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Prediction of crop productivity and evapotranspiration with two photosynthetic parameter regionalization methods

Published online by Cambridge University Press:  27 November 2012

S. HU  and
X. MO
S. HU
Affiliation:
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P.R. China
X. MO*
Affiliation:
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P.R. China
*
*To whom all correspondence should be addressed. Email: [email protected]
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Summary

Parameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then used to simulate the wheat yield and evapotranspiration (ET) in the North China Plain (referred to as the Vcmax method). To evaluate its performance, the simulated yield and ET were compared with those derived by the leaf area index (LAI) method using the retrieved LAI from NDVI to drive the VIP model. The results showed that the Vcmax method performed better than the LAI method in highly productive fields, while the LAI method described the inter-annual variations of yield more favourably in fields with low productivity. Over the study area, average yield (4520 kg/ha) and seasonal ET (360 mm) simulated by the LAI method was slightly lower than those simulated using the Vcmax method (4730 kg/ha for yield and 372 mm for ET). Compared with the census data of yield, the relative root mean square error (RMSE) of grain yield with Vcmax method (0·17) was lower than that of the LAI method (0·20). In conclusion, the physical model with spatial Vcmax pattern from remote sensing is reliable for regional crop productivity prediction.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 152 , Issue 1 , February 2014 , pp. 119 - 133
Copyright
Copyright © Cambridge University Press 2012 

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References

Alonso, A., Perez, P. A. & Martinez-Carrasco, R. (2009). Growth in elevated CO2 enhances temperature response of photosynthesis in wheat. Physiologia Plantarum 135, 109–120.Google Scholar
Arora, V. K. & Boer, G. J. (2005). A parameterization of leaf phenology for the terrestrial ecosystem component of climate models. Global Change Biology 11, 39–59.CrossRefGoogle Scholar
Ball, J. T., Woodrow, I. E. & Berry, J. A. (1987). A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In Progress in Photosynthesis Research vol. 4 (Ed. Biggens, J.), pp. 221–224. Netherlands: Martinus Nijhoff Publishers.Google Scholar
Baret, F. & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 35, 161–173.Google Scholar
Basnyat, P., McConkey, B., Lafond, G. R., Moulin, A. & Pelcat, Y. (2004). Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies. Canadian Journal of Plant Science 84, 97–103.Google Scholar
Becker-Reshef, I., Vermote, E., Lindeman, M. & Justice, C. (2010). A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment 114, 1312–1323.Google Scholar
Bernacchi, C. J., Portis, A. R., Nakano, H., von Caemmerer, S. & Long, S. P. (2002). Temperature response of mesophyll conductance. implication for the determination of Rubisco enzyme kinetics and for limitations to photosynthesis in vivo. Plant Physiology 130, 1992–1998.Google Scholar
Brown, L., Chen, J. M., Leblanc, S. G. & Cihlar, J. (2000). A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis. Remote Sensing of Environment 71, 16–25.Google Scholar
Bruck, H. & Guo, S. W. (2006). Influence of N form on growth and photosynthesis of Phaseolus vulgaris L. plants. Journal of Plant Nutrition and Soil Science 169, 849–856.Google Scholar
Bsaibes, A., Courault, D., Baret, F., Weiss, M., Olioso, A., Jacob, F., Hagolle, O., Marloie, O., Bertrand, N., Desfond, V. & Kzemipour, F. (2009). Albedo and LAI estimates from FORMOSAT-2 data for crop monitoring. Remote Sensing of Environment 113, 716–729.Google Scholar
Carrer, J. A. M. & Mansur, W. J. (2006). Solution of the two-dimensional scalar wave equation by the time-domain boundary element method: Lagrange truncation strategy in time integration. Structural Engineering and Mechanics 23, 263–278.Google Scholar
Chen, J. (1996). Evaluation of vegetation indices and modified simple ratio for boreal applications. Canadian Journal of Remote Sensing 22, 229–242.Google Scholar
Chen, J., Jonsson, P., Tamura, M., Gu, Z., Matsushita, B. & Eklundh, L. (2004). A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment 91, 332–344.Google Scholar
Collatz, G. J., Ball, J. T., Grivet, C. & Berry, J. A. (1991). Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agricultural and Forest. Meteorology 54, 107–136.Google Scholar
Duchemin, B., Hadria, R., Erraki, S., Boulet, G., Maisongrande, P., Chehbouni, A., Escadafal, R., Ezzahar, J., Hoedjes, J. C. B., Kharrou, M. H., Khabba, S., Mougenot, B., Olioso, A., Rodriguez, J. C. & Simonneaux, V. (2006). Monitoring wheat phenology and irrigation in Central Morocco: on the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management 79, 1–27.Google Scholar
Farquhar, G. D., Von Caemmerer, S. & Berry, J. A. (1980). A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90.CrossRefGoogle Scholar
Farquhar, G. D., von Caemmerer, S. & Berry, J. A. (2001). Models of photosynthesis. Plant Physiology 125, 42–45.Google Scholar
Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337–352.CrossRefGoogle Scholar
Hasegawa, K., Matsuyama, H., Tsuzuki, H. & Sweda, T. (2006). The effect of bidirectional reflectance distribution function on the estimation of vegetation indices and leaf area index (LAI)-A case study of the vegetation in succession stages after forest fire in Northwestern Canada. Journal of the Remote Sensing Society of Japan 26, 186–201 (in Japanese with English abstract).Google Scholar
Hasegawa, K., Matsuyama, H., Tsuzuki, H. & Sweda, T. (2010). Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures. Remote Sensing of Environment 114, 514–519.Google Scholar
Hebei Province Bureau of Statistics (2009). Hebei Agricultural Statistical Yearbook. Beijing: China Statistics Press.Google Scholar
Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V. & Bonfil, D. J. (2011). LAI assessment of wheat and potato crops by VEN mu S and Sentinel-2 bands. Remote Sensing of Environment 115, 2141–2152.Google Scholar
Hillel, D. (1980). Fundamentals of Soil Physics. New York: Academic Press.Google Scholar
Hu, S. & Mo, X. (2011). Interpreting spatial heterogeneity of crop yield with a process model and remote sensing. Ecological Modelling 222, 2530–2541.Google Scholar
Huete, A., Justice, C. & van Leeuwen, W. (1999). Modis Vegetation Index (MOD13). Algorithm Theoretical Database Document version 3. Washington, DC: NASA. Available online at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf (accessed October 2010).Google Scholar
James, R. A., Munns, R., Von Caemmerer, S., Trejo, C., Miller, C. & Condon, T. (2006). Photosynthetic capacity is related to the cellular and subcellular partitioning of Na+, K+ and Cl− in salt-affected barley and durum wheat. Plant Cell and Environment 29, 2185–2197.Google Scholar
Institute of Soil Science, Academia Sinica (1989). The Soil Atlas of China. Beijing, China: Cartographic Publishing House.Google Scholar
Kaufman, Y. J. & Tanre, D. (1996). Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: from AVHRR to EOS-MODIS. Remote Sensing of Environment 55, 65–79.Google Scholar
Leuning, R. (1995). A critical appraisal of a combined stomatal-photosynthesis model for C3 plants. Plant Cell and Environment 18, 339–355.Google Scholar
Lenney, M. P., Woodcock, C. E., Collins, J. B. & Hamdi, H. (1996). The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM. Remote Sensing of Environment 56, 8–20.CrossRefGoogle Scholar
Li, L., Nielsen, D. C., Yua, Q., Mac, L. & Ahuja, L. R. (2010). Evaluating the crop water stress index and its correlation with latent heat and CO2 fluxes over winter wheat and maize in the North China plain. Agricultural Water Management 97, 1146–1155.Google Scholar
Liu, J., Tian, H., Liu, M., Zhuang, D., Melillo, J. M. & Zhang, Z. (2005). China's changing landscape during the 1990s: Large-scale land transformations estimated with satellite data. Geophysical Research Letters 32, L02405. doi:10.1029/2004GL021649Google Scholar
Makino, A. (2003). Rubisco and nitrogen relationships in rice: leaf photosynthesis and plant growth. Soil Science & Plant Nutrition 49, 319–327.Google Scholar
Mo, X. G. & Liu, S. X. (2001). Simulating evapotranspiration and photosynthesis of winter wheat over the growing season. Agricultural and Forest Meteorology 109, 203–222.Google Scholar
Mo, X. G., Liu, S. X., Lin, Z. H. & Zhao, W. (2004). Simulating temporal and spatial variation of evapotranspiration over the Lushi basin. Journal of Hydrology 285, 125–142.Google Scholar
Mo, X. G., Liu, S. X., Lin, Z. H. & Guo, R. P. (2009). Regional crop yield, water consumption and water use efficiency and their responses to climate change in the North China Plain. Agriculture Ecosystems and Environment 134, 67–78.Google Scholar
Mo, X. G., Liu, S. X. & Lin, Z. H. (2012). Evaluation of an ecosystem model for a wheat-maize double cropping system over the North China Plain. Environmental Modelling and Software 32, 61–73.Google Scholar
Monteith, J. L. & Unsworth, M. H. (1990). Principles of Environmental Physics. London: Edward Armold.Google Scholar
Moragues, M., del Moral, L. F. G., Moralejo, M. & Royo, C. (2006). Yield formation strategies of durum wheat landraces with distinct pattern of dispersal within the Mediterranean basin – II. Biomass production and allocation. Field Crops Research 95, 182–193.Google Scholar
Moriondo, M., Maselli, F. & Bindi, M. (2007). A simple model of regional wheat yield based on NDVI data. European Journal of Agronomy 26, 266–274.CrossRefGoogle Scholar
Müller, J., Wernecke, P. & Diepenbrock, W. (2005). LEAFC3-N: a nitrogen-sensitive extension of the CO2 and H2O gas exchange model LEAFC3 parameterised and tested for winter wheat (Triticum aestivum L.). Ecological Modelling 183, 183–210.Google Scholar
Murphy, K. M., Dawson, J. C. & Jones, S. S. (2008). Relationship among phenotypic growth traits, yield and weed suppression in spring wheat landraces and modern cultivars. Field Crops Research 105, 107–115.Google Scholar
Myneni, R. B., Hall, F. G., Sellers, P. J. & Marshak, A. L. (1995). The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing 33, 481–486.Google Scholar
Myneni, R. B., Hoffman, S., Knyazikhin, Y., Privette, J. L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G. R., Lotsch, A., Friedl, M., Morisette, J. T., Votava, P., Nemani, R. R. & Running, S. W. (2002). Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment 83, 214–231.Google Scholar
Ozalkan, C., Sepetoglu, H. T., Daur, I. & Sen, O. F. (2010). Relationship between some plant growth parameters and grain yield of chickpea (Cicer arietinum L.) during different growth stages. Turkish Journal of Field Crops 15, 79–83.Google Scholar
Parker, J. A., Kenyon, R. V. & Troxel, D. E. (1983). Comparison of interpolating methods for image resampling. IEEE Transactions on Medical Imaging 2, 31–39.Google Scholar
Rautiainen, M. (2005). Retrieval of leaf area index for a coniferous forest by inverting a forest reflectance model. Remote Sensing of Environment 99, 295–303.Google Scholar
Ren, J. Q., Chen, Z. X., Zhou, Q. B. & Tang, H. J. (2008). Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation 10, 403–13.Google Scholar
Sellers, P. J., Los, S. O., Tucker, C. J., Justice, C. O., Dazlich, D. A., Collatz, G. J. & Randall, D. (1996). A revised land surface parameterization (SiB2) for atmospheric GCMs. Part II. The generation of global fields of terrestrial biophysical parameters from satellite data. Journal of Climate 9, 706–737.Google Scholar
Smith, A. M., Bourgeois, G., Teillet, P. M., Freemantle, J. & Nadeau, C. (2008). A comparison of NDVI and MTVI2 for estimating LAI using CHRIS imagery: a case study in wheat. Canadian Journal of Remote Sensing 34, 539–548.Google Scholar
Tester, M. & Davenport, R. (2003). Na+ tolerance and Na+ transport in higher plants. Annals of Botany 91, 503–27.Google Scholar
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127–150.Google Scholar
Yang, Y., Watanabe, M., Zhang, X., Zhang, J., Wang, Q. & Hayashi, S. (2006). Optimizing irrigation management for wheat to reduce groundwater depletion in the piedmont region of the Taihang mountains in the North China Plain. Agricultural Water Management 82, 25–44.Google Scholar
Zhuang, D., Liu, J. & Liu, M. (1999). Research activities on land use/land cover in the past ten years in China using space technology. Chinese Geographical Science 9, 330–334.Google Scholar