Name
Title | ||
|---|---|---|
Integrating an attention-based deep learning framework and the SAFY-V model for winter wheat yield estimation using time series SAR and optical data |
Abstract | ||
|---|---|---|
•Few previous studies have explored the estimation of LAI using SAR and optical data with large differences in spatial resolution.•The UpSc-AConvGRU model is proposed to improve the LAI estimation accuracy and fill gaps in the optical image time series.•Time-series estimated LAIs and VTCIs are used as inputs of the SAFY-V model for yield estimation.•The yield estimation accuracies are validated at irrigated and rain-fed farmlands, also under different levels of disease.•The proposed approach has low calculation cost and satisfactory yield estimation accuracy at the regional scale. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.compag.2022.107334 | Computers and Electronics in Agriculture |
Keywords | DocType | Volume |
Sentinel-1 and -3,Leaf area index,Deep learning,SAFY-V model,Yield estimation | Journal | 201 |
ISSN | Citations | PageRank |
0168-1699 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Dong | 1 | 138 | 14.22 |
| Peng Xin Wang | 2 | 14 | 6.53 |
| Kevin Tansey | 3 | 0 | 0.34 |
| Junming Liu | 4 | 0 | 0.68 |
| Yue Zhang | 5 | 3 | 2.90 |
| Huiren Tian | 6 | 0 | 0.34 |
| Shuyu Zhang | 7 | 1 | 0.75 |