IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i24p13930-d704212.html
   My bibliography  Save this article

Research on the Prediction of Several Soil Properties in Heihe River Basin Based on Remote Sensing Images

Author

Listed:
  • Zhihui Li

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China)

  • Yang Yang

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China)

  • Siyu Gu

    (College of Resources and Environment, Northeast Agricultural University, Harbin 150000, China)

  • Boyu Tang

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China)

  • Jing Zhang

    (School of Information Science and Engineering, University of Jinan, Jinan 250000, China)

Abstract

Soil property monitoring is useful for sustainable agricultural production and environmental modeling. It is possible to automatically predict soil properties in a wide range based on remote sensing images. Heihe River Basin was chosen as the research area. Measurements on three soil properties, which were pH, organic carbon, and bulk density, were available there. Two kinds of attributes were extracted, which were the remote sensing index and terrain attributes. The prediction models were constructed by random forest algorithms. The features were determined by combining correlation statistics with prediction error, and different features were selected for each of the three properties. The validation experimental results are presented. The error results were as follows: pH (MAE = 0.28, RMSE = 0.39, R 2 = 0.41), organic carbon (MAE = 4.75, RMSE = 8.26, R 2 = 0.75), and bulk density (MAE = 0.11, RMSE = 0.13, R 2 = 0.70). Through the analysis and comparison of the experimental results, it was proven that the algorithm in this paper had a good performance in the prediction of organic carbon and bulk density.

Suggested Citation

  • Zhihui Li & Yang Yang & Siyu Gu & Boyu Tang & Jing Zhang, 2021. "Research on the Prediction of Several Soil Properties in Heihe River Basin Based on Remote Sensing Images," Sustainability, MDPI, vol. 13(24), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13930-:d:704212
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/24/13930/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/24/13930/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gerald Forkuor & Ozias K L Hounkpatin & Gerhard Welp & Michael Thiel, 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, Public Library of Science, vol. 12(1), pages 1-21, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guillermo Martínez Pastur & Marie-Claire Aravena Acuña & Jimena E. Chaves & Juan M. Cellini & Eduarda M. O. Silveira & Julián Rodriguez-Souilla & Axel von Müller & Ludmila La Manna & María V. Lencinas, 2023. "Nitrogenous and Phosphorus Soil Contents in Tierra del Fuego Forests: Relationships with Soil Organic Carbon, Climate, Vegetation and Landscape Metrics," Land, MDPI, vol. 12(5), pages 1-18, April.
    2. Kingsley JOHN & Isong Abraham Isong & Ndiye Michael Kebonye & Esther Okon Ayito & Prince Chapman Agyeman & Sunday Marcus Afu, 2020. "Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil," Land, MDPI, vol. 9(12), pages 1-20, December.
    3. Kangbéni Dimobe & Jean Léandre N’djoré Kouakou & Jérôme E. Tondoh & Benewinde J.-B. Zoungrana & Gerald Forkuor & Korotimi Ouédraogo, 2018. "Predicting the Potential Impact of Climate Change on Carbon Stock in Semi-Arid West African Savannas," Land, MDPI, vol. 7(4), pages 1-21, October.
    4. Chenxia Hu & Alan L Wright & Gang Lian, 2019. "Estimating the Spatial Distribution of Soil Properties Using Environmental Variables at a Catchment Scale in the Loess Hilly Area, China," IJERPH, MDPI, vol. 16(3), pages 1-14, February.
    5. Ramalingam Kumaraperumal & Sellaperumal Pazhanivelan & Vellingiri Geethalakshmi & Moorthi Nivas Raj & Dhanaraju Muthumanickam & Ragunath Kaliaperumal & Vishnu Shankar & Athira Manikandan Nair & Manoj , 2022. "Comparison of Machine Learning-Based Prediction of Qualitative and Quantitative Digital Soil-Mapping Approaches for Eastern Districts of Tamil Nadu, India," Land, MDPI, vol. 11(12), pages 1-26, December.
    6. Jasmina Defterdarović & Lana Filipović & Filip Kranjčec & Gabrijel Ondrašek & Diana Kikić & Alen Novosel & Ivan Mustać & Vedran Krevh & Ivan Magdić & Vedran Rubinić & Igor Bogunović & Ivan Dugan & Kre, 2021. "Determination of Soil Hydraulic Parameters and Evaluation of Water Dynamics and Nitrate Leaching in the Unsaturated Layered Zone: A Modeling Case Study in Central Croatia," Sustainability, MDPI, vol. 13(12), pages 1-20, June.
    7. Fatemeh Sadat Hosseini & Myoung Bae Seo & Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Mohammad Jamshidi & Soo-Mi Choi, 2023. "Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting," Sustainability, MDPI, vol. 15(19), pages 1-25, September.
    8. Wu Xiao & Wenqi Chen & Tingting He & Linlin Ruan & Jiwang Guo, 2020. "Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    9. Fahao Wang & Weidong Lu & Jingyun Zheng & Shicheng Li & Xuezhen Zhang, 2020. "Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000," Sustainability, MDPI, vol. 12(3), pages 1-16, February.
    10. Seyedalireza Khatibi & Azadeh Aghajanpour, 2020. "Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field," Energies, MDPI, vol. 13(14), pages 1-16, July.
    11. Chipidza, Wallace & Yan, Jie, 2020. "Does Flagging POTUS’s Tweets Lead to Fewer or More Retweets? Preliminary Evidence from Machine Learning Models," SocArXiv 69hkb, Center for Open Science.
    12. Jules Degila & Ida Sèmévo Tognisse & Anne-Carole Honfoga & Sèton Calmette Ariane Houetohossou & Fréjus Ariel Kpedetin Sodedji & Hospice Gérard Gracias Avakoudjo & Souand Peace Gloria Tahi & Achille Ep, 2023. "A Survey on Digital Agriculture in Five West African Countries," Agriculture, MDPI, vol. 13(5), pages 1-15, May.
    13. Showmitra Kumar Sarkar & Saifullah Bin Ansar & Khondaker Mohammed Mohiuddin Ekram & Mehedi Hasan Khan & Swapan Talukdar & Mohd Waseem Naikoo & Abu Reza Towfiqul Islam & Atiqur Rahman & Amir Mosavi, 2022. "Developing Robust Flood Susceptibility Model with Small Numbers of Parameters in Highly Fertile Regions of Northwest Bangladesh for Sustainable Flood and Agriculture Management," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    14. Shiny Abraham & Chau Huynh & Huy Vu, 2019. "Classification of Soils into Hydrologic Groups Using Machine Learning," Data, MDPI, vol. 5(1), pages 1-14, December.
    15. Fuat Kaya & Gaurav Mishra & Rosa Francaviglia & Ali Keshavarzi, 2023. "Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity," Land, MDPI, vol. 12(4), pages 1-20, April.
    16. Sumin Park & Haemi Park & Jungho Im & Cheolhee Yoo & Jinyoung Rhee & Byungdoo Lee & ChunGeun Kwon, 2019. "Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-23, October.
    17. Nausheen Mazhar & Safdar Ali Shirazi, 2023. "Community perceptions of the impacts of desertification as related to adaptive capacity in drylands of South Punjab, Pakistan," Asia-Pacific Journal of Regional Science, Springer, vol. 7(2), pages 549-568, June.
    18. Clement Nyamekye & Michael Thiel & Sarah Schönbrodt-Stitt & Benewinde J.-B. Zoungrana & Leonard K. Amekudzi, 2018. "Soil and Water Conservation in Burkina Faso, West Africa," Sustainability, MDPI, vol. 10(9), pages 1-24, September.
    19. Sawadogo, Alidou & Dossou-Yovo, Elliott R. & Kouadio, Louis & Zwart, Sander J. & Traoré, Farid & Gündoğdu, Kemal S., 2023. "Assessing the biophysical factors affecting irrigation performance in rice cultivation using remote sensing derived information," Agricultural Water Management, Elsevier, vol. 278(C).
    20. Zinhle Mashaba-Munghemezulu & George Johannes Chirima & Cilence Munghemezulu, 2021. "Modeling the Spatial Distribution of Soil Nitrogen Content at Smallholder Maize Farms Using Machine Learning Regression and Sentinel-2 Data," Sustainability, MDPI, vol. 13(21), pages 1-21, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13930-:d:704212. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.