Novel approach for computing photosynthetically active radiation for productivity modeling using remotely sensed images in the Great Plains, United States
Journal of Applied Remote Sensing
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Abstract
Gross primary production (GPP) is a key indicator of ecosystem performance, and helps in many decision-making processes related to environment. We used the Eddy covariancelight use efficiency (EC-LUE) model for estimating GPP in the Great Plains, United States in order to evaluate the performance of this model. We developed a novel algorithm for computing the photosynthetically active radiation (PAR) based on net radiation. A strong correlation (R2=0.94,N=24) was found between daily PAR and Landsat-based mid-day instantaneous net radiation. Though the Moderate Resolution Spectroradiometer (MODIS) based instantaneous net radiation was in better agreement (R2=0.98,N=24) with the daily measured PAR, there was no statistical significant difference between Landsat based PAR and MODIS based PAR. The EC-LUE model validation also confirms the need to consider biological attributes (C3 versus C4 plants) for potential light use efficiency. A universal potential light use efficiency is unable to capture the spatial variation of GPP. It is necessary to use C3 versus C4 based land use/land cover map for using EC-LUE model for estimating spatiotemporal distribution of GPP.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Novel approach for computing photosynthetically active radiation for productivity modeling using remotely sensed images in the Great Plains, United States |
| Series title | Journal of Applied Remote Sensing |
| DOI | 10.1117/1.JRS.6.063522 |
| Volume | 6 |
| Issue | 1 |
| Year Published | 2012 |
| Language | English |
| Publisher | SPIE |
| Publisher location | Bellingham, WA |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center |
| Description | 063522 |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Journal of Applied Remote Sensing |
| Country | United States |
| Google Analytic Metrics | Metrics page |