IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i5p1088-d1151183.html
   My bibliography  Save this article

Spatial Prediction and Mapping of Soil Water Content by TPE-GBDT Model in Chinese Coastal Delta Farmland with Sentinel-2 Remote Sensing Data

Author

Listed:
  • Dexi Zhan

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Yongqi Mu

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Wenxu Duan

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Mingzhu Ye

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Yingqiang Song

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Zhenqi Song

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Kaizhong Yao

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Dengkuo Sun

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Ziqi Ding

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

Abstract

Soil water content is an important indicator used to maintain the ecological balance of farmland. The efficient spatial prediction of soil water content is crucial for ensuring crop growth and food production. To this end, 104 farmland soil samples were collected in the Yellow River Delta (YRD) in China, and the soil water content was determined using the drying method. A gradient boosting decision tree (GBDT) model based on a tree-structured Parzen estimator (TPE) hyperparametric optimization was developed, and then the soil water content was predicted and mapped based on the soil texture and vegetation index from Sentinel-2 remote sensing images. The results of statistical analysis showed that the soil water content had a high coefficient of variation (55.30%), a non-normal distribution, and complex spatial variability. Compared with other models, the TPE-GBDT model had the highest prediction accuracy (RMSE = 6.02% and R 2 = 0.71), and its mapping results showed that the areas with high soil water content were distributed on both sides of the river and near the estuary. Furthermore, the results of Shapley additive explanation (SHAP) analysis showed that the soil texture (PC2 and PC5), modified normalized difference vegetation index (MNDVI), and Sentinel-2 red edge position (S2REP) index provided important contributions to the spatial prediction of soil water content. We found that the hydraulic physical properties of soil texture and the vegetation characteristics (such as vegetation coverage, root action, and transpiration) are the key factors affecting the spatial migration and heterogeneity of the soil water content in the study area. The above results show that the TPE algorithm can quickly capture the hyperparameters that are most suitable for the GBDT model, so that the GBDT model can ensure prediction accuracy, reduce the loss function with less training data, and accurately learn of the nonlinear relationship between soil water content and environmental factors. This paper proposes a machine learning method for hyperparameter optimization that shows considerable potential to predict the spatial heterogeneity of soil water content, which can effectively support regional farmland soil and water conservation and high-quality agricultural development.

Suggested Citation

  • Dexi Zhan & Yongqi Mu & Wenxu Duan & Mingzhu Ye & Yingqiang Song & Zhenqi Song & Kaizhong Yao & Dengkuo Sun & Ziqi Ding, 2023. "Spatial Prediction and Mapping of Soil Water Content by TPE-GBDT Model in Chinese Coastal Delta Farmland with Sentinel-2 Remote Sensing Data," Agriculture, MDPI, vol. 13(5), pages 1-19, May.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:1088-:d:1151183
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/5/1088/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/5/1088/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Bingxia & Shao, Ming’an, 2015. "Modeling soil–water dynamics and soil–water carrying capacity for vegetation on the Loess Plateau, China," Agricultural Water Management, Elsevier, vol. 159(C), pages 176-184.
    2. Fan, Junliang & Zheng, Jing & Wu, Lifeng & Zhang, Fucang, 2021. "Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models," Agricultural Water Management, Elsevier, vol. 245(C).
    3. Rockström, Johan & Karlberg, Louise & Wani, Suhas P. & Barron, Jennie & Hatibu, Nuhu & Oweis, Theib & Bruggeman, Adriana & Farahani, Jalali & Qiang, Zhu, 2010. "Managing water in rainfed agriculture--The need for a paradigm shift," Agricultural Water Management, Elsevier, vol. 97(4), pages 543-550, April.
    4. Filgueiras, Roberto & Almeida, Thomé Simpliciano & Mantovani, Everardo Chartuni & Dias, Santos Henrique Brant & Fernandes-Filho, Elpídio Inácio & da Cunha, Fernando França & Venancio, Luan Peroni, 2020. "Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).
    5. Li, Dong & Ma, Changxi, 2022. "Research on lane change prediction model based on GBDT," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    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. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
    2. Zheng, Jing & Fan, Junliang & Zhou, Minghua & Zhang, Fucang & Liao, Zhenqi & Lai, Zhenlin & Yan, Shicheng & Guo, Jinjin & Li, Zhijun & Xiang, Youzhen, 2022. "Ridge-furrow plastic film mulching enhances grain yield and yield stability of rainfed maize by improving resources capture and use efficiency in a semi-humid drought-prone region," Agricultural Water Management, Elsevier, vol. 269(C).
    3. Nesterenko Sergey & Vyatkin Konstantin, 2017. "The study of land management and geographic information support of municipal building in Ukraine," Technology audit and production reserves, 1(33) 2017, Socionet;Technology audit and production reserves, vol. 1(4(33)), pages 24-28.
    4. Islam, AFM Tariqul & Islam, AKM Saiful & Islam, GM Tarekul & Bala, Sujit Kumar & Salehin, Mashfiqus & Choudhury, Apurba Kanti & Dey, Nepal C. & Hossain, Akbar, 2022. "Adaptation strategies to increase water productivity of wheat under changing climate," Agricultural Water Management, Elsevier, vol. 264(C).
    5. Lankford, B. & Makin, Ian & Matthews, N. & McCornick, Peter G. & Noble, A. & Shah, Tushaar, "undated". "A compact to revitalise large-scale irrigation systems using a leadership-partnership-ownership 'Theory of Change'," Papers published in Journals (Open Access) H047459, International Water Management Institute.
    6. Festo Richard Silungwe & Frieder Graef & Sonoko Dorothea Bellingrath-Kimura & Emmanuel A Chilagane & Siza Donald Tumbo & Fredrick Cassian Kahimba & Marcos Alberto Lana, 2019. "Modelling Rainfed Pearl Millet Yield Sensitivity to Abiotic Stresses in Semi-Arid Central Tanzania, Eastern Africa," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    7. Getnet, Kindie & MacAlister, Charlotte, 2012. "Integrated innovations and recommendation domains: Paradigm for developing, scaling-out, and targeting rainwater management innovations," Ecological Economics, Elsevier, vol. 76(C), pages 34-41.
    8. Feng Huang & Baoguo Li, 2020. "What is the Redline Water Withdrawal for Crop Production in China?—Projection to 2030 Derived from the Past Twenty-Year Trajectory," Sustainability, MDPI, vol. 12(10), pages 1-14, May.
    9. Hanjra, Munir A. & Qureshi, M. Ejaz, 2010. "Global water crisis and future food security in an era of climate change," Food Policy, Elsevier, vol. 35(5), pages 365-377, October.
    10. Khushbu Kumari & Raushan Kumar & Nirmali Bordoloi & Tatiana Minkina & Chetan Keswani & Kuldeep Bauddh, 2023. "Unravelling the Recent Developments in the Production Technology and Efficient Applications of Biochar for Agro-Ecosystems," Agriculture, MDPI, vol. 13(3), pages 1-26, February.
    11. Datta, Nirupam, 2015. "Evaluating Impacts of Watershed Development Program on Agricultural Productivity, Income, and Livelihood in Bhalki Watershed of Bardhaman District, West Bengal," World Development, Elsevier, vol. 66(C), pages 443-456.
    12. Krauß, Michael & Kraatz, Simone & Drastig, Katrin & Prochnow, Annette, 2015. "The influence of dairy management strategies on water productivity of milk production," Agricultural Water Management, Elsevier, vol. 147(C), pages 175-186.
    13. Dereje Mengistie & Desale Kidane, 2016. "Assessment of the Impact of Small-Scale Irrigation on Household Livelihood Improvement at Gubalafto District, North Wollo, Ethiopia," Agriculture, MDPI, vol. 6(3), pages 1-22, June.
    14. María Blanco & Benjamin Van Doorslaer & Wolfgang Britz & Heinz-Peter Witzke, 2012. "Exploring the feasibility of integrating water issues into the CAPRI model," JRC Research Reports JRC77058, Joint Research Centre.
    15. Ndung’u, M. & Mugwe, J.N. & Mucheru-Muna, M.W. & Ngetich, F.K. & Mairura, F.S. & Mugendi, D.N., 2023. "Tied-ridging and soil inputs enhance small-scale maize productivity and profitability under erratic rainfall conditions in central Kenya," Agricultural Water Management, Elsevier, vol. 286(C).
    16. Elke Noellemeyer & Romina Fernández & Alberto Quiroga, 2013. "Crop and Tillage Effects on Water Productivity of Dryland Agriculture in Argentina," Agriculture, MDPI, vol. 3(1), pages 1-11, January.
    17. Nouri, Milad & Homaee, Mehdi & Pereira, Luis S. & Bybordi, Mohammad, 2023. "Water management dilemma in the agricultural sector of Iran: A review focusing on water governance," Agricultural Water Management, Elsevier, vol. 288(C).
    18. Yan, Haofang & Li, Mi & Zhang, Chuan & Zhang, Jianyun & Wang, Guoqing & Yu, Jianjun & Ma, Jiamin & Zhao, Shuang, 2022. "Comparison of evapotranspiration upscaling methods from instantaneous to daytime scale for tea and wheat in southeast China," Agricultural Water Management, Elsevier, vol. 264(C).
    19. Marta Antonelli & Martina Sartori, 2014. "Unfolding the Potential of the Virtual Water Concept. What is still under debate?," IEFE Working Papers 74, IEFE, Center for Research on Energy and Environmental Economics and Policy, Universita' Bocconi, Milano, Italy.
    20. Cheng, Minghui & Wang, Haidong & Fan, Junliang & Zhang, Shaohui & Wang, Yanli & Li, Yuepeng & Sun, Xin & Yang, Ling & Zhang, Fucang, 2021. "Water productivity and seed cotton yield in response to deficit irrigation: A global meta-analysis," Agricultural Water Management, Elsevier, vol. 255(C).

    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:jagris:v:13:y:2023:i:5:p:1088-:d:1151183. 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.