Name
Title | ||
|---|---|---|
Assessment of Leaf Chlorophyll Content Models for Winter Wheat Using Landsat-8 Multispectral Remote Sensing Data |
Abstract | ||
|---|---|---|
The leaf chlorophyll content (LCC) is a critical index to characterize crop growth conditions, photosynthetic capacity, and physiological status. Its dynamic change characteristics are of great significance for monitoring crop growth conditions and understanding the process of material and energy exchange between crops and the environment. Extensive research has focused on LCC retrieval with hyperspectral data onboard various sensor platforms. Nevertheless, limited attention has been paid to LCC inversion from multispectral data, such as the data from Landsat-8, and the potentials and capabilities of the data for crop LCC estimation have not been fully explored. The present study made use of Landsat-8 Operational Land Imager (OLI) imagery and the corresponding field experimental data to evaluate their capabilities and potentials for LCC modeling using four different retrieval methods: vegetation indices (VIs), machine learning regression algorithms (MLRAs), lookup-table (LUT)-based inversion, and hybrid regression approaches. The results showed that the modified triangular vegetation index (MTVI2) exhibited the best estimate accuracy for LCC retrieval with a root mean square error (RMSE) of 5.99 mu g/cm(2)and a relative RMSE (RRMSE) of 10.49%. Several other vegetation indices that were established from red and near-infrared (NIR) bands also exhibited good accuracy. Models established from Gaussian process regression (GPR) achieved the highest accuracy for LCC retrieval (RMSE = 5.50 mu g/cm(2), RRMSE = 9.62%) compared with other MLRAs. Moreover, red and NIR bands outweighed other bands in terms of GPR modelling. LUT-based inversion methods with the "K(x) = -log (x) + x" cost function that belongs to the "minimum contrast estimates" family showed the best estimation results (RMSE = 8.08 mu g/cm(2), RRMSE = 14.14%), and the addition of multiple solution regularization strategies effectively improved the inversion accuracy. For hybrid regression methods, the use of active learning (AL) techniques together with GPR for LCC modelling significantly increased the estimation accuracy, and the combination of entropy query by bagging (EQB) AL and GPR had the best accuracy for LCC estimation (RMSE = 12.43 mu g/cm(2), RRMSE = 21.77%). Overall, our study suggest that Landsat-8 OLI data are suitable for crop LCC retrieval and could provide a basis for LCC estimation with similar multispectral datasets. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/rs12162574 | REMOTE SENSING |
Keywords | DocType | Volume |
leaf chlorophyll content,Landsat-8,vegetation index,machine learning,lookup table-based inversion,hybrid regression | Journal | 12 |
Issue | Citations | PageRank |
16 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xian-Feng Zhou | 1 | 54 | 9.35 |
| Jingcheng Zhang | 2 | 45 | 10.39 |
| Dongmei Chen | 3 | 0 | 0.34 |
| Yanbo Huang | 4 | 50 | 8.86 |
| Weiping Kong | 5 | 2 | 2.14 |
| Lin Yuan | 6 | 14 | 3.03 |
| Huichun Ye | 7 | 13 | 9.18 |
| Wenjiang Huang | 8 | 179 | 51.84 |