A Method for Estimating Alfalfa (<i>Medicago sativa</i> L.) Forage Yield Based on Remote Sensing Data
Alfalfa (<i>Medicago sativa</i> L.) is a widely planted perennial legume forage plant with excellent quality and high yield. In production, it is very important to determine alfalfa growth dynamics and forage yield in a timely and accurate manner. This study focused on inverse algorithms...
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MDPI AG
2023-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/10/2597 |
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| author | Jingsi Li Ruifeng Wang Mengjie Zhang Xu Wang Yuchun Yan Xinbo Sun Dawei Xu |
| author_facet | Jingsi Li Ruifeng Wang Mengjie Zhang Xu Wang Yuchun Yan Xinbo Sun Dawei Xu |
| author_sort | Jingsi Li |
| collection | DOAJ |
| description | Alfalfa (<i>Medicago sativa</i> L.) is a widely planted perennial legume forage plant with excellent quality and high yield. In production, it is very important to determine alfalfa growth dynamics and forage yield in a timely and accurate manner. This study focused on inverse algorithms for predicting alfalfa forage yield in large-scale alfalfa production. We carried out forage yield and aboveground biomass (AGB) field surveys at different times in 2022. The correlations among the reflectance of different satellite remote sensing bands, vegetation indices, and alfalfa forage yield/AGB were analyzed, additionally the suitable bands and vegetation indices for alfalfa forage yield inversion algorithms were screened, and the performance of the statistical models and machine learning (ML) algorithms for alfalfa forage yield inversion were comparatively analyzed. The results showed that (1) regarding different harvest times, the alfalfa forage yield inversion model for first-harvest alfalfa had relatively large differences in growth, and the simulation accuracy of the alfalfa forage yield inversion model was higher than that for the other harvest times, with the growth of the second- and third-harvest alfalfa being more homogeneous and the simulation accuracy of the forage yield inversion model being relatively low. (2) In the alfalfa forage yield inversion model based on a single parameter, the moisture-related vegetation indices, such as the global vegetation moisture index (GVMI), normalized difference water index (NDWI) and normalized difference infrared index (NDII), had higher coefficients of correlation with alfalfa forage yield/AGB, and the coefficients of correlation <i>R</i><sup>2</sup> values for the first-harvest alfalfa were greater than 0.50, with the NDWI correlation being the best with an <i>R</i><sup>2</sup> value of 0.60. (3) For the alfalfa forage yield inversion model constructed with vegetation indices and band reflectance as multiparameter variables, the random forest (RF) and support vector machine (SVM) simulation accuracy was higher than that of the alfalfa forage yield inversion model based on a single parameter; the first-harvest alfalfa <i>R</i><sup>2</sup> values based on the multiparameter RF and SVM models were both 0.65, the root mean square errors (RMSEs) were 329.74 g/m<sup>2</sup> and 332.32 g/m<sup>2</sup>, and the biases were −0.47 g/m<sup>2</sup> and −2.24 g/m<sup>2</sup>, respectively. The vegetation indices related to plant water content can be considered using a single parameter inversion model for alfalfa forage yield, the vegetation indices and band reflectance can be considered using a multiparameter inversion model for alfalfa forage yield, and ML algorithms are also an optimal choice. The findings in this study can provide technical support for the effective and strategic production management of large-scale alfalfa. |
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| spelling | doaj.art-1be3a579ad144cf0832cefa6683267252023-11-19T15:22:25ZengMDPI AGAgronomy2073-43952023-10-011310259710.3390/agronomy13102597A Method for Estimating Alfalfa (<i>Medicago sativa</i> L.) Forage Yield Based on Remote Sensing DataJingsi Li0Ruifeng Wang1Mengjie Zhang2Xu Wang3Yuchun Yan4Xinbo Sun5Dawei Xu6State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Crop Growth Regulation of Hebei Province, College of Agronomy, Hebei Agricultural University, Baoding 071001, ChinaInner Mongolia Yihe Lvjin Agriculture Develop Co., Ltd., National Center of Technology Innovation for Dairy, Hohhot 010080, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Crop Growth Regulation of Hebei Province, College of Agronomy, Hebei Agricultural University, Baoding 071001, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Key Laboratory of Grassland Resource Monitoring Evaluation and Innovative Utilization, Ministry of Agriculture and Rural Affairs, Hulunber Grassland Ecosystem National Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Key Laboratory of Grassland Resource Monitoring Evaluation and Innovative Utilization, Ministry of Agriculture and Rural Affairs, Hulunber Grassland Ecosystem National Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Crop Growth Regulation of Hebei Province, College of Agronomy, Hebei Agricultural University, Baoding 071001, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Key Laboratory of Grassland Resource Monitoring Evaluation and Innovative Utilization, Ministry of Agriculture and Rural Affairs, Hulunber Grassland Ecosystem National Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAlfalfa (<i>Medicago sativa</i> L.) is a widely planted perennial legume forage plant with excellent quality and high yield. In production, it is very important to determine alfalfa growth dynamics and forage yield in a timely and accurate manner. This study focused on inverse algorithms for predicting alfalfa forage yield in large-scale alfalfa production. We carried out forage yield and aboveground biomass (AGB) field surveys at different times in 2022. The correlations among the reflectance of different satellite remote sensing bands, vegetation indices, and alfalfa forage yield/AGB were analyzed, additionally the suitable bands and vegetation indices for alfalfa forage yield inversion algorithms were screened, and the performance of the statistical models and machine learning (ML) algorithms for alfalfa forage yield inversion were comparatively analyzed. The results showed that (1) regarding different harvest times, the alfalfa forage yield inversion model for first-harvest alfalfa had relatively large differences in growth, and the simulation accuracy of the alfalfa forage yield inversion model was higher than that for the other harvest times, with the growth of the second- and third-harvest alfalfa being more homogeneous and the simulation accuracy of the forage yield inversion model being relatively low. (2) In the alfalfa forage yield inversion model based on a single parameter, the moisture-related vegetation indices, such as the global vegetation moisture index (GVMI), normalized difference water index (NDWI) and normalized difference infrared index (NDII), had higher coefficients of correlation with alfalfa forage yield/AGB, and the coefficients of correlation <i>R</i><sup>2</sup> values for the first-harvest alfalfa were greater than 0.50, with the NDWI correlation being the best with an <i>R</i><sup>2</sup> value of 0.60. (3) For the alfalfa forage yield inversion model constructed with vegetation indices and band reflectance as multiparameter variables, the random forest (RF) and support vector machine (SVM) simulation accuracy was higher than that of the alfalfa forage yield inversion model based on a single parameter; the first-harvest alfalfa <i>R</i><sup>2</sup> values based on the multiparameter RF and SVM models were both 0.65, the root mean square errors (RMSEs) were 329.74 g/m<sup>2</sup> and 332.32 g/m<sup>2</sup>, and the biases were −0.47 g/m<sup>2</sup> and −2.24 g/m<sup>2</sup>, respectively. The vegetation indices related to plant water content can be considered using a single parameter inversion model for alfalfa forage yield, the vegetation indices and band reflectance can be considered using a multiparameter inversion model for alfalfa forage yield, and ML algorithms are also an optimal choice. The findings in this study can provide technical support for the effective and strategic production management of large-scale alfalfa.https://www.mdpi.com/2073-4395/13/10/2597alfalfaforage yieldremote sensingstatistical modelmachine learning |
| spellingShingle | Jingsi Li Ruifeng Wang Mengjie Zhang Xu Wang Yuchun Yan Xinbo Sun Dawei Xu A Method for Estimating Alfalfa (<i>Medicago sativa</i> L.) Forage Yield Based on Remote Sensing Data Agronomy alfalfa forage yield remote sensing statistical model machine learning |
| title | A Method for Estimating Alfalfa (<i>Medicago sativa</i> L.) Forage Yield Based on Remote Sensing Data |
| title_full | A Method for Estimating Alfalfa (<i>Medicago sativa</i> L.) Forage Yield Based on Remote Sensing Data |
| title_fullStr | A Method for Estimating Alfalfa (<i>Medicago sativa</i> L.) Forage Yield Based on Remote Sensing Data |
| title_full_unstemmed | A Method for Estimating Alfalfa (<i>Medicago sativa</i> L.) Forage Yield Based on Remote Sensing Data |
| title_short | A Method for Estimating Alfalfa (<i>Medicago sativa</i> L.) Forage Yield Based on Remote Sensing Data |
| title_sort | method for estimating alfalfa i medicago sativa i l forage yield based on remote sensing data |
| topic | alfalfa forage yield remote sensing statistical model machine learning |
| url | https://www.mdpi.com/2073-4395/13/10/2597 |
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