Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models
Accurate information on the soil moisture content in croplands is essential for monitoring crop growth conditions. This study aimed to enhance soil moisture monitoring by employing laboratory-based soil spectral measurements and radiative transfer models. This study comprised three main components:...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Agriculture Communications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S294979812400036X |
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| author | Jibo Yue Ting Li Haikuan Feng Yuanyuan Fu Yang Liu Jia Tian Hao Yang Guijun Yang |
| author_facet | Jibo Yue Ting Li Haikuan Feng Yuanyuan Fu Yang Liu Jia Tian Hao Yang Guijun Yang |
| author_sort | Jibo Yue |
| collection | DOAJ |
| description | Accurate information on the soil moisture content in croplands is essential for monitoring crop growth conditions. This study aimed to enhance soil moisture monitoring by employing laboratory-based soil spectral measurements and radiative transfer models. This study comprised three main components: (1) Utilizing laboratory-measured soil spectra to investigate the influence of soil moisture content on soil spectral properties (n = 178), and describing the impact of canopy coverage on the mixed spectra of wheat and soil in croplands using a radiative transfer model (RTM) (n = 144, 180); (2) employing a deep learning model trained on extensive simulated datasets to estimate soil moisture beneath the canopy from wheat‒soil mixed spectra (n = 200); and (3) comparing the performance of deep learning model with statistical regression techniques based on soil moisture spectral index (SI) for estimating wheat fractional vegetation cover (FVC) and relative soil moisture content (RMC) under medium to low canopy coverage. The conclusions of this study were as follows: (1) Compared with the conventional statistical regression approaches, the deep learning model exhibited superior accuracy in estimating RMC across all levels of normalized difference vegetation index (NDVI). (2) By combining laboratory soil spectral measurements with an RTM, a pretrained dataset can be created. When combined with transfer learning techniques (FVC: R2 = 0.782, RMSE = 0.107, and RMC: R2 = 0.825, RMSE = 0.130), this approach enhanced the accuracy of estimating wheat FVC and RMC. Future research should expand experiments to include additional regions and crop types to verify the accuracy and generalizability of this method for estimating FVC and RMC under various remote sensing conditions. |
| format | Article |
| id | doaj-art-a4e29f4e52754fa1bfd322c26fbc7e89 |
| institution | Kabale University |
| issn | 2949-7981 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agriculture Communications |
| spelling | doaj-art-a4e29f4e52754fa1bfd322c26fbc7e892024-12-25T04:21:42ZengElsevierAgriculture Communications2949-79812024-12-0124100060Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer modelsJibo Yue0Ting Li1Haikuan Feng2Yuanyuan Fu3Yang Liu4Jia Tian5Hao Yang6Guijun Yang7College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China; Corresponding author. Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Corresponding author. Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.Accurate information on the soil moisture content in croplands is essential for monitoring crop growth conditions. This study aimed to enhance soil moisture monitoring by employing laboratory-based soil spectral measurements and radiative transfer models. This study comprised three main components: (1) Utilizing laboratory-measured soil spectra to investigate the influence of soil moisture content on soil spectral properties (n = 178), and describing the impact of canopy coverage on the mixed spectra of wheat and soil in croplands using a radiative transfer model (RTM) (n = 144, 180); (2) employing a deep learning model trained on extensive simulated datasets to estimate soil moisture beneath the canopy from wheat‒soil mixed spectra (n = 200); and (3) comparing the performance of deep learning model with statistical regression techniques based on soil moisture spectral index (SI) for estimating wheat fractional vegetation cover (FVC) and relative soil moisture content (RMC) under medium to low canopy coverage. The conclusions of this study were as follows: (1) Compared with the conventional statistical regression approaches, the deep learning model exhibited superior accuracy in estimating RMC across all levels of normalized difference vegetation index (NDVI). (2) By combining laboratory soil spectral measurements with an RTM, a pretrained dataset can be created. When combined with transfer learning techniques (FVC: R2 = 0.782, RMSE = 0.107, and RMC: R2 = 0.825, RMSE = 0.130), this approach enhanced the accuracy of estimating wheat FVC and RMC. Future research should expand experiments to include additional regions and crop types to verify the accuracy and generalizability of this method for estimating FVC and RMC under various remote sensing conditions.http://www.sciencedirect.com/science/article/pii/S294979812400036XHyperspectralFVCRMCDeep learning |
| spellingShingle | Jibo Yue Ting Li Haikuan Feng Yuanyuan Fu Yang Liu Jia Tian Hao Yang Guijun Yang Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models Agriculture Communications Hyperspectral FVC RMC Deep learning |
| title | Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models |
| title_full | Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models |
| title_fullStr | Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models |
| title_full_unstemmed | Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models |
| title_short | Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models |
| title_sort | enhancing field soil moisture content monitoring using laboratory based soil spectral measurements and radiative transfer models |
| topic | Hyperspectral FVC RMC Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S294979812400036X |
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