Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image
With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previ...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/19/3874 |
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| author | Xu Ma Lei Lu Jianli Ding Fei Zhang Baozhong He |
| author_facet | Xu Ma Lei Lu Jianli Ding Fei Zhang Baozhong He |
| author_sort | Xu Ma |
| collection | DOAJ |
| description | With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images. |
| first_indexed | 2024-03-10T06:52:47Z |
| format | Article |
| id | doaj.art-255478aa3b7043e295a4110b4f3c0862 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:52:47Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-255478aa3b7043e295a4110b4f3c08622023-11-22T16:42:08ZengMDPI AGRemote Sensing2072-42922021-09-011319387410.3390/rs13193874Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution ImageXu Ma0Lei Lu1Jianli Ding2Fei Zhang3Baozhong He4Key Laboratory of Oasis Ecology of Ministry of Education, Postdoctoral Mobile Station, College of Resource and Environment Sciences, Xingjiang University, Urumqi 830064, ChinaCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, ChinaKey Laboratory of Oasis Ecology of Ministry of Education, College of Resource and Environment Sciences, Xingjiang University, Urumqi 830064, ChinaKey Laboratory of Oasis Ecology of Ministry of Education, College of Resource and Environment Sciences, Xingjiang University, Urumqi 830064, ChinaKey Laboratory of Oasis Ecology of Ministry of Education, College of Resource and Environment Sciences, Xingjiang University, Urumqi 830064, ChinaWith high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.https://www.mdpi.com/2072-4292/13/19/3874high spatial resolution imagefractional vegetation coverrow cropsneural networks |
| spellingShingle | Xu Ma Lei Lu Jianli Ding Fei Zhang Baozhong He Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image Remote Sensing high spatial resolution image fractional vegetation cover row crops neural networks |
| title | Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image |
| title_full | Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image |
| title_fullStr | Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image |
| title_full_unstemmed | Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image |
| title_short | Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image |
| title_sort | estimating fractional vegetation cover of row crops from high spatial resolution image |
| topic | high spatial resolution image fractional vegetation cover row crops neural networks |
| url | https://www.mdpi.com/2072-4292/13/19/3874 |
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