Development of an index-based insurance product: validation of a forage production index derived from medium spatial resolution fCover time series

References

• Agreste. 2014. Statistique Agricole Annuelle. V-Cultures fourragères. Paris: Ministère de l’agriculture, de l’agroalimentaire et de la forêt. http://agreste.agriculture.gouv.fr/IMG/pdf/saa2014T13bspca.pdf.
   Google Scholar
• Agriculture Financial Services Corporation. 2014. “Satellite Yield Insurance”. In Protection for Perennial Crops, 88 pp. https://www.afsc.ca/doc.aspx?id=5333
   Google Scholar
• Agroasemex, ed. 2006. La experiencia Mexicana en el Desarollo y Operacion de Seguros Paramétricos Orientados a la Ganaderia. Santiago de Queretaro: Agroasemex.
   Google Scholar
• Agroseguro, ed. 2012. Consulta de indices de vegetacion para seguros por teledeteccion. Informe annual. Agroseguro: Madrid. http://www.agresguro.es/servicios.html
   Google Scholar
• AIC (Agriculture Insurance Company of India Limited), ed. 2010. Annual Report 2009–2010. New Delhi: AIC.
   Google Scholar
• Al Haj Khaled, R., M. Duru, J.-P. Theau, S. Plantureux, and P. Cruz. 2005. “Variation in Leaf Traits through Seasons and N-Availability Levels and Its Consequences for Ranking Grassland Species.” Journal of Vegetation Science 16: 391–398. doi:10.1111/j.1654-1103.2005.tb02378.x.
   Web of Science ®Google Scholar
• Asner, G. P., C. A. Wessman, and S. Archer. 1998. “Scale Dependence of Absorption of Photosynthetically Active Radiation in Terrestrial Ecosystems.” Ecological Applications 8: 1003–1021. doi:10.1890/1051-0761(1998)008[1003:sdoaop]2.0.co;2.
   Web of Science ®Google Scholar
• Baret, F., O. Hagolle, B. Geiger, P. Bicheron, B. Miras, M. Huc, B. Berthelot, et al. 2007. “LAI, Fapar and Fcover CYCLOPES Global Products Derived from VEGETATION – Part 1: Principles of the Algorithm.” Remote Sensing of Environment 110: 275–286. doi:10.1016/j.rse.2007.02.018.
   Web of Science ®Google Scholar
• Baret, F., and M. Weiss. 2010. “Towards an Operational GMES Land Monitoring Core Service-BioPar Methods Compendium-LAI, FAPAR, F COVER NDVI.” Issue 1.5; Geoland 2. EC Proposal Reference No. FP-7-218795. http://web.vgt.vito.be/documents/BioPar/g2-BP-RP-BP038-ATBD_VegetationVariables_INRA-I1.5.pdf.
   Google Scholar
• Baret, F., M. Weiss, D. Allard, S. Garrigue, M. Leroy, H. Jeanjean, R. Fernandes, et al. Forthcoming. VALERI: A Network of Sites and a Methodology for the Validation of Medium Spatial Resolution Land Satellite Products. Remote Sensing of Environment.
   Google Scholar
• Binswanger-Mkhize, H. P. 2012. “Is There Too Much Hype about Index-Based Agricultural Insurance?” The Journal of Development Studies 48: 187–200. doi:10.1080/00220388.2011.625411.
   Web of Science ®Google Scholar
• Black, A., and H. Stephen. 2014. “Relating Temperature Trends to the Normalized Difference Vegetation Index in Las Vegas.” GIScience & Remote Sensing 51: 468–482. doi:10.1080/15481603.2014.940695.
   Web of Science ®Google Scholar
• Brown, M. E., J. E. Pinzon, K. Didan, J. T. Morisette, and C. J. Tucker. 2006. “Evaluation of the Consistency of Long-Term NDVI Time Series Derived from AVHRR,SPOT-Vegetation, Seawifs, MODIS, and Landsat ETM+ Sensors.” IEEE Transactions on Geoscience and Remote Sensing 44: 1787–1793. doi:10.1109/TGRS.2005.860205.
   Web of Science ®Google Scholar
• Bsaibes, A., D. Courault, F. Baret, M. Weiss, A. Olioso, F. Jacob, O. Hagolle, et al. 2009. “Albedo and LAI Estimates from FORMOSAT-2 Data for Crop Monitoring.” Remote Sensing of Environment 113: 716–729. doi:10.1016/j.rse.2008.11.014.
   Web of Science ®Google Scholar
• Burke, M., A. de Janvry, and J. Quintero. 2010. Providing Index Based Agricultural Insurance to Smallholders: Recent Progress and Future Promise, 38. Berkeley: University of California.
   Google Scholar
• Camacho, F., and J. Cernicharo. 2011.”Towards an Operational GMES Land Monitoring Core Service – Validation Report Medium Resolution (MERIS) Vegetation Parameters.” Issue 1.0; Geoland 2. EC Proposal Reference No. FP-7-218795. http://web.vgt.vito.be/documents/BioPar/g2-BP-RP-BP022-ValidationReport-MERISProducts-I1.00.pdf.
   Google Scholar
• Camacho, F., and I. Torralba. 2011. “Towards an Operational GMES Land Monitoring Core Service – Validation Report High Resolution Vegetation Parameters.” Issue 1.1; Geoland 2. EC Proposal Reference No. FP-7-218795. http://web.vgt.vito.be/documents/BioPar/g2-BP-RP-BP022-ValidationReport-HRProducts-I1.10.pdf
   Google Scholar
• Cayrol, P., A. Chehbouni, L. Kergoat, G. Dedieu, P. Mordelet, and Y. Nouvellon. 2000. “Grassland Modeling and Monitoring with SPOT-4 VEGETATION Instrument during the 1997–1999 SALSA Experiment.” Agricultural and Forest Meteorology 105: 91–115. doi:10.1016/S0168-1923(00)00191-X.
   Web of Science ®Google Scholar
• Ceccato, P., M. Brown, C. Funk, C. Small, E. Holthaus, A. Siebert, and N. Ward. 2008. “Remote Sensing Vegetation.” In Index Insurance and Climate Risk: Prospects for Development and Disaster Management, edited by M. E. Hellmuth, D. E. Osgood, U. Hess, A. Moorhead, and H. Bhojwani. New York: Columbia University.
   Google Scholar
• Chantarat, S., A. G. Mude, C. B. Barrett, and M. R. Carter. 2013. “Designing Index-Based Livestock Insurance for Managing Asset Risk in Northern Kenya.” Journal of Risk and Insurance 80: 205–237. doi:10.1111/j.1539-6975.2012.01463.x.
   Web of Science ®Google Scholar
• Chen, F., K. T. Weber, J. Anderson, and B. Gokhale. 2010. “Comparison of MODIS fPAR Products with Landsat-5 TM-Derived fPAR over Semiarid Rangelands of Idaho.” GIScience & Remote Sensing 47: 360–378. doi:10.2747/1548-1603.47.3.360.
   Web of Science ®Google Scholar
• Chen, F., K. T. Weber, and B. Gokhale. 2011. “Herbaceous Biomass Estimation from SPOT 5 Imagery in Semiarid Rangelands of Idaho.” Giscience & Remote Sensing 48: 195–209. doi:10.2747/1548-1603.48.2.195.
   Web of Science ®Google Scholar
• Coquil, B., and J.-P. Bordes. 2005. “FARMSTAR: An Efficient Decision Support Tool for near Real Time Crop Management from Satellite Images.” In Proceedings of the 5th European Conference on Precision Agriculture, edited by J. V. Stafford, 873–880. Wageningen: Wageningen Academic Publishers.
   Google Scholar
• Courault, D., R. Hadria, F. Ruget, A. Olioso, B. Duchemin, O. Hagolle, and G. Dedieu. 2010. “Combined Use of FORMOSAT-2 Images with a Crop Model for Biomass and Water Monitoring of Permanent Grassland in Mediterranean Region.” Hydrology and Earth System Sciences 14: 1731–1744. doi:10.5194/hess-14-1731-2010.
   Web of Science ®Google Scholar
• Craine, J. M., J. B. Nippert, A. J. Elmore, A. M. Skibbe, S. L. Hutchinson, and N. A. Brunsell. 2012. “Timing of Climate Variability and Grassland Productivity.” Proceedings of the National Academy of Sciences 109: 3401–3405. doi:10.1073/pnas.1118438109.
   PubMed Web of Science ®Google Scholar
• de Beurs, K., and G. Henebry. 2010. “Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology.” In Phenological Research, edited by I. L. Hudson and M. R. Keatley, 177–208. Berlin: Springer.
   Google Scholar
• de Leeuw, J., A. Vrieling, A. Shee, C. Atzberger, K. Hadgu, C. Biradar, H. Keah, and C. Turvey. 2014. “The Potential and Uptake of Remote Sensing in Insurance: A Review.” Remote Sensing 6: 10888–10912. doi:10.3390/rs61110888.
   Web of Science ®Google Scholar
• di Bella, C., R. Faivre, F. Ruget, B. Seguin, M. Guérif, B. Combal, M. Weiss, and C. Rebella. 2004. “Remote Sensing Capabilities to Estimate Pasture Production in France.” International Journal of Remote Sensing 25: 5359–5372. doi:10.1080/01431160410001719849.
   Web of Science ®Google Scholar
• Dinku, T., C. Funk, and D. Grimes. 2008. “The Potential of Satellite Rainfall Estimates for Index Insurance.” In Index Insurance and Climate Risk: Prospects for Development and Disaster Management, edited by M. E. Hellmuth, D. E. Osgood, U. Hess, A. Moorhead, and H. Bhojwani. New York: Columbia University.
   Google Scholar
• Douche, H., P. Roudier, P. Houdry, and A. Blondlot. 2008. “Farmstar: An Operational Remote Sensing Service for Precision Agriculture.” In Proceedings of the 9th International Conference on Precision Agriculture. Denver: Colorado State University.
   Google Scholar
• Drusch, M., U. del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, et al. 2012. “Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services.” Remote Sensing of Environment 120: 25–36. doi:10.1016/j.rse.2011.11.026.
   Web of Science ®Google Scholar
• Duan, M., Q. Gao, Y. Wan, Y. Li, Y. Guo, Z. Ganzhu, Y. Liu, and X. Qin. 2012. “Biomass Estimation of Alpine Grasslands under Different Grazing Intensities Using Spectral Vegetation Indices.” Canadian Journal of Remote Sensing 37: 413–421. doi:10.5589/m11-050.
   Web of Science ®Google Scholar
• Duru, M., M. Adam, P. Cruz, G. Martin, P. Ansquer, C. Ducourtieux, C. Jouany, J. P. Theau, and J. Viegas. 2009. “Modelling Above-Ground Herbage Mass for a Wide Range of Grassland Community Types.” Ecological Modelling 220: 209–225. doi:10.1016/j.ecolmodel.2008.09.015.
   Web of Science ®Google Scholar
• Duru, M., P. Cruz, G. Martin, J.-P. Theau, M. H. Charron, and M. Desange. 2010. “Herb’sim, a Model for a Rational Management of Grass Production and Grass Utilization.” Fourrages 201: 37–46.
   Web of Science ®Google Scholar
• Duveiller, G., F. Baret, and P. Defourny. 2013. “Using Thermal Time and Pixel Purity for Enhancing Biophysical Variable Time Series: An Interproduct Comparison.” IEEE Transactions on Geoscience and Remote Sensing 51: 2119–2127. doi:10.1109/TGRS.2012.2226731.
   Web of Science ®Google Scholar
• Gao, T., B. Xu, X. Yang, Y. Jin, H. Ma, J. Li, and H. Yu. 2013. “Using MODIS Time Series Data to Estimate Aboveground Biomass and Its Spatio-Temporal Variation in Inner Mongolia’s Grassland between 2001 and 2011.” International Journal of Remote Sensing 34: 7796–7810. doi:10.1080/01431161.2013.823000.
   Web of Science ®Google Scholar
• Gao, Z., Q. Wang, X. Cao, and W. Gao. 2014. “The Responses of Vegetation Water Content (EWT) and Assessment of Drought Monitoring along a Coastal Region Using Remote Sensing.” GIScience & Remote Sensing 51: 1–16. doi:10.1080/15481603.2014.882564.
   Web of Science ®Google Scholar
• Gommes, R., and F. Kayitakire. 2013. The Challenges of Index-Based Insurance for Food Security in Developing Countries. Ispra Varese, Italy. 2 and 3 May 2012. Luxembourg: Publications Office of the European Union.
   Google Scholar
• Gower, S. T., C. J. Kucharik, and J. M. Norman. 1999. “Direct and Indirect Estimation of Leaf Area Index, Fapar, and Net Primary Production of Terrestrial Ecosystems.” Remote Sensing of Environment 70: 29–51. doi:10.1016/S0034-4257(99)00056-5.
   Web of Science ®Google Scholar
• Grime, J. P., V. K. Brown, K. Thompson, G. J. Masters, S. H. Hillier, I. P. Clarke, A. P. Askew, D. Corker, and J. P. Kielty. 2000. “The Response of Two Contrasting Limestone Grasslands to Simulated Climate Change.” Science 289: 762–765. doi:10.1126/science.289.5480.762.
   PubMed Web of Science ®Google Scholar
• Gruber, A., and V. Levizzani. 2008. “Assessment of Global Precipitation Products.” A project of the World Climate Research Programmed Global Energy and Water Cycle Experiment (GEWEX) radiation panel, WCRP Report No. 128, WMO/TD. No. 1430, 50 pp. Bologna: Istituto di Scienze dell’Atmosfera e del Clima.
   Google Scholar
• Hazell, P. R., and U. Hess. 2010. “Drought Insurance for Agricultural Development and Food Security in Dryland Areas.” Food Security 2: 395–405. doi:10.1007/s12571-010-0087-y.
   Web of Science ®Google Scholar
• Hellmuth, M. E., D. E. Osgood, U. Hess, A. Moorhead, and H. Bhojwani. 2009. “Index Insurance and Climate Risk: Prospects for Development and Disaster Management.” In Climate and Society, Series No. 2. International Research Institute for Climate and Society (IRI), edited by D. Osgood and M. Hellmuth. New York: Columbia University.
   Google Scholar
• Hentgen, A. 1982. “Une méthode pour améliorer la connaissance de la production disponible des surfaces herbagères au niveau national.” Fourrages 92: 15–49.
   Google Scholar
• Herbold, J. 2013. “RS Technology for Crop Insurance.” Geospatial World 4: 42–44.
   Google Scholar
• Jacquemoud, S., and F. Baret. 1990. “PROSPECT: A Model of Leaf Optical Properties Spectra.” Remote Sensing of Environment 34: 75–91. doi:10.1016/0034-4257(90)90100-Z.
   Web of Science ®Google Scholar
• Jacquemoud, S., W. Verhoef, F. Baret, C. Bacour, P. J. Zarco-Tejada, G. P. Asner, C. François, and S. L. Ustin. 2009. “PROSPECT+SAIL Models: A Review of Use for Vegetation Characterization.” Remote Sensing of Environment 113 (Supplement 1): S56–S66. doi:10.1016/j.rse.2008.01.026.
   Google Scholar
• Knapp, A. K., P. A. Fay, J. M. Blair, S. L. Collins, M. D. Smith, J. D. Carlisle, C. W. Harper, B. T. Danner, M. S. Lett, and J. K. McCarron. 2002. “Rainfall Variability, Carbon Cycling, and Plant Species Diversity in a Mesic Grassland.” Science 298: 2202–2205. doi:10.1126/science.1076347.
   PubMed Web of Science ®Google Scholar
• Kneizys, F. X., E. P. Shettle, W. O. Gallery, L. W. Abreu, J. E. A. Selby, J. H. Chetwynd, and S. A. Clough. 1988. Users Guide to Lowtran. 7Air Force Geophysics Laboratory. Bedford, MA: Hanscom Air Force Base.
   Google Scholar
• Koetz, B., F. Baret, H. Poilvé, and J. Hill. 2005. “Use of Coupled Canopy Structure Dynamic and Radiative Transfer Models to Estimate Biophysical Canopy Characteristics.” Remote Sensing of Environment 95: 115–124. doi:10.1016/j.rse.2004.11.017.
   Web of Science ®Google Scholar
• Ma, W., Z. Liu, Z. Wang, W. Wang, C. Liang, Y. Tang, J.S He, and J. Fang. 2010. “Climate Change Alters Interannual Variation of Grassland Aboveground Productivity: Evidence from a 22-Year Measurement Series in the Inner Mongolian Grassland.” Journal of Plant Research 123: 509–517. doi:10.1007/s10265-009-0302-0.
   PubMed Web of Science ®Google Scholar
• Mahul, O., and J. Skees. 2007. “Managing Agricultural Risk at the Country Level: The Case of Index-Based Livestock Insurance in Mongolia.” Policy Research Working Paper 4325, Washington, DC, 37 pp.
   Google Scholar
• Mahul, O., and C. J. Stutley. 2010. “Government Support to Agricultural Insurance. Challenges and Options for Developing Countries.” Annex E: International experiences with agricultural insurance: Findings from a World Bank Survey of 65 countries. Washington, DC: The World Bank.
   Google Scholar
• Makaudze, E. M., and M. J. Miranda. 2010. “Catastrophic Drought Insurance Based on the Remotely Sensed Normalised Difference Vegetation Index for Smallholder Farmers in Zimbabwe.” Agrekon 49: 418–432. doi:10.1080/03031853.2010.526690.
   Web of Science ®Google Scholar
• Martínez, B., F. J. García-Haro, and F. Camacho de Coca. 2009. “Derivation of High-Resolution Leaf Area Index Maps in Support of Validation Activities: Application to the Cropland Barrax Site.” Agricultural and Forest Meteorology 149: 130–145. doi:10.1016/j.agrformet.2008.07.014.
   Web of Science ®Google Scholar
• Meng, Q., W. H. Cooke, and J. Rodgers. 2013. “Derivation of 16-Day Time-Series NDVI Data for Environmental Studies Using a Data Assimilation Approach.” GIScience & Remote Sensing 50: 500–514. doi:10.1080/15481603.2013.823733.
   Web of Science ®Google Scholar
• Meroni, M., C. Atzberger, C. Vancutsem, N. Gobron, F. Baret, R. Lacaze, H. Eerens, and O. Leo. 2013. “Evaluation of Agreement between Space Remote Sensing SPOT-VEGETATION fAPAR Time Series.” IEEE Transactions on Geoscience and Remote Sensing 51: 1951–1962. doi:10.1109/tgrs.2012.2212447.
   Web of Science ®Google Scholar
• Meroni, M., F. Rembold, M. M. Verstraete, R. Gommes, A. Schucknecht, and G. Beye. 2014. “Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel.” Remote Sensing 6: 5868–5884. doi:10.3390/rs6065868.
   Web of Science ®Google Scholar
• Ministère de l’Agriculture, de l’Agroalimentaire et de la Forêt. 2013. “La gestion des risques climatiques en agriculture.” http://agriculture.gouv.fr/risques-agriculture.
   Google Scholar
• Miura, T., A. Huete, and H. Yoshioka. 2006. “An Empirical Investigation of Cross-Sensor Relationships of NDVI and Red/Near-Infrared Reflectance Using EO-1 Hyperion Data.” Remote Sensing of Environment 100: 223–236. doi:10.1016/j.rse.2005.10.010.
   Web of Science ®Google Scholar
• Monteith, J. L., and C. J. Moss. 1977. “Climate and the Efficiency of Crop Production in Britain [and Discussion].” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 281: 277–294. doi:10.2307/2417832.
   Web of Science ®Google Scholar
• Mosnier, C., S. Fourdin, J.-C. Moreau, A. Boutry, E. Le Floch, M. Lherm, and J. Devun. 2014. “Impacts des aléas climatiques en élevages bovin et ovin allaitants et demande de couverture assurantielle.” Notes et études socio-économiques 38: 73–94.
   Google Scholar
• Pettorelli, N., J. O. Vik, A. Mysterud, J.-M. Gaillard, C. J. Tucker, and N. C. Stenseth. 2005. “Using the Satellite-Derived NDVI to Assess Ecological Responses to Environmental Change.” Trends in Ecology & Evolution 20: 503–510. doi:10.1016/j.tree.2005.05.011.
   PubMed Web of Science ®Google Scholar
• Phillips, R., M. West, N. Saliendra, B. Rundquist, and D. Pool. 2013. “Prediction of Senescent Rangeland Canopy Structural Attributes with Airborne Hyperspectral Imagery.” GIScience & Remote Sensing 50: 133–153. doi:10.1080/15481603.2013.793469.
   Web of Science ®Google Scholar
• Poilvé, H. 2010. “Towards an Operational GMES Land Monitoring Core Service - BioPar Methods Compendium - MERIS FR Biophysical Products.” Issue 1.0; Geoland 2. EC Proposal Reference No. FP-7-218795.
   Google Scholar
• Polley, H. W., R. L. Phillips, A. B. Frank, J. A. Bradford, P. L. Sims, J. A. Morgan, and J. R. Kiniry. 2011. “Variability in Light-Use Efficiency for Gross Primary Productivity on Great Plains Grasslands.” Ecosystems 14: 15–27. doi:10.1007/s10021-010-9389-3.
   Web of Science ®Google Scholar
• Potter, C. 2014. “Regional Analysis of MODIS Satellite Greenness Trends for Ecosystems of Interior Alaska.” GIScience & Remote Sensing 51: 390–402. doi:10.1080/15481603.2014.933606.
   Web of Science ®Google Scholar
• Propastin, P. 2013. “Large-Scale Mapping of Aboveground Biomass of Tropical Rainforest in Sulawesi, Indonesia, Using Landsat ETM+ and MODIS Data.” GIScience & Remote Sensing 50: 633–651. doi:10.1080/15481603.2013.850305.
   Web of Science ®Google Scholar
• Protin, P.-V. 2010. “Choix des points de prélèvement pour estimer la quantité d’herbe sur pied produite par une prairie de fauche ou offerte en pâture aux animaux.” Arvalis - Institut du Végétal, Paris, mai 28, 10 pp.
   Google Scholar
• RMA (Risk Management Agency). 2012. “Rainfall and Vegetation Insurance Standards Handbook. Product Administration and Standards Division.” Risk Management Agency, Federal Crop Insurance Corporation FCIC-18150 (06-2012). http://www.rma.usda.gov/handbooks/18000/2013/13_18150-1.pdf
   Google Scholar
• Roumiguié, A., A. Jacquin, G. Sigel, H. Poilvé, and B. Lepoivre. 2014. “A Risk Management Solution for Forage Production Monitoring in France: Development of an Index-Based Insurance Using Fcover Time Series from Satellite Images.” Poster presented at the Global Vegetation Monitoring and Modeling Conference, Avignon, February 3–6.
   Google Scholar
• Rowley, R. J., K. P. Price, and J. Kastens. 2007. “Remote Sensing and the Rancher: Linking Rancher Perception and Remote Sensing.” Rangeland Ecology and Management 60: 359–368. doi:10.2111/1551-5028(2007)60[359:RSATRL]2.0.CO;2.
   Web of Science ®Google Scholar
• Ruget, F., S. Satger, F. Volaire, and F. Lelièvre. 2009. “Modeling Tiller Density, Growth, and Yield of Mediterranean Perennial Grasslands with STICS.” Crop Science 49: 2379–2385. doi:10.2135/cropsci2009.06.0323.
   Web of Science ®Google Scholar
• Running, S. W., R. R. Nemani, F. A. Heinsch, M. S. Zhao, M. Reeves, and H. Hashimoto. 2004. “A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production.” Bioscience 54: 547–560. doi:10.1641/0006-3568(2004)054[0547:acsmog]2.0.co;2.
   Web of Science ®Google Scholar
• Stampfli, A., and M. Zeiter. 2004. “Plant Regeneration Directs Changes in Grassland Composition after Extreme Drought: A 13-Year Study in Southern Switzerland.” Journal of Ecology 92: 568–576. doi:10.1111/j.0022-0477.2004.00900.x.
   Web of Science ®Google Scholar
• Stöckle, C. O., M. Donatelli, and R. Nelson. 2003. “Cropsyst, a Cropping Systems Simulation Model.” European Journal of Agronomy 18: 289–307. doi:10.1016/S1161-0301(02)00109-0.
   Web of Science ®Google Scholar
• Trishchenko, A. P., J. Cihlar, and Z. Q. Li. 2002. “Effects of Spectral Response Function on Surface Reflectance and NDVI Measured with Moderate Resolution Satellite Sensors.” Remote Sensing of Environment 81: 1–18. doi:10.1016/S0034-4257(01)00328-5.
   Web of Science ®Google Scholar
• Turvey, C. G., and M. K. McLaurin. 2012. “Applicability of the Normalized Difference Vegetation Index (NDVI) in Index-Based Crop Insurance Design.” Weather, Climate, and Society 4: 271–284. doi:10.1175/WCAS-D-11-00059.1.
   Web of Science ®Google Scholar
• Verhoef, W. 1984. “Light Scattering by Leaf Layers with Application to Canopy Reflectance Modeling: The SAIL Model.” Remote Sensing of Environment 16: 125–141. doi:10.1016/0034-4257(84)90057-9.
   Web of Science ®Google Scholar
• Vidal, J.-P., E. Martin, L. Franchistéguy, M. Baillon, and J.-M. Soubeyroux. 2010. “A 50-Year High-Resolution Atmospheric Reanalysis over France with the Safran System.” International Journal of Climatology 30: 1627–1644. doi:10.1002/joc.2003.
   Web of Science ®Google Scholar
• Vrieling, A., M. Meroni, A. Shee, A. G. Mude, J. Woodard, C. A. J. M. de Bie, and F. Rembold. 2014. “Historical Extension of Operational NDVI Products for Livestock Insurance in Kenya.” International Journal of Applied Earth Observation and Geoinformation 28: 238–251. doi:10.1016/j.jag.2013.12.010.
   Web of Science ®Google Scholar
• World Bank. 2012. Argentina - NDVI Pasture Index-Based Insurance for Livestock Producers in South West Buenos Aires Province. Feasibility Study, 120 pp. Final Report, PADA355. Washington, DC: The World Bank.
   Google Scholar
• Yang, Y., J. Fang, W. Ma, and W. Wang. 2008. “Relationship between Variability in Aboveground Net Primary Production and Precipitation in Global Grasslands.” Geophysical Research Letters 35: L23710. doi:10.1029/2008GL035408.
   Google Scholar