| Title | Estimation of Plant Water Content of Agricultural Canopies Using Hyperspectral Remote Sensing |
| Download | Downloads (Preprint) |
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| Licence | Please note the adoption of the Open Government Licence - Canada
supersedes any previous licences. |
| Author | Champagne, C; Staenz, K; Bannari, A; White, H P ; Deguise, J -C; McNairn, H |
| Source | 1st International Symposium on Recent Advances in Quantitative Remote Sensing, Torrent, Valencia (Spain), 16-20 September; 2002., https://doi.org/10.4095/219955  Open Access |
| Year | 2002 |
| Alt Series | Earth Sciences Sector, Contribution Series 20043153 |
| Document | book |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Released | 2002 01 01 |
| Abstract | Hyperspectral models developed to estimate plant water content have had limited application under field conditions and have not been rigorously validated. A physical model using a spectrum matching
technique was applied to hyperspectral data to directly calculate the canopy equivalent water thickness (EWT) using a look-up table approach. The objective of this study was to test the validity of this algorithm using plant water content information
collected under field conditions, and to relate this to the needs of precision agriculture. Image data were acquired over two experimental test sites in Canada, near Clinton, Ontario and Indian Head, Saskatchewan, using the Probe-1 airborne
hyperspectral sensor. Plant biomass samples were collected simultaneously from plots spanning fourteen fields of various crop types (wheat, canola, corn, beans and peas). The model was validated against EWT estimated from biomass samples. The model
predicts EWT in the range found with all crop types pooled together, a root mean squared error (RMSE) of 26.8 % of the average. The model was sensitive to within-crop variability for broad leaf crops such as peas, corn, and beans (RMSE = 24.4%, 12.0,
21.8%, respectively).The RMSE for canola was relatively high (39.9%) as a result of a poor prediction at low water contents. The model proved a poor predictor of EWT in wheat (RMSE = 69.9%). EWT is related to plant biomass and leaf area index (LAI).
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| GEOSCAN ID | 219955 |
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