The increasing availability of satellite data at higher spatial, temporal and spectral resolutions is enabling new applications in agriculture and economic development, including agricultural insurance. Yet, effectively using satellite data in this context requires blending technical knowledge about their capabilities and limitations with an understanding of their influence on the value of risk-reduction programmes. In this Review, we discuss how approaches to estimate agricultural losses for index insurance have evolved from costly field-sampling-based campaigns towards lower-cost techniques using weather and satellite data. We identify advances in remote sensing and crop modelling for assessing agricultural conditions, but reliably and cheaply assessing production losses remains challenging in complex landscapes. We illustrate how an economic framework can be used to gauge and enhance the value of insurance based on earth-observation data, emphasizing that even as yield-estimation techniques improve, the value of an index insurance contract for the insured depends largely on how well it captures the losses when people suffer most. Strategically improving the collection and accessibility of reliable ground-reference data on crop types and production would facilitate this task. Audits to account for inevitable misestimation complement efforts to detect and protect against large losses.
In many developing regions, adverse weather can lead to food insecurity, reduced investments or distressed asset sales that ensnare people in a cycle of poverty.
Tools to manage risk — such as well-designed insurance — can help people avoid the most severe possible consequences of bad weather and build confidence to invest in additional income-generating opportunities.
In recent decades, governments and researchers across the globe have trialled approaches to inexpensively assess agricultural losses. Index-based insurance offers promise, but detecting losses cheaply and accurately remains challenging.
Recent advances in crop modelling and remote sensing can improve index-based approaches by strengthening the link between indices and actual losses, as well as reducing programme costs.
We provide an economic framework to evaluate indices, suggesting how the remote sensing and modelling communities can contribute to enhancing index insurance quality through better detection of adverse conditions.
Promising opportunities to enhance index insurance programmes include inexpensively addressing heterogeneous conditions on the ground, such as employing audits, optimizing insurance zones, using new sensors or increasing contract flexibility.
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Ravallion, M. et al. Poverty Comparisons Vol. 56 (Taylor & Francis, 1994).
Townsend, R. M. Risk and insurance in village India. Econometrica 62, 539–591 (1994).
Jacoby, H. G. & Skoufias, E. Risk, financial markets, and human capital in a developing country. Rev. Econ. Stud. 64, 311–335 (1997).
Kochar, A. Smoothing consumption by smoothing income: hours-of-work responses to idiosyncratic agricultural shocks in rural India. Rev. Econ. Stat. 81, 50–61 (1999).
Alderman, H. & Paxson, C. H. in Economics in a Changing World (ed. Bacha, E. L.) 48–78 (Springer, 1994).
Kazianga, H. & Udry, C. Consumption smoothing? Livestock, insurance and drought in rural Burkina Faso. J. Dev. Econ. 79, 413–446 (2006).
Dercon, S. & Christiaensen, L. Consumption risk, technology adoption and poverty traps: evidence from Ethiopia. J. Dev. Econ. 96, 159–173 (2011).
Hill, R. V. & Viceisza, A. A field experiment on the impact of weather shocks and insurance on risky investment. Exp. Econ. 15, 341–371 (2012).
Oviedo, A. M. & Moroz, H. A Review of the Ex Post and Ex Ante Impacts of Risk (World Bank, 2013).
Cai, J. The impact of insurance provision on household production and financial decisions. Am. Econ. J. Econ. Policy 8, 44–88 (2016).
Jensen, N. D. & Barrett, C. B. Agricultural index insurance for development. Appl. Econ. Perspect. Policy 39, 199–219 (2017).
Shah, M. & Steinberg, B. M. Drought of opportunities: contemporaneous and long-term impacts of rainfall shocks on human capital. J. Political Econ. 125, 527–561 (2017).
Janzen, S. A. & Carter, M. R. After the drought: the impact of microinsurance on consumption smoothing and asset protection. Am. J. Agric. Econ. 101, 651–671 (2018).
Amare, M., Jensen, N. D., Shiferaw, B. & Cissé, J. D. Rainfall shocks and agricultural productivity: implication for rural household consumption. Agric. Syst. 166, 79–89 (2018).
Morduch, J. Income smoothing and consumption smoothing. J. Econ. Perspect. 9, 103–114 (1995).
Mobarak, A. M. & Rosenzweig, M. Informal risk sharing, index insurance, and risk taking in developing countries. Am. Econ. Rev. 103, 375–380 (2013).
Cole, S., Giné, X. & Vickery, J. How does risk management influence production decisions? Evidence from a field experiment. Rev. Financ. Stud. 30, 1935–1970 (2017).
Elabed, G. & Carter, M. R. Ex-ante Impacts of Agricultural Insurance: Evidence From a Field Experiment in Mali (Univ. California, Davis, 2014).
Karlan, D., Osei, R., Osei-Akoto, I. & Udry, C. Agricultural decisions after relaxing credit and risk constraints. Q. J. Econ. 129, 597–652 (2014).
Hill, R. V. et al. Ex ante and ex post effects of hybrid index insurance in Bangladesh. J. Dev. Econ. 136, 1–17 (2019).
Hazell, P. B. The appropriate role of agricultural insurance in developing countries. J. Int. Dev. 4, 567–581 (1992).
Skees, J. R., Hazell, P. B. & Miranda, M. J. New Approaches to Crop Yield Insurance in Developing Countries (International Food Policy Research Institute, 1999).
Pauly, M. V. The economics of moral hazard: comment. Am. Econ. Rev. 58, 531–537 (1968).
Shavell, S. in Foundations of Insurance Economics (eds Dionne, G. & Harrington, S. E.) 280–301 (Springer, 1979).
Gommes, R. & Kayitakire, F. The Challenges of Index-based Insurance for Food Security in Developing Countries: Proceedings of a Technical Workshop Organised by the EC Joint Research Centre (JRC) and the International Research Institute for Climate and Society (IRI, Earth Institute, Columbia University), JRC Ispra, Italy, 2 and 3 May 2012 (European Commission, 2013).
Jensen, N. D., Mude, A. G. & Barrett, C. B. How basis risk and spatiotemporal adverse selection influence demand for index insurance: evidence from northern Kenya. Food Policy 74, 172–198 (2014).
Miranda, M. J. Area-yield crop insurance reconsidered. Am. J. Agric. Econ. 73, 233–242 (1991).
Miranda, M. J. & Farrin, K. Index insurance for developing countries. Appl. Econ. Perspect. Policy 34, 391–427 (2012).
Clarke, D. J. A theory of rational demand for index insurance. Am. Econ. J. Microecon. 8, 283–306 (2016).
Carter, M. R., de Janvry, A., Sadoulet, E. & Sarris, A. Index insurance for developing country agriculture: a reassessment. Annu. Rev. Resour. Econ. 9, 421–438 (2017).
De Leeuw, J. et al. The potential and uptake of remote sensing in insurance: a review. Remote Sens. 6, 10888–10912 (2014).
Vedenov, D. V. & Barnett, B. J. Efficiency of weather derivatives as primary crop insurance instruments. J. Agric. Resour. Econ. 29, 387–403 (2004).
Berg, A., Quirion, P. & Sultan, B. Weather-index drought insurance in Burkina-Faso: assessment of its potential interest to farmers. Weather Clim. Soc. 1, 71–84 (2009).
Jensen, N. D., Barrett, C. B. & Mude, A. G. Index insurance quality and basis risk: evidence from northern Kenya. Am. J. Agric. Econ. 98, 1450–1469 (2016).
Carter, M. R. & Chiu, T. Quality standards for agricultural index insurance: an agenda for action. Microinsurance Network https://microinsurancenetwork.org/sites/default/files/SoM_2018_WEB_final.pdf (2018).
Harrison, G. W., Martínez-Correa, J., Ng, J. M. & Swarthout, J. T. Evaluating the welfare of index insurance: an application of behavioural welfare economics. Center for the Economic Analysis of Risk, Robinson College of Business, Georgia State University https://cear.gsu.edu/files/2020/05/CEAR-WP-2016-07-Evaluating-the-Welfare-of-Index-Insurance-MAY-2020.pdf (2020).
Morduch, J. Between the state and the market: can informal insurance patch the safety net? World Bank Res. Obs. 14, 187–207 (1999).
African Risk Capacity. Response to ActionAid’s flawed claims. African Risk Capacity https://www.africanriskcapacity.org/2017/07/10/african-risk-capacity-response-to-actionaids-flawed-claims/ (2017).
Hazell, P. & Varangis, P. Best practices for subsidizing agricultural insurance. Glob. Food Security 25, 100326 (2019).
Kist, F. O., Meyers, G., Witcraft, S. E. & Sherman, H. A. Evaluating the Effectiveness of Index-Based Insurance Derivatives in Hedging Property/Casualty Insurance Transaction (American Academy of Actuaries Index Securitization Task Force, 1999).
Vrieling, A. et al. Historical extension of operational NDVI products for livestock insurance in Kenya. Int. J. Appl. Earth Obs. Geoinf. 28, 238–251 (2014).
Black, E., Greatrex, H., Young, M. & Maidment, R. Incorporating satellite data into weather index insurance. Bull. Am. Meteorol. Soc. 97, ES203–ES206 (2016).
Jensen, N. D. et al. Does the design matter? Comparing satellite-based indices for insuring pastoralists against drought. Ecol. Econ. 162, 59–73 (2019).
Vrieling, A. et al. Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists. Remote Sens. Environ. 174, 44–55 (2016).
Fafchamps, M. Sequential labor decisions under uncertainty: an estimable household model of West-African farmers. Econometrica 61, 1173–1197 (1993).
Maidment, R. I. et al. The 30 TAMSAT African rainfall climatology and time series (TARCAT) data set. J. Geophys. Res. Atmos. 119, 10,619–10,644 (2014).
Leblois, A. & Quirion, P. Agricultural insurances based on meteorological indices: realizations, methods and research challenges. Meteorol. Appl. 20, 1–9 (2013).
Jia, H., Wang, J., Cao, C., Pan, D. & Shi, P. Maize drought disaster risk assessment of China based on EPIC model. Int. J. Digit. Earth 5, 488–515 (2012).
Yu, C. et al. Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions. Environ. Model. Softw. 62, 454–464 (2014).
Stojanovski, P. et al. Agricultural risk modeling challenges in China: probabilistic modeling of rice losses in Hunan Province. Int. J. Disaster Risk Sci. 6, 335–346 (2015).
Elliott, J. et al. Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management. Agric. Syst. 159, 275–281 (2018).
JBA Risk Management. India crop model executive briefing. JBA Risk Management https://www.jbarisk.com/media/1443/jba-india-crop-model-executive-briefing.pdf (2018).
Carter, M. R. in Protecting the Poor: A Microinsurance Compendium Vol. II (eds Churchill, C. & Matul, M.) 238–257 (International Labour Office and Munich Re Foundation, 2012).
Chantarat, S., Mude, A. G., Barrett, C. B. & Carter, M. R. Designing index-based livestock insurance for managing asset risk in northern Kenya. J. Risk Insur. 80, 205–237 (2013).
Carletto, C., Savastano, S. & Zezza, A. Fact or Artefact: The Impact of Measurement Errors on the Farm Size–Productivity Relationship (World Bank, 2011).
Gourlay, S., Kilic, T. & Lobell, D. B. A new spin on an old debate: errors in farmer-reported production and their implications for inverse scale-productivity relationship in Uganda. J. Dev. Econ. 141, 102376 (2019).
Osgood, D. et al. Farmer perception, recollection, and remote sensing in weather index insurance: an Ethiopia case study. Remote Sens. 10, 1887 (2018).
Chakravarti, J. S. Agricultural Insurance a Practical Scheme Suited to Indian Conditions (Government Press, 1920).
Skees, J. R., Black, J. R. & Barnett, B. J. Designing and rating an area yield crop insurance contract. Am. J. Agric. Econ. 79, 430–438 (1997).
Elabed, G., Bellemare, M. F., Carter, M. R. & Guirkinger, C. Managing basis risk with multiscale index insurance. Agric. Econ. 44, 419–431 (2013).
Casaburi, L. & Willis, J. Time versus state in insurance: experimental evidence from contract farming in Kenya. Am. Econ. Rev. 108, 3778–3813 (2018).
Stoeffler, Q., Carter, M. R., Guirkinger, C. & Gelade, W. The spillover impact of index insurance on agricultural investment by cotton farmers in Burkina Faso. National Bureau of Economic Research https://www.nber.org/papers/w27564 (2020).
Makanza, R. et al. High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging. Plant Methods 14, 49 (2018).
Ceballos, F., Kramer, B. & Robles, M. The feasibility of picture-based insurance (PBI): Smartphone pictures for affordable crop insurance. Dev. Eng. 4, 100042 (2019).
Platteau, J.-P., De Bock, O. & Gelade, W. The demand for microinsurance: a literature review. World Dev. 94, 139–156 (2017).
Hess, U., Hazell, P. & Kuhn, S. Innovations and Emerging Trends in Agricultural Insurance. (Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, 2016).
Regional Centre for Mapping of Resources for Development (RCMRD). Using satellite imagery to improve implementation of crop insurance program in Kenya. Regional Centre for Mapping of Resources for Development (RCMRD) https://www.rcmrd.org/using-satellite-imagery-to-improve-implementation-of-crop-insurance-program-in-kenya (2019).
Stigler, M. M. & Lobell, D. Suitability of index insurance: new insights from satellite data. Agricultural and Applied Economics Association (AAEA) 2020 Annual Meeting, July 26–28, Kansas City, Missouri https://doi.org/10.22004/ag.econ.304663 (2020).
Hernandez, E., Goslinga, R. & Wang, V. Using satellite data to scale smallholder agricultural insurance. CGAP http://www.cgap.org/sites/default/files/Brief-Using-Satellite-Data-Smallholder-Agricultural-Insurance-Aug-2018.pdf (2018).
Sahajpal, R., Coutu, S., Tombez, G. & Becker-Reshef, I. Reliably Forecasting Field-Scale Crop Yields Through Optimizing Number and Location of Crop Cuts: A Case Study in Ukraine (American Geophysical Union (AGU), 2019).
Greatrex, H. et al. Scaling Up Index Insurance for Smallholder Farmers: Recent Evidence and Insights (Climate Change, Agriculture and Food Security, 2015).
Black, E. et al. The use of remotely sensed rainfall for managing drought risk: a case study of weather index insurance in Zambia. Remote Sens. 8, 342 (2016).
Flatnes, J. E. & Carter, M. R. Fail-safe index insurance without the cost: a satellite based conditional audit approach (Univ. California, 2016).
Vroege, W., Dalhaus, T. & Finger, R. Index insurances for grasslands–A review for Europe and North-America. Agric. Syst. 168, 101–111 (2019).
AIR Worldwide. Current crop risk in India: how can it be managed effectively. AIR Worldwide https://www.air-worldwide.com/publications/air-currents/2019/Current-Crop-Risk-in-India-How-Can-It-Be-Managed-Effectively-/ (2019).
Ahmed, S., McIntosh, C. & Sarris, A. The impact of commercial rainfall index insurance: experimental evidence from Ethiopia. Am. J. Agric. Econ. 102, 1154–1176 (2020).
Forshaw, M. R. B., Haskell, A., Miller, P. F., Stanley, D. J. & Townshend, J. R. G. Spatial resolution of remotely sensed imagery A review paper. Int. J. Remote Sens. 4, 497–520 (1983).
Apollo Mapping. Apollo Mapping price list. Apollo Mapping https://apollomapping.com/image_downloads/Apollo_Mapping_Imagery_Price_List.pdf (2018).
Weiss, M., Jacob, F. & Duveiller, G. Remote sensing for agricultural applications: a meta-review. Remote Sens. Environ. 236, 111402 (2020).
Lobell, D. B. The use of satellite data for crop yield gap analysis. Field Crops Res. 143, 56–64 (2013).
Hufkens, K. et al. Monitoring crop phenology using a smartphone based near-surface remote sensing approach. Agric. For. Meteorol. 265, 327–337 (2019).
Guan, K. et al. The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields. Remote Sens. Environ. 199, 333–349 (2017).
Fritz, S. et al. A comparison of global agricultural monitoring systems and current gaps. Agric. Syst. 168, 258–272 (2019).
Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1978).
Anyamba, A. & Tucker, C. J. Historical Perspectives on AVHRR NDVI and Vegetation Drought Monitoring (NASA Publications, 2012).
Gitelson, A. A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 161, 165–173 (2004).
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).
Gitelson, A. A. et al. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58, 289–298 (1996).
Gitelson, A. A. et al. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett. 30, 1248 (2003).
Burke, M. & Lobell, D. B. Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proc. Natl Acad. Sci. USA 114, 2189–2194 (2017).
Khanal, S., Fulton, J. & Shearer, S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput. Electron. Agric. 139, 22–32 (2017).
Steele-Dunne, S. C. et al. Radar remote sensing of agricultural canopies: a review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2249–2273 (2017).
Basso, B. & Liu, L. Seasonal crop yield forecast: methods, applications, and accuracies. Adv. Agron. 154, 201–255 (2019).
Johnson, D. M. An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 141, 116–128 (2014).
Peng, B., Guan, K., Pan, M. & Li, Y. Benefits of seasonal climate prediction and satellite data for forecasting US maize yield. Geophys. Res. Lett. 45, 9662–9671 (2018).
Jiang, H. et al. A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: a case study of the US Corn Belt at the county level. Glob. Change Biol. 26, 1754–1766 (2020).
Bolton, D. K. & Friedl, M. A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol. 173, 74–84 (2013).
Anderson, M. C. et al. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impacts. Remote Sens. Environ. 174, 82–99 (2016).
Enenkel, M. et al. Exploiting the convergence of evidence in satellite data for advanced weather index insurance design. Weather Clim. Soc. 11, 65–93 (2019).
Davenport, F. M. et al. Using out-of-sample yield forecast experiments to evaluate which earth observation products best indicate end of season maize yields. Environ. Res. Lett. 14, 124095 (2019).
Wang, H., Ghosh, A., Linquist, B. A. & Hijmans, R. J. Satellite-based observations reveal effects of weather variation on rice phenology. Remote Sens. 12, 1522 (2020).
Franch, B. et al. Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information. Remote Sens. Environ. 161, 131–148 (2015).
Funk, C. & Budde, M. E. Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sens. Environ. 113, 115–125 (2009).
Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl Acad. Sci. USA 111, E1327–E1333 (2014).
Sun, Y. et al. Overview of Solar-Induced chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory-2: retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sens. Environ. 209, 808–823 (2018).
Köhler, P. et al. Global retrievals of solar-induced chlorophyll fluorescence with TROPOMI: first results and intersensor comparison to OCO-2. Geophys. Res. Lett. 45, 10–456 (2018).
Guan, K. et al. Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence. Glob. Change Biol. 22, 716–726 (2016).
Song, L. et al. Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains. Glob. Change Biol. 24, 4023–4037 (2018).
Somkuti, P. et al. A new space-borne perspective of crop productivity variations over the US Corn Belt. Agric. For. Meteorol. 281, 107826 (2020).
He, L. et al. From the ground to space: using solar-induced chlorophyll fluorescence (SIF) to estimate crop productivity. Geophys. Res. Lett. 47, e2020GL087474 (2020).
Chaparro, D. et al. L-band vegetation optical depth seasonal metrics for crop yield assessment. Remote Sens. Environ. 212, 249–259 (2018).
Wiseman, G., McNairn, H., Homayouni, S. & Shang, J. RADARSAT-2 polarimetric SAR response to crop biomass for agricultural production monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 4461–4471 (2014).
Mateo-Sanchis, A. et al. Synergistic integration of optical and microwave satellite data for crop yield estimation. Remote Sens. Environ. 234, 111460 (2019).
Zhu, Z. & Woodcock, C. E. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: an algorithm designed specifically for monitoring land cover change. Remote Sens. Environ. 152, 217–234 (2014).
Jain, M. The benefits and pitfalls of using satellite data for causal inference. Rev. Environ. Econ. Policy 14, 157–169 (2020).
Whitcraft, A. K., Vermote, E. F., Becker-Reshef, I. & Justice, C. O. Cloud cover throughout the agricultural growing season: impacts on passive optical earth observations. Remote Sens. Environ. 156, 438–447 (2015).
Sudmanns, M., Tiede, D., Augustin, H. & Lang, S. Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass. Int. J. Digit. Earth 13, 768–784 (2019).
Gao, F., Masek, J., Schwaller, M. & Hall, F. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44, 2207–2218 (2006).
Houborg, R. & McCabe, M. F. A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data. Remote Sens. Environ. 209, 211–226 (2018).
Luo, Y., Guan, K. & Peng, J. STAIR: a generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product. Remote Sens. Environ. 214, 87–99 (2018).
Zhu, X., Cai, F., Tian, J. & Williams, T. K.-A. Spatiotemporal fusion of multisource remote sensing data: literature survey, taxonomy, principles, applications, and future directions. Remote Sens. 10, 527 (2018).
Veloso, A. et al. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sens. Environ. 199, 415–426 (2017).
Kenduiywo, B. K., Bargiel, D. & Soergel, U. Crop-type mapping from a sequence of Sentinel 1 images. Int. J. Remote Sens. 39, 6383–6404 (2018).
Scarpa, G., Gargiulo, M., Mazza, A. & Gaetano, R. A CNN-based fusion method for feature extraction from sentinel data. Remote Sens. 10, 236 (2018).
Forkuor, G., Conrad, C., Thiel, M., Ullmann, T. & Zoungrana, E. Integration of optical and Synthetic Aperture Radar imagery for improving crop mapping in Northwestern Benin, West Africa. Remote Sens. 6, 6472–6499 (2014).
Van Tricht, K., Gobin, A., Gilliams, S. & Piccard, I. Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping: a case study for Belgium. Remote Sens. 10, 1642 (2018).
Shuai, G. et al. Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image. Int. J. Appl. Earth Obs. Geoinf. 74, 1–15 (2019).
Fieuzal, R., Sicre, C. M. & Baup, F. Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks. Int. J. Appl. Earth Obs. Geoinf. 57, 14–23 (2017).
Ameline, M., Fieuzal, R., Betbeder, J., Berthoumieu, J.-F. & Baup, F. Estimation of corn yield by assimilating SAR and optical time series into a simplified agro-meteorological model: from diagnostic to forecast. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11, 4747–4760 (2018).
Hosseini, M. et al. Synthetic aperture radar and optical satellite data for estimating the biomass of corn. Int. J. Appl. Earth Obs. Geoinf. 83, 101933 (2019).
Bose, P., Kasabov, N. K., Bruzzone, L. & Hartono, R. N. Spiking neural networks for crop yield estimation based on spatiotemporal analysis of image time series. IEEE Trans. Geosci. Remote Sens. 54, 6563–6573 (2016).
Gandhi, N., Armstrong, L. J., Petkar, O. & Tripathy, A. K. in 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE) 1–5 (IEEE, 2016).
You, J., Li, X., Low, M., Lobell, D. & Ermon, S. in Thirty-First AAAI Conference on Artificial Intelligence (AAAI, 2017).
Cai, Y. et al. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agric. For. Meteorol. 274, 144–159 (2019).
Mann, M. L., Warner, J. M. & Malik, A. S. Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia. Clim. Change 154, 211–227 (2019).
Kaneko, A. et al. in International Conference on Machine Learning AI for Social Good Workshop (AI for Social Good, 2019).
Hobbs, A. & Svetlichnaya, S. Satellite-based prediction of forage conditions for livestock in Northern Kenya. arxiv https://arxiv.org/abs/2004.04081 (2020).
Chlingaryan, A., Sukkarieh, S. & Whelan, B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput. Electron. Agric. 151, 61–69 (2018).
Qin, Y. et al. in Proceedings of the 26th International Joint Conference on Artificial Intelligence 2627–2633 (IJCAI, 2017).
Kamilaris, A. & Prenafeta-Boldú, F. X. Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018).
Richetti, J. et al. Using phenology-based enhanced vegetation index and machine learning for soybean yield estimation in Paraná State, Brazil. J. Appl. Remote Sens. 12, 026029 (2018).
Zhang, L., Zhang, Z., Luo, Y., Cao, J. & Tao, F. Combining optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield in China using machine learning approaches. Remote Sens. 12, 21 (2020).
Challinor, A. J. et al. Improving the use of crop models for risk assessment and climate change adaptation. Agric. Syst. 159, 296–306 (2018).
Peng, B. et al. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nat. Plants 6, 338–348 (2020).
Sinclair, T. R. & Seligman, N. G. Crop modeling: from infancy to maturity. Agron. J. 88, 698–704 (1996).
Li, T. et al. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob. Change Biol. 21, 1328–1341 (2015).
Müller, C. et al. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).
Deryng, D., Conway, D., Ramankutty, N., Price, J. & Warren, R. Global crop yield response to extreme heat stress under multiple climate change futures. Environ. Res. Lett. 9, 034011 (2014).
Jin, Z. et al. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Glob. Change Biol. 22, 3112–3126 (2016).
Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E. & Peng, B. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the united states. Glob. Change Biol. 25, 2325–2337 (2019).
Bassu, S. et al. How do various maize crop models vary in their responses to climate change factors? Glob. Change Biol. 20, 2301–2320 (2014).
Martre, P. et al. Multimodel ensembles of wheat growth: many models are better than one. Glob. Change Biol. 21, 911–925 (2015).
Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).
Raftery, A. E., Madigan, D. & Hoeting, J. A. Bayesian model averaging for linear regression models. J. Am. Stat. Assoc. 92, 179–191 (1997).
Wöhling, T., Schöniger, A., Gayler, S. & Nowak, W. Bayesian model averaging to explore the worth of data for soil-plant model selection and prediction. Water Resour. Res. 51, 2825–2846 (2015).
Huang, J. et al. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agric. For. Meteorol. 216, 188–202 (2016).
Castañeda-Vera, A., Leffelaar, P. A., Álvaro-Fuentes, J., Cantero-Martínez, C. & Mínguez, M. I. Selecting crop models for decision making in wheat insurance. Eur. J. Agron. 68, 97–116 (2015).
Müller, C. et al. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset. Sci. Data 6, 50 (2019).
Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).
Pongratz, J. et al. Models meet data: challenges and opportunities in implementing land management in Earth system models. Glob. Change Biol. 24, 1470–1487 (2018).
Folberth, C. et al. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 11872 (2016).
Srivastava, A. K., Mboh, C. M., Gaiser, T., Webber, H. & Ewert, F. Effect of sowing date distributions on simulation of maize yields at regional scale–A case study in Central Ghana, West Africa. Agric. Syst. 147, 10–23 (2016).
Ceglar, A. et al. Improving WOFOST model to simulate winter wheat phenology in Europe: evaluation and effects on yield. Agric. Syst. 168, 168–180 (2019).
Zinyengere, N., Crespo, O., Hachigonta, S. & Tadross, M. Local impacts of climate change and agronomic practices on dry land crops in Southern Africa. Agric. Ecosyst. Environ. 197, 1–10 (2014).
Assefa, Y. et al. Yield responses to planting density for US modern corn hybrids: a synthesis-analysis. Crop. Sci. 56, 2802–2817 (2016).
Salo, T. J. et al. Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization. J. Agric. Sci. 154, 1218–1240 (2016).
Elliott, J. et al. Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl Acad. Sci. USA 111, 3239–3244 (2014).
Zaveri, E. & Lobell, D. B. The role of irrigation in changing wheat yields and heat sensitivity in India. Nat. Commun. 10, 4144 (2019).
Kubitza, C., Krishna, V. V., Schulthess, U. & Jain, M. Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review. Agron. Sustain. Dev. 40, 16 (2020).
Reinermann, S., Asam, S. & Kuenzer, C. Remote sensing of grassland production and management — a review. Remote Sens. 12, 1949 (2020).
De Wit, A. & Van Diepen, C. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agric. For. Meteorol. 146, 38–56 (2007).
Ines, A. V., Das, N. N., Hansen, J. W. & Njoku, E. G. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sens. Environ. 138, 149–164 (2013).
Andreadis, K. M. et al. The regional hydrologic extremes assessment system: a software framework for hydrologic modeling and data assimilation. PLoS ONE 12, e0176506 (2017).
Kang, Y. & Özdog˘an, M. Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach. Remote Sens. Environ. 228, 144–163 (2019).
Pagani, V. et al. A high-resolution, integrated system for rice yield forecasting at district level. Agric. Syst. 168, 181–190 (2019).
Nearing, G. S. et al. Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: an observing system simulation experiment. Water Resour. Res. 48, W05525 (2012).
Lobell, D. B., Thau, D., Seifert, C., Engle, E. & Little, B. A scalable satellite-based crop yield mapper. Remote Sens. Environ. 164, 324–333 (2015).
Azzari, G., Jain, M. & Lobell, D. B. Towards fine resolution global maps of crop yields: testing multiple methods and satellites in three countries. Remote Sens. Environ. 202, 129–141 (2017).
Jin, Z., Azzari, G. & Lobell, D. B. Improving the accuracy of satellite-based high-resolution yield estimation: a test of multiple scalable approaches. Agric. For. Meteorol. 247, 207–220 (2017).
Jin, Z. et al. Smallholder maize area and yield mapping at national scales with Google Earth Engine. Remote Sens. Environ. 228, 115–128 (2019).
Lobell, D. B. et al. Eyes in the sky, boots on the ground: assessing satellite-and ground-based approaches to crop yield measurement and analysis. Am. J. Agric. Econ. 102, 202–219 (2020).
Lobell, D. B. et al. Sight for sorghums: comparisons of satellite-and ground-based sorghum yield estimates in Mali. Remote Sens. 12, 100 (2020).
Leroux, L. et al. Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. Eur. J. Agron. 108, 11–26 (2019).
Karpatne, A. et al. Theory-guided data science: a new paradigm for scientific discovery from data. IEEE Trans. Knowl. Data Eng. 29, 2318–2331 (2017).
Read, J. S. et al. Process-guided deep learning predictions of lake water temperature. Water Resour. Res. 55, 9173–9190 (2019).
Ganguly, A. et al. Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques. Nonlin. Process. Geophys. 21, 777–795 (2014).
Jia, X. et al. in Proceedings of the 2019 SIAM International Conference on Data Mining 558–566 (SIAM, 2019).
Wang, N., Zhang, D., Chang, H. & Li, H. Deep learning of subsurface flow via theory-guided neural network. J. Hydrol. 584, 124700 (2020).
Yang, T. et al. Evaluation and machine learning improvement of global hydrological model-based flood simulations. Environ. Res. Lett. 14, 114027 (2019).
Columbia University. Using AI to better understand and model the Earth system: International research team wins major grant to support work combining machine learning with physical models of atmosphere and land to improve climate modeling and methods. Columbia University https://engineering.columbia.edu/news/ai-model-earth-system (2019).
Funk, C. et al. The climate hazards infrared precipitation with stations — a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
van Etten, J. et al. Crop variety management for climate adaptation supported by citizen science. Proc. Natl Acad. Sci. USA 116, 4194–4199 (2019).
Luciani, T. C., Distasio, B. A., Bungert, J., Sumner, M. & Bozzo, T. L. Use of drones to assist with insurance, financial and underwriting related activities. US Patent Application 14/843,455 (2016).
Yinka-Banjo, C. & Ajayi, O. Sky-farmers: Applications of unmanned aerial vehicles (UAV) in agriculture. IntechOpen https://www.intechopen.com/online-first/sky-farmers-applications-of-unmanned-aerial-vehicles-uav-in-agriculture (2019).
Food and Agriculture Organization of the United Nations. In East Africa, a race to outsmart locusts with drones and data. Food and Agriculture Organization of the United Nations http://www.fao.org/fao-stories/article/en/c/1270472/ (2020).
Food and Agriculture Organization of the United Nations. E-agriculture in action: drones for agriculture. Food and Agriculture Organization of the United Nations http://www.fao.org/3/I8494EN/i8494en.pdf (2018).
Benami, E. & Carter, M. R. Can digital technologies reshape rural microfinance? Implications for credit, insurance, and saving. Appl. Econ. Perspect. Policy http://dx.doi.org/10.1002/aepp.13151 (2021).
Hill, R. V., et al. Flexible insurance for heterogeneous farmers: Results from a small-scale pilot in Ethiopia. International Food Policy Research Institute https://www.ifpri.org/publication/flexible-insurance-heterogeneous-farmers (2011).
Smith, W. K., Fox, A. M., MacBean, N., Moore, D. J. & Parazoo, N. C. Constraining estimates of terrestrial carbon uptake: new opportunities using long-term satellite observations and data assimilation. New Phytol. 225, 105–112 (2020).
This work has benefited from research conducted under the auspices of the United States Agency for International Development (USAID) Feed the Future Innovation Lab for Markets, Risk and Resilience (grant no. 7200AA19LE00004), which M.R.C. directs and from which M.R.C., E.B. and A.H. have previously received funds. The contents are the responsibility of the authors and do not necessarily reflect the views of the USAID or the United States Government.
The authors declare no competing interests.
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- Loss-adjusted insurance
Insurance that requires the assessment of claims to issue payment.
- Moral hazard
A hazard that occurs when an insured individual takes actions that increase risk and make insurance payouts more likely.
- Adverse selection
A situation that insurers are prone to when only the riskiest subset of the population purchases insurance, such that pay-offs occur more frequently than they would if every person purchases insurance.
- Insurance zone
An area covered by a single index value.
The amount of money paid to have insurance coverage.
- Crop cuts
Preharvest crop yield estimates derived from visiting and physically harvesting and weighing a sample of production from a selection of fields.
- Design risk
The failure of the index to accurately capture average losses in the insurance zone.
- False negatives
The cases when no payout is triggered, despite some insured individuals experiencing losses; arises from design or idiosyncratic risk.
- False positives
The cases when the insurance index signals a loss and issues a payout, even though some insured individuals did not experience a loss.
- Basis risk
The risk that index insurance payments do not cover the losses experienced by an individual. Basis risk is the sum of the design risk and the idiosyncratic risk.
- Expected utility
A measure of anticipated future economic well-being that increases with expected income and, for a risk-averse person, decreases with the variance of income.
- Certainty equivalent
The amount of money that, if received for sure, would make a person indifferent between the sure money and a set of risky income prospects.
- Crop mask
A map that characterizes the extent and type of crops over a region, often derived from satellite imagery classification.
- Idiosyncratic risk
Risk that is specific to an individual and is uncorrelated with losses experienced by neighbours or others in the insurance zone.
The level of the index at which payouts begin to occur (for example, 90 mm of rainfall during planting season).
- Risk averse
Exhibiting the preference to give up some money in expectation in order to reduce variability.
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Benami, E., Jin, Z., Carter, M.R. et al. Uniting remote sensing, crop modelling and economics for agricultural risk management. Nat Rev Earth Environ 2, 140–159 (2021). https://doi.org/10.1038/s43017-020-00122-y
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