properties mapping
• Spatial modeling
• Topsoil and subsoil variations

مراجع

1. Abd El-Aziz, S.H., Gameh, M.A., & Ghallab, A. (2018). Applications of geographic information systems in studying changes in groundwater quality and soil salinity in Sohag Governorate. Eurasian Journal of Soil Science, 7(3), 213-223. https://doi.org/10.18393/ejss.416675.
2. Abd-Elmabod, S.K., Fitch, A.C., Zhang, Z., Ali, R.R., & Jones, L. (2019). Rapid urbanisation threatens fertile agricultural land and soil carbon in the Nile delta. Journal of Environmental Management,252, 109668. https://doi.org/10.1016/j.jenvman.2019.109668
3. Ardakani, M.A., & Vahdati, A.R. (2018). Monitoring of organic matter and soil salinity by using IRS-LissIII satellite data in the Harat plain, of Yazd province. Desert23(1): 1-8
4. Avliyakulov, M.A., Kumari, M., Rajabov, N.Q., & Durdiev, N.K. (2020). Characterization of soil salinity and its impact on wheat crop using space-borne hyperspectral data. Geoinformation Support of Sustainable Development of Territories,26(Part 3), 271-285.
5. Batista, F. (2020). Geostatistical analysis of soil properties of the karstic sub-horizontal plain of the Yucatan Peninsula. Tropical and Subtropical Agroecosystems, 24, 09.
6. Boehner, J., Koethe, R., Conrad, O., Gross, J., Ringeler, A., & Selige, T. (2002): Soil regionalisation by means of Terrain analysis and process parameterisation. In: Micheli, E., Nachtergaele, F., Montanarella, L. [Ed.]: Soil Classification 2001. European Soil Bureau, Research Report No. 7, EUR 20398 EN, Luxembourg. pp.213-222.
7. Boser, B.E., Guyon, I.M., & Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Association for Computing Machinery, pp. 144–152.
8. Breiman, L. (2001). Random forests: Machine Learning 45(1): 5-32. https://doi.org/10.1023/A:1010933404324.
9. Corwin, D.L. (2021). Climate change impacts on soil salinity in agricultural areas. European Journal of Soil Science,72(2), 842-862. https://doi.org/10.1111/ejss.13010
10. de Anta, R.C., Luís, E., Febrero-Bande, M., Galiñanes, J., Macías, F., Ortíz, R., & Casás, F. (2020). Soil organic carbon in peninsular Spain: influence of environmental factors and spatial distribution. Geoderma,370, 114365.
11. Esfandiarpour-Boroujeni, I., Shahini-Shamsabadi, M., Shirani, H., Mosleh, Z., Bagheri-Bodaghabadi, M., & Salehi, M.H. (2020). Assessment of different digital soil mapping methods for prediction of soil classes in the Shahrekord plain, Central Iran. Catena,193, 104648
12. FAO and ITPS. (2015). Status of the World’s Soil Resources (SWSR) – Main Report. Food and Agriculture 439 Organization of the United Nations and Intergovernmental Technical Panel on Soils, Rome, Italy.
13. Fathizad, H., Ali Hakimzadeh Ardakani, M., Sodaiezadeh, H., Kerry, R., & Taghizadeh Mehrjardi, R. (2020). Investigation of the spatial and temporal variation of soil salinity using random forests in the central desert of Iran. Geoderma, 365, 114233.
14. Fu, T., Gao, H., & Liu, J. (2021). Comparison of different interpolation methods for prediction of soil salinity in arid irrigation region in northern China. Agronomy 11(8): 1535. https://doi.org/10.3390/agronomy11081535
15. Gholizadeh, A., Žižala, D., Saberioon, M., & Borůvka, L. (2018). Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging. Remote Sensing of Environment218: 89-103. https://doi.org/10.1016/j.rse.2018.09.015
16. Gomes, L.C., Faria, R. M., de Souza, E., Veloso, G.V., Schaefer, C.E.G., & Fernandes Filho, E.I. (2019). Modelling and mapping soil organic carbon stocks in Brazil. Geoderma,340, 337-350. https://doi.org/10.1016/j.geoderma.2019.01.007.
17. .Halima, O.I., Azzouzi, M.E., Douaik, A., Azim, K., & Zouahri, A. (2019). Organic and inorganic remediation of soils affected by salinity in the Sebkha of Sed El Mesjoune–Marrakech (Morocco). Soil and Tillage Research,193, 153-160. https://doi.org/10.1016/j.still.2019.06.003.
18. Hamzehpour, N., Shafizadeh-Moghadam, H., & Valavi, R. (2019). Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture. Catena,182, 104141. https://doi.org/10.1016/j.catena.2019.104141
19. Hassani, A., Azapagic, A., & Shokri, N. (2020). Predicting long-term dynamics of soil salinity and sodicity on a global scale. Proceedings of the National Academy of Sciences,117(52), 33017-33027. https://doi.org/10.1073/pnas.2013771117
20. 17-Hengl, T., Heuvelink, G.B.M., & Stein, A. (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120, 75–93. https://doi.org/10.1016/j.geoderma.2003.08.018
21. Heung, B., Ho, H.C., Zhang, J., Knudby, A., Bulmer, C.E., Schmidt, M.G. (2016). An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma265: 62-77. https://doi.org/10.1016/j.geoderma.2015.11.014
22. Holmquist, J. R., Windham-Myers, L., Bliss, N., Crooks, S., Morris, J.T., Megonigal, J. P., & Woodrey, M. (2018). Accuracy and precision of tidal wetland soil carbon mapping in the conterminous United States. Scientific Reports, 8(1), 1-16. https://doi.org/10.1038/s41598-018-26948-7
23. Jackson, M.L. (1967). Soil Chemical Analysis–Prentice Hall Inc. Englewood Cliffs, NJ, USA.
24. Khamoshi, S. E., Sarmadian, F., & Keshavarzi, A. (2018). Digital soil mapping using random forests model in Abyek, Qazvin province. Iranian Journal of Soil Research, 32(3).
25. Khaledian, Y., & Miller, B.A. (2020). Selecting appropriate machine learning methods fordigital soil mapping. Appl. Math Model 81: 401–418. https://doi.org/10.1016/j.apm.2019.12.016.
26. Khazaie, E., Bostani, A. A., & Davatgar, N. (2017). Geostatic and GIS evaluation of spatial variability of nitrogen, phosphorus, potassium, and cation exchange capacity in agro-industrial land of Sharif Abad in Qazvin. Iranian Journal of Soil Research, 31(2), 195-213. http://doi.org/10.22092/ijsr.2017.11310.
27. Lamichhane, S., Kumar, L., & Wilson, B. (2019). Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review. Geoderma,352, 395-413. https://doi.org/10.1016/j.geoderma.2019.05.031
28. Ma, G., Ding, J., Han, L., Zhang, Z., & Ran, S. (2021). Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms. Regional Sustainability,2(2), 177-188. https://doi.org/10.1016/j.regsus.2021.06.001
29. Mahmoudzadeh, H., Matinfar, H. R., Taghizadeh-Mehrjardi, R., & Kerry, R. (2020). Spatial prediction of soil organic carbon using machine learning techniques in western Iran. Geoderma Regional,21, e00260. https://doi.org/10.1016/j.geodrs.2020.e00260
30. Mashalaba, L., Galleguillos, M., Seguel, O., & Olivares, J. (2020). Predicting spatial variability of selected soil properties using digital soil mapping in a rainfed vineyard of central Chile. Geoderma Regional, 22, e00289 https://doi.org/10.1016/j.geodrs.2020.e00289
31. McBratney, A., Santos, M.M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117, 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4
32. Mousavi, S. R., Sarmadian, F., Omid, M., & Bogaert, P. (2021). Digital modeling of three-dimensional soil salinity variation using machine learning algorithms in arid and semi-arid lands of Qazvin plain. Iranian Journal of Soil and Water Research,52(7), 1915-1929. (In Persian with English abstract)
33. Mousavi, S. R., Sarmadian, F., Omid, M., & Bogaert, P. (2022). Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran. Measurement,201, 111706. (In Persian with English abstract)
34. Nabiollahi, K., Eskandari, S., Taghizadeh-Mehrjardi, R., Kerry, R., & Triantafilis, J. (2019). Assessing soil organic carbon stocks under land-use change scenarios using random forest models. Carbon Management,10(1), 63-77. https://doi.org/10.1016/j.measurement.2022.111706
35. Nawar, S., & Mouazen, A.M. (2017). Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line Vis-NIR spectroscopy measurements of soil total nitrogen and total carbon. Sensors,17(10), 2428. https://doi.org/10.3390/s17102428
36. Osmani, M., Osmani, F., & Pourhoseingholi, M.A. (2019). Comparison of decision tree and logistic regression for prediction of functional dyspepsia and gastroesophageal reflux disease in tehran province using rome iii. Modern Care Journal,16(4).
37. Rahmani, A., Sarmadian, F., & Arefi, H. (2022). Digital mapping of top-soil thickness and associated uncertainty using machine learning approach in some part of arid and semi-arid lands of Qazvin Plain. Iranian Journal of Soil and Water Research,53(3), 585-602. (In Persian with English abstract)
38. Rhoades, J.D. (1982). Cation exchangeable capacity. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis: Part2. Chemical and Microbiological Properties. Agronomy Monograph, 9, 149–157.
39. Rossel, R.A.V. & McBratney, A.B. (2009). Diffuse reflectance spectroscopy as a tool for digital soil mapping. In: Digital soil mapping with limited data.
40. Shabani, S., Samadianfard, S., Sattari, M. T., Mosavi, A., Shamshirband, S., Kmet, T., & Várkonyi-Kóczy, A.R. (2020). Modeling pan evaporation using Gaussian process regression K-nearest neighbors’ random forest and support vector machines; comparative analysis. Atmosphere,11(1), 66. https://doi.org/10.3390/atmos11010066
41. Swileam, G.S., Shahin, R.R., Nasr, H.M., & Essa, K.S. (2019). Assessment of soil variability using electrical resistivity technique for normal alluvial soils, Egypt. Plant Archives,19(1), 905-912.
42. Taati, A., Sarmadian, F., Motaghian, H., & Mousavi, S.R. (2020). Mapping Features of Surface and Depth, Soil Profiles by Using Geostatistical Techniques in Part of Qazvin Plain. Human & Environment,18(1), 67-81.
43. Taghadosi, M.M., Hasanlou, M., & Eftekhari, K. (2019). Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery. International Journal of Remote Sensing,40(1), 237-252. https://doi.org/10.1080/01431161.2018.1512767
44. Taghizadeh-Mehrjardi, R., Schmidt, K., Toomanian, N., Heung, B., Behrens, T., Mosavi, A., & Scholten, T. (2021). Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models. Geoderma,383, https://doi.org/10.1016/j.geoderma.2020.114793
45. Tripathi, A., & Tiwari, R.K. (2021). A simplified subsurface soil salinity estimation using synergy of SENTINEL‐1 SAR and SENTINEL‐2 multispectral satellite data, for early stages of wheat crop growth in Rupnagar, Punjab, India. Land Degradation & Development,32(14), 3905-3919. https://doi.org/10.1002/ldr.4009
46. Wallach, D., Makowski, D., Jones, J.W., & Brun, F. (2006). Working with dynamic crop models: evaluation, analysis, parameterization, and applications. Elsevier.
47. Wang, J., Peng, J., Li, H., Yin, C., Liu, W., Wang, T., & Zhang, H. (2021). Soil salinity mapping using machine learning algorithms with the sentinel-2 MSI in Arid Areas, China. Remote Sensing,13(2), 305. https://doi.org/10.3390/rs13020305
48. Wilding, L.P. (1985). Spatial variability: its documentation, accommodation, and implication to soil surveys. In: Soil Spatial Variability, Las Vegas NV, pp. 166–194.
49. Wu, W., Zucca, C., Muhaimeed, A. S., Al‐Shafie, W. M., Fadhil Al‐Quraishi, A. M., Nangia, V., & Liu, G. (2018). Soil salinity prediction and mapping by machine learning regression in C entral M esopotamia, I raq. Land Degradation & Development,29(11), 4005-4014. https://doi.org/10.1002/ldr.3148
50. Zhang, H., Wu, P., Yin, A., Yang, X., Zhang, M., & Gao, C. (2017). Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model. Science of the Total Environment,592, 704-713. https://doi.org/10.1016/j.scitotenv.2017.02.146
51. Zhao, C., Zhang, H., Song, C., Zhu, J.K., & Shabala, S. (2020). Mechanisms of plant responses and adaptation to soil salinity. The Innovation1(1): 100017. https://doi.org/10.1016/ j.xinn.2020.100017
52. Zhou, T., Geng, Y., Chen, J., Pan, J., Haase, D., & Lausch, A. (2020). High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms. Science of The Total Environment,729, 138244. https://doi.org/10.1016/j.scitotenv.2020.138244
53. Zhou, Y., Hartemink, A.E., Shi, Z., Liang, Z., & Lu, Y. (2019). Land use and climate change effects on soil organic carbon in North and Northeast China. Science of the Total Environment,647, 1230-1238. https://doi.org/10.1016/j.scitotenv.2018.08.016
54. Žížala, D., Minařík, R., & Zádorová, T. (2019). Soil organic carbon mapping using multispectral remote sensing data: Prediction ability of data with different spatial and spectral resolutions. Remote Sensing,11(24), 2947. https://doi.org/10.3390/rs11242947.