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Combing transfer learning with the OPtical TRApezoid Model (OPTRAM) to diagnosis small-scale field soil moisture from hyperspectral data

Author

Listed:
  • Du, Ruiqi
  • Xiang, Youzhen
  • Zhang, Fucang
  • Chen, Junying
  • Shi, Hongzhao
  • Liu, Hao
  • Yang, Xiaofei
  • Yang, Ning
  • Yang, Xizhen
  • Wang, Tianyang
  • Wu, Yuxiao

Abstract

Accurate, timely, and continuous soil moisture information is helpful for crop stress diagnosis and irrigation management decision. OPtical TRApezoid Model (OPTRAM) based on optical satellite data has been proven to be an effective method for assessing soil moisture status. However, the applicability of OPTRAM to small-scale field soil moisture assessment remains to be explored. In this study, we propose a strategy for the genetically parameterized OPTRAM and evaluate its applicability on Unmanned Aerial Vehicle (UAV) high-resolution hyspectral data. The results showed that: (1) When OPTRAM was used to genetically parameterized with PROSAIL generated dataset, 46 characteristic narrowband bands (|R|= 0.52–0.78) were determined in the spectral region of near infrared (NIR) (750–850 nm) and SWIR (1060–1080 and 1450–1500 nm); (2) By fine-tuned soil moisture estimation model using transfer learning strategy, the reliable soil moisture estimation was achieved in three crops (R2=0.57–0.64; RMSE=0.008–0.022 m3m−3);(3) Compared to soil moisture estimation model using a single spectral region (NIR or SWIR), the DSWC model that combine NIR and SWIR was more effective for tracking soil moisture; (4) The scale effect was observed when the fine-tuned soil moisture estimation model was applied on the high-resolution UAV images. The model performance was stable in pixel size of 1–7 cm and began to drop at pixel size of 11 cm. The above results advance the application of OPTRAM on small farmland soil moisture assessment and demonstrate the application potential of OPTRAM on narrow-band hyperspectral data. This study provides a new candidate for the use of hyperspectral data to estimate soil moisture, and scientific support for precision agriculture and irrigation scheduling.

Suggested Citation

  • Du, Ruiqi & Xiang, Youzhen & Zhang, Fucang & Chen, Junying & Shi, Hongzhao & Liu, Hao & Yang, Xiaofei & Yang, Ning & Yang, Xizhen & Wang, Tianyang & Wu, Yuxiao, 2024. "Combing transfer learning with the OPtical TRApezoid Model (OPTRAM) to diagnosis small-scale field soil moisture from hyperspectral data," Agricultural Water Management, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:agiwat:v:298:y:2024:i:c:s0378377424001914
    DOI: 10.1016/j.agwat.2024.108856
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    References listed on IDEAS

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    1. Ren, Shoujia & Guo, Bin & Wang, Zhijun & Wang, Juan & Fang, Quanxiao & Wang, Jianlin, 2022. "Optimized spectral index models for accurately retrieving soil moisture (SM) of winter wheat under water stress," Agricultural Water Management, Elsevier, vol. 261(C).
    2. El-Hendawy, Salah E. & Al-Suhaibani, Nasser A. & Elsayed, Salah & Hassan, Wael M. & Dewir, Yaser Hassan & Refay, Yahya & Abdella, Kamel A., 2019. "Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates," Agricultural Water Management, Elsevier, vol. 217(C), pages 356-373.
    3. Amin, M.G. Mostofa & Mahbub, S.M. Mubtasim & Hasan, Md. Moudud & Pervin, Wafa & Sharmin, Jinat & Hossain, Md. Delwar, 2023. "Plant–water relations in subtropical maize fields under mulching and organic fertilization," Agricultural Water Management, Elsevier, vol. 286(C).
    4. Ma, Chunfeng & Johansen, Kasper & McCabe, Matthew F., 2022. "Combining Sentinel-2 data with an optical-trapezoid approach to infer within-field soil moisture variability and monitor agricultural production stages," Agricultural Water Management, Elsevier, vol. 274(C).
    5. Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
    6. Grados, D. & Reynarfaje, X. & Schrevens, E., 2020. "A methodological approach to assess canopy NDVI–based tomato dynamics under irrigation treatments," Agricultural Water Management, Elsevier, vol. 240(C).
    7. Uniyal, Bhumika & Dietrich, Jörg & Vasilakos, Christos & Tzoraki, Ourania, 2017. "Evaluation of SWAT simulated soil moisture at catchment scale by field measurements and Landsat derived indices," Agricultural Water Management, Elsevier, vol. 193(C), pages 55-70.
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