| Hyperspectral estimation model for chlorophyll content of rice canopy |
|
View Fulltext View/Add Comment Download reader |
| |
| DOI：10.7606/j.issn.1000-7601.2019.03.31 |
| Key Words: hyperspectral remote sensing estimation model random forest algorithm rice canopy chlorophyll content vegetation index |
| Author Name | Affiliation | | WU Xu-mei | College of Natural and Environment, Northwest N&F University, Yangling, Shaanxi 712100, China | | CHANG Qing-rui | College of Natural and Environment, Northwest N&F University, Yangling, Shaanxi 712100, China | | LUO Li-li | College of Natural and Environment, Northwest N&F University, Yangling, Shaanxi 712100, China | | YOU Ming-ming | College of Natural and Environment, Northwest N&F University, Yangling, Shaanxi 712100, China |
|
| Hits: 870 |
| Download times: 454 |
| Abstract: |
| In order to establish a high-precision model to estimate rice canopy chlorophyll content in Northwest Yellow River Irrigation Area, rice canopy SPAD and hyperspectral data were measured in a field experiment. In this paper, a series of Ratio Vegetation Index(RVI), Difference Vegetation Index(DVI), Normalized Difference Vegetation Index (NDVI), and Soil-Adjust Vegetation Index (SAVI) were computed by the combination of original canopy spectra. The optimal vegetation indexes that sensitively reflected the rice canopy SPAD were screened out. The estimation models of rice canopy SPAD in Northwest Yellow River Irrigation Area were established by using ordinary regression analysis method and random forest algorithm. The results showed that: (1) Using the general regression analysis method, the exponential model established with RVI (D1316, D736) as the independent variable was the best single-variable model to estimate rice canopy SPAD in the study area. (2) Compared with the normal regression models, the random forest model established using four vegetation indices RVI (R696, R540), DVI (R700, R536), SAVI (R700, R536), and RVI (D1316, D736) had the best prediction accuracy. The validation results showed that R2 was 0.873, RMSE was 3.221, and RE was 13.25%. Therefore, we concluded that the random forest model can be used for the rapid and lossless estimation of rice canopy chlorophyll content in Northwest Yellow River Irrigation Area. |
|
|
|