Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index
The spectral vegetation indices derived from remote sensing data provide a detailed spectral analysis for assessing vegetation characteristics. This study investigated the relationship between cotton yield and canopy spectral indices to develop yield estimation models. Spectral reflectance data were...
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2025-01-01
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| author | Yaqin Qi Xi Chen Zhengchao Chen Xin Zhang Congju Shen Yan Chen Yuanying Peng Bing Chen Qiong Wang Taijie Liu Hao Zhang |
| author_facet | Yaqin Qi Xi Chen Zhengchao Chen Xin Zhang Congju Shen Yan Chen Yuanying Peng Bing Chen Qiong Wang Taijie Liu Hao Zhang |
| author_sort | Yaqin Qi |
| collection | DOAJ |
| description | The spectral vegetation indices derived from remote sensing data provide a detailed spectral analysis for assessing vegetation characteristics. This study investigated the relationship between cotton yield and canopy spectral indices to develop yield estimation models. Spectral reflectance data were collected at various growth stages using an ASD FieldSpec Pro VNIR 2500 spectrometer. Six prediction models were developed using spectral vegetation indices, including the Normalized Difference Vegetation Index (<i>NDVI</i>) and Ratio Vegetation Index (<i>RVI</i>), to estimate the Leaf Area Index (<i>LAI</i>) and above-ground biomass. For <i>LAI</i> estimation using the <i>NDVI</i>, the power function model (<i>y = 10.083x<sup>11.298</sup></i>) demonstrated higher precision, with a multiple correlation coefficient of <i>R</i><sup>2</sup> = 0.8184 and the smallest root mean square error (<i>RMSE</i> = 0.3613). These results confirm the strong predictive capacity of <i>NDVI</i> for <i>LAI</i>, with the power function model offering the best estimation accuracy. In estimating above-ground biomass using <i>RVI</i>, the power function model of <i>y = 6.5218x<sup>1.33917</sup></i> achieved the higher correlation (<i>R</i><sup>2</sup> = 0.8851) for fresh biomass with an <i>RMSE</i> of 0.1033, making it the most accurate. For dry biomass, the exponential function model (<i>y = 9.1565 × 10<sup>−5</sup>∙exp(1.1146x)</i>) was the most precise, achieving an <i>R</i><sup>2</sup> value of 0.8456 and the lowest <i>RMSE</i> value of 0.0076. These findings highlight the potential of spectral remote sensing for accurately predicting cotton canopy structural parameters and biomass weights. By integrating spectral analysis techniques with remote sensing, this research offers valuable insights for precision cotton planting and field management, enabling optimized agricultural practices and enhanced vegetation health monitoring. |
| format | Article |
| id | doaj-art-d90c8d701b454124a22118a5d0006d88 |
| institution | Kabale University |
| issn | 2075-1729 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-d90c8d701b454124a22118a5d0006d882025-01-24T13:38:37ZengMDPI AGLife2075-17292025-01-011516210.3390/life15010062Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation IndexYaqin Qi0Xi Chen1Zhengchao Chen2Xin Zhang3Congju Shen4Yan Chen5Yuanying Peng6Bing Chen7Qiong Wang8Taijie Liu9Hao Zhang10Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaResearch Institute, Xinjiang Academy Agricultural and Reclamation Science, Shihezi 832003, ChinaResearch Institute, Xinjiang Academy Agricultural and Reclamation Science, Shihezi 832003, ChinaThe Oasis Key Laboratory of Ecological Agriculture of Xinjiang, Shihezi University, Shihezi 832003, ChinaCollege of Arts and Sciences, Lewis University, Romeoville, IL 60446, USAResearch Institute, Xinjiang Academy Agricultural and Reclamation Science, Shihezi 832003, ChinaResearch Institute, Xinjiang Academy Agricultural and Reclamation Science, Shihezi 832003, ChinaResearch Institute, Xinjiang Academy Agricultural and Reclamation Science, Shihezi 832003, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaThe spectral vegetation indices derived from remote sensing data provide a detailed spectral analysis for assessing vegetation characteristics. This study investigated the relationship between cotton yield and canopy spectral indices to develop yield estimation models. Spectral reflectance data were collected at various growth stages using an ASD FieldSpec Pro VNIR 2500 spectrometer. Six prediction models were developed using spectral vegetation indices, including the Normalized Difference Vegetation Index (<i>NDVI</i>) and Ratio Vegetation Index (<i>RVI</i>), to estimate the Leaf Area Index (<i>LAI</i>) and above-ground biomass. For <i>LAI</i> estimation using the <i>NDVI</i>, the power function model (<i>y = 10.083x<sup>11.298</sup></i>) demonstrated higher precision, with a multiple correlation coefficient of <i>R</i><sup>2</sup> = 0.8184 and the smallest root mean square error (<i>RMSE</i> = 0.3613). These results confirm the strong predictive capacity of <i>NDVI</i> for <i>LAI</i>, with the power function model offering the best estimation accuracy. In estimating above-ground biomass using <i>RVI</i>, the power function model of <i>y = 6.5218x<sup>1.33917</sup></i> achieved the higher correlation (<i>R</i><sup>2</sup> = 0.8851) for fresh biomass with an <i>RMSE</i> of 0.1033, making it the most accurate. For dry biomass, the exponential function model (<i>y = 9.1565 × 10<sup>−5</sup>∙exp(1.1146x)</i>) was the most precise, achieving an <i>R</i><sup>2</sup> value of 0.8456 and the lowest <i>RMSE</i> value of 0.0076. These findings highlight the potential of spectral remote sensing for accurately predicting cotton canopy structural parameters and biomass weights. By integrating spectral analysis techniques with remote sensing, this research offers valuable insights for precision cotton planting and field management, enabling optimized agricultural practices and enhanced vegetation health monitoring.https://www.mdpi.com/2075-1729/15/1/62spectral vegetation indexcotton canopy informationestimation model |
| spellingShingle | Yaqin Qi Xi Chen Zhengchao Chen Xin Zhang Congju Shen Yan Chen Yuanying Peng Bing Chen Qiong Wang Taijie Liu Hao Zhang Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index Life spectral vegetation index cotton canopy information estimation model |
| title | Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index |
| title_full | Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index |
| title_fullStr | Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index |
| title_full_unstemmed | Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index |
| title_short | Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index |
| title_sort | estimation model for cotton canopy structure parameters based on spectral vegetation index |
| topic | spectral vegetation index cotton canopy information estimation model |
| url | https://www.mdpi.com/2075-1729/15/1/62 |
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