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Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

Author

Listed:
  • Wu Xiao

    (Department of Land Management, Zhejiang University, Hangzhou 310058, China)

  • Wenqi Chen

    (Department of Land Management, Zhejiang University, Hangzhou 310058, China)

  • Tingting He

    (Institute of Land Reclamation and Ecological Restoration, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Linlin Ruan

    (Department of Land Management, Zhejiang University, Hangzhou 310058, China)

  • Jiwang Guo

    (Department of Land Management, Zhejiang University, Hangzhou 310058, China)

Abstract

Nitrogen plays an important role in improving soil productivity and maintaining ecosystem stability. Mapping and monitoring the soil total nitrogen (STN) content is the basis for modern soil management. The Google Earth Engine (GEE) platform covers a wide range of available satellite remote sensing datasets and can process massive data calculations. We collected 6823 soil samples in Shandong Province, China. The random forest (RF) algorithm predicted the STN content in croplands from 2002 to 2016 in Shandong Province, China on the GEE platform. Our results showed that RF had the coefficient of determination (R 2 ) (0.57), which can predict the spatial distribution of the STN and analyze the trend of STN changes. The remote sensing spectral reflectance is more important in model building according to the variable importance analysis. From 2002 to 2016, the STN content of cropland in the province had an upward trend of 35.6%, which increased before 2010 and then decreased slightly. The GEE platform provides an opportunity to map dynamic changes of the STN content effectively, which can be used to evaluate soil properties in the future long-term agricultural management.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10274-:d:459089
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    Cited by:

    1. Zunfang Liu & Haochuan Lei & Lei Lei & Haiyan Sheng, 2022. "Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning," Sustainability, MDPI, vol. 14(19), pages 1-14, September.
    2. Li, Gensheng & Hu, Zhenqi & Li, Pengyu & Yuan, Dongzhu & Wang, Wenjuan & Yang, Kun, 2021. "The optimal framework and model to balance underground coal mining and cropland protection in Jining, eastern China," Resources Policy, Elsevier, vol. 74(C).

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