Empirical Regression Models for Estimating Multiyear Leaf Area Index of Rice from Several Vegetation Indices at the Field Scale
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.
References
- Dente, L.; Satalino, G.; Mattia, F.; Rinaldi, M. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-wheat model to map wheat yield. Remote Sens. Environ 2008, 112, 1395–1407. [Google Scholar]
- Fang, H.; Liang, S.; Hoogenboom, G.; Teasdale, J.; Cavigelli, M. Corn-yield estimation through assimilation of remotely sensed data into the CSM-CERES-Maize model. Int. J. Remote Sens 2008, 29, 3011–3032. [Google Scholar]
- Curnel, Y.; de Wit, A.; Duveiller, G.; Defourny, P. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment. Agric. For. Meteorol 2011, 151, 1843–1855. [Google Scholar]
- Padilla, F.L.M.; Maas, S.J.; González-Dugo, M.P.; Mansilla, F.; Rajan, N.; Gavilán, P.; Dominguez, J. Monitoring regional wheat yield in southern Spain using the GRAMI model and satellite imagery. Field Crops Res 2012, 130, 145–154. [Google Scholar]
- Xiao, X.; He, L.; Salas, W.; Li, C.; Moore, B., III; Zhao, R.; Frolking, S.; Boles, S. Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice field. Int. J. Remote Sens 2002, 23, 3595–3604. [Google Scholar]
- Bsaibes, A.; Courault, D.; Baret, F.; Weiss, M.; Olioso, A.; Jacob, F.; Hagolle, O.; Marloie, O.; Bertrand, N.; Desfond, V.; Kzemipour, F. Albedo and LAI estimation from FORMOSAT-2 data for crop monitoring. Remote Sens. Environ 2009, 113, 716–729. [Google Scholar]
- Hansen, P.M.; Schjoerring, J.K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ 2003, 86, 542–553. [Google Scholar]
- Darvishzadeh, R.; Skidmore, A.; Schlerf, M.; Atzberger, C.; Corsi, F.; Cho, M. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J. Photogramm. Remote Sens 2008, 63, 409–426. [Google Scholar]
- Knyazikhin, Y.; Martonchik, J.V.; Myneni, R.B.; Diner, D.J.; Running, S.W. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J. Geophys. Res 1998, 103, 32257–32276. [Google Scholar]
- Baret, F.; Hagolle, O.; Geiger, B.; Bicheron, P.; Miras, B.; Huc, M.; Berthelot, B.; Niño, F.; Weiss, M.; Samain, O.; et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm. Remote Sens. Environ 2007, 110, 275–286. [Google Scholar]
- Darvishzaden, R.; Atzberger, C.; Skidmore, A.; Schlerf, M. Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models. ISPRS J. Photogramm. Remote Sens 2011, 66, 894–906. [Google Scholar]
- Atzberger, C.; Richter, K. Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery. Remote Sens. Environ 2012, 120, 208–218. [Google Scholar]
- Huete, A. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ 1988, 25, 295–309. [Google Scholar]
- Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ 1996, 55, 95–107. [Google Scholar]
- Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ 2004, 90, 337–352. [Google Scholar]
- Liu, H.Q.; Huete, A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sens 1995, 33, 457–465. [Google Scholar]
- Gitelson, A.A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol 2004, 161, 165–173. [Google Scholar]
- Oki, K.; Noda, K.; Yoshida, K.; Azechi, I.; Maki, M.; Homma, K.; Hongo, C.; Shirakawa, H. Development of an Environmentally Advanced Basin Model in Asia. In Crop Production, 1st ed; Goyal, A., Asif, M., Eds.; InTech: Rijeka, Croatia, 2013; pp. 17–48. [Google Scholar]
- Liu, J.; Jégo, G. Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sens. Environ 2012, 123, 347–358. [Google Scholar]
- Hashimoto, N.; Maki, M.; Tanaka, K.; Tamura, M. Study of a method for extracting LAI time-series patterns for estimation of crop phenology. J. Remote Sens. Soc. Jpn 2009, 29, 381–391, (In Japanese with English abstract). [Google Scholar]
- Lillesaeter, O. Spectral reflectance of partly transmitting leaves-laboratory measurements and mathematical modelling. Remote Sens. Environ 1982, 12, 247–254. [Google Scholar]
- Baret, F.; Guyot, G. Potentials and limits of vegetation indexes for LAI and APAR assessment. Remote Sens. Environ 1991, 35, 161–173. [Google Scholar]
- Huete, A.; Liu, H.; Batchily, K.; van Leeuwen, W. Comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ 1997, 59, 440–451. [Google Scholar]
- Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ 2002, 83, 195–213. [Google Scholar]
- Steven, M. The sensitivity of the OSAVI vegetation index to observational parameters. Remote Sens. Environ 1998, 63, 49–60. [Google Scholar]
- Gitelson, A.A.; Wardlow, B.D.; Keydan, G.P.; Leavitt, B. An evaluation of MODIS 250-m data for green LAI estimation in crops. Geophys. Res. Lett 2007, 34. [Google Scholar] [CrossRef]
- Oki, K.; Oguma, H. Estimation of the canopy coverage in specific forest using remotely sensed data—Estimation of Alder trees in Kushiro Mire. J. Remote Sens. Soc. Jpn 2002, 22, 510–516, (In Japanese with English abstract). [Google Scholar]
- Kobayashi, H.; Iwabuchi, H. A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape. Remote Sens. Environ 2008, 112, 173–185. [Google Scholar]
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