Empirical Regression Models for Estimating Multiyear Leaf Area Index of Rice from Several Vegetation Indices at the Field Scale
<p>Simulation results of TIPS behavior with increasing values of LAI on a paddy field, as derived from FLiES. The panels show the results with diffuse radiations of (<b>a</b>) 0%, (<b>b</b>) 50%, and (<b>c</b>) 100%, respectively.</p> ">
<p>Relationship between LAI and each vegetation index in 2008 and 2009. (<b>a</b>) NDVI; (<b>b</b>) EVI; (<b>c</b>) SAVI; (<b>d</b>) OSAVI; (<b>e</b>) MTVI2; (<b>f</b>) WDRVI; (<b>g</b>) SR; (<b>h</b>) TIPS.</p> ">
<p>Comparison of results for actual LAI and estimated LAI. (<b>a</b>) NDVI; (<b>b</b>) EVI; (<b>c</b>) SAVI; (<b>d</b>) OSAVI; (<b>e</b>) MTVI2; (<b>f</b>) WDRVI; (<b>g</b>) SR; (<b>h</b>) TIPS.</p> ">
<p>Comparison of results for actual LAI and estimated LAI. (<b>a</b>) NDVI; (<b>b</b>) EVI; (<b>c</b>) SAVI; (<b>d</b>) OSAVI; (<b>e</b>) MTVI2; (<b>f</b>) WDRVI; (<b>g</b>) SR; (<b>h</b>) TIPS.</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. Field Experiment Data
2.2. Vegetation Indices Used in This Study
2.3. Validation of the Performance of TIPS under Several Light Conditions
2.4. Analysis Procedure
3. Results and Discussion
4. Conclusions
Acknowledgments
Conflicts of Interest
- Author ContributionsMasayasu Maki designed the study and conducted field measurements. Koki Homma prepared the paddy field and managed rice growth. Masayasu Maki and Koki Homma analyzed the measurement data and wrote the manuscript.
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Year | Days After Transplanting (DAT) |
---|---|
2008 | −3, 11, 19, 33, 40, 48, 53, 64, 69, 76, 84 |
2009 | 26, 33, 43, 53, 61, 68 |
2010 | 9, 15, 48, 79 |
2011 | 23, 35, 65, 73 |
Parameter | Value |
---|---|
Fraction of diffuse radiation | 0%, 50%, 100% |
Solar zenith angle | Calculated from location and observation date |
Reflectances of leaf | Blue: 0.049, Green: 0.152, Red: 0.059, NIR: 0.469 |
Transmittances of leaf | Blue: 0.171, Green: 0.192, Red: 0.160, NIR: 0.456 |
Reflectances of background * | Blue: 0.074, Green: 0.090, Red: 0.101, NIR: 0.103 |
Leaf area density | Derived from leaf area index measured on each observation data |
Seeding density | 28 seedlings/m2 |
Vegetation Index | Estimated Equation |
---|---|
NDVI | ln(LAI) = 4.896 × NDVI − 3.136 (r2 = 0.982) |
EVI | LAI = 5.219 × EVI − 0.227 (r2 = 0.925) |
SAVI | LA I = 5.852 × SAVI − 0.317 (r2 = 0.930) |
OSAVI | ln(LAI) = 6.335 × OSAVI − 2.838 (r2 = 0.951) |
MTVI2 | LAI = 4.769 × MTVI2 − 0.0759 (r2 = 0.957) |
WDRVI | LAI = 3.153 × WDRVI + 2.461 (r2 = 0.971) |
SR | LAI = 0.164 × SR + 0.291 (r2 = 0.936) |
TIPS | LAI = 4.273 × TIPS + 0.119 (r2 = 0.975) |
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Maki, M.; Homma, K. Empirical Regression Models for Estimating Multiyear Leaf Area Index of Rice from Several Vegetation Indices at the Field Scale. Remote Sens. 2014, 6, 4764-4779. https://doi.org/10.3390/rs6064764
Maki M, Homma K. Empirical Regression Models for Estimating Multiyear Leaf Area Index of Rice from Several Vegetation Indices at the Field Scale. Remote Sensing. 2014; 6(6):4764-4779. https://doi.org/10.3390/rs6064764
Chicago/Turabian StyleMaki, Masayasu, and Koki Homma. 2014. "Empirical Regression Models for Estimating Multiyear Leaf Area Index of Rice from Several Vegetation Indices at the Field Scale" Remote Sensing 6, no. 6: 4764-4779. https://doi.org/10.3390/rs6064764