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Production Capacity Evaluation of Farmland Using Long Time Series of Remote Sensing Images

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  • Mei Lu

    (School of Land Engineering, Chang’an University, Xi’an 710061, China
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Xiaohe Gu

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Qian Sun

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Xu Li

    (College of Surveying Mapping and Spatial Information, Shandong University of Science and Technology, Qingdao 266590, China)

  • Tianen Chen

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Yuchun Pan

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

Abstract

Farmland is a crucial resource for the survival and evolution of humans. The accurate evaluation of farmland production capacity (FPC) is of great significance for planting structure optimization, the improvement of low-yield farmland and sustainable utilization. The objective of this study is to quantitatively evaluate the FPC at the county scale using time series remote sensing (RS) images. Taking winter wheat as a benchmark crop, the relations between annual yield and the Normalized Difference Vegetation Index (NDVI) were established by a multiple linear regression algorithm. The mean and standard deviations (SD) of the multi-year yield of winter wheat were used to evaluate FPC and its instability using the farmland parcels as the basic unit. The results show that the estimation model for annual winter wheat yield performed best in 2011. The R2 of the modeling sample was 0.93, and the RMSE of the testing sample was 368.1 kg/ha. The FPC grades in the south and north of the study area were relatively high with a good stability, while those in the center were low with poor stability. There was a certain correlation between FPC and soil organic matter (SOM), and the correlation coefficient was 0.525 ( p < 0.01). In this study, taking the farmland parcel as a basic unit instead of a pixel, long time series of multi-source RS images with medium resolution were used to monitor the per unit yield of benchmark crops and then evaluate the FPC. This can provide a method for the rapid evaluation of FPC at the county scale.

Suggested Citation

  • Mei Lu & Xiaohe Gu & Qian Sun & Xu Li & Tianen Chen & Yuchun Pan, 2022. "Production Capacity Evaluation of Farmland Using Long Time Series of Remote Sensing Images," Agriculture, MDPI, vol. 12(10), pages 1-16, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1619-:d:934346
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    References listed on IDEAS

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    1. Lu, Hua & Xie, Hualin & Lv, Tiangui & Yao, Guanrong, 2019. "Determinants of cultivated land recuperation in ecologically damaged areas in China," Land Use Policy, Elsevier, vol. 81(C), pages 160-166.
    2. Yongzhong Tan & Hang Chen & Kuan Lian & Zhenning Yu, 2020. "Comprehensive Evaluation of Cultivated Land Quality at County Scale: A Case Study of Shengzhou, Zhejiang Province, China," IJERPH, MDPI, vol. 17(4), pages 1-15, February.
    3. Chong Zhao & Yong Zhou & Xigui Li & Pengnan Xiao & Jinhui Jiang, 2018. "Assessment of Cultivated Land Productivity and Its Spatial Differentiation in Dongting Lake Region: A Case Study of Yuanjiang City, Hunan Province," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
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    Cited by:

    1. Quan Xu & Mengting Jin & Peng Guo, 2022. "A High-Precision Crop Classification Method Based on Time-Series UAV Images," Agriculture, MDPI, vol. 13(1), pages 1-18, December.

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