Name
Title | ||
|---|---|---|
Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data. |
Abstract | ||
|---|---|---|
In this study, 27 polarimetric parameters were extracted from Radarsat-2 polarimetric synthetic aperture radar (SAR) at each growth stage of the rape crop. The sensitivity to growth parameters such as stem height, leaf area index (LAI), and biomass were investigated as a function of days after sowing. Based on the sensitivity analysis, five empirical regression models were compared to determine the best model for stem height, LAI, and biomass inversion. Of these five models, quadratic models had higher R-2 values than other models in most cases of growth parameter inversions, but when these results were related to physical scattering mechanisms, the inversion results produced overestimation in the performance of some parameters. By contrast, linear and logarithmic models, which had lower R-2 values than the quadratic models, had stable performance for growth parameter inversions, particularly in terms of their performance at each growth stage. The best biomass inversion performance was acquired by the volume component of a quadratic model, with an R-2 value of 0.854 and root mean square error (RMSE) of 109.93 g m(-2). The best LAI inversion was also acquired by a quadratic model, but used the radar vegetation index (Cloude), with an R-2 value of 0.8706 and RMSE of 0.56 m(2) m(-2). Stem height was acquired by scattering angle alphausing a logarithmic model, with an R-2 of 0.926 value and RMSE of 11.09 cm. The performances of these models were also analysed for biomass estimation at the second growth stage (P2), third growth stage (P3), and fourth growth stage (P4). The results showed that the models built at the P3 stage had better substitutability with the models built during all of the growth stages. From the mapping results, we conclude that a model built at the P3 stage can be used for rape biomass inversion, with 90% of estimation errors being less than 100 g m(-2). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/rs10020206 | REMOTE SENSING |
Keywords | Field | DocType |
rape (Brassica napus L.),monitoring,biomass,stem height,LAI,empirical regression model,inversion | Radar,Leaf area index,Time series,Inversion (meteorology),Regression analysis,Remote sensing,Mean squared error,Quadratic equation,Logarithm,Geology,Statistics | Journal |
Volume | Issue | ISSN |
10 | 2 | 2072-4292 |
Citations | PageRank | References |
1 | 0.38 | 14 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wangfei Zhang | 1 | 1 | 1.39 |
| Erxue Chen | 2 | 2 | 3.45 |
| Zengyuan Li | 3 | 58 | 25.14 |
| Lei Zhao | 4 | 19 | 4.83 |
| Yongjie Ji | 5 | 4 | 1.49 |
| Yahong Zhang | 6 | 1 | 0.38 |
| Zhi-qin Liu | 7 | 12 | 4.93 |