Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices

European Journal of Agronomy - Tập 108 - Trang 11-26 - 2019
Louise Leroux1,2, Mathieu Castets3,4, Christian Baron3,4, Maria-Jose Escorihuela5, Agnès Bégué3,4, Danny Lo Seen3,4
1CIRAD, UPR AIDA, Dakar, Senegal
2AIDA, Univ Montpellier, CIRAD, Montpellier, France
3CIRAD, UMR TETIS, F-34398, Montpellier, France
4TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, IRSTEA, Montpellier, France
5isardSAT, Advanced Industry Park, Carrer Marie Curie 8-14, 08042 Barcelona, Spain

Tài liệu tham khảo

Adiku, 2015, Climate change impacts on West African agriculture: an integrated regional assessment (CIWARA), 25 Akponikpè, 2011, Spatial fields’ dispersion as a farmer strategy to reduce agro-climatic risk at the household level in pearl millet-based systems in the Sahel : a modeling perspective, Agric. For. Meteorol., 151, 215, 10.1016/j.agrformet.2010.10.007 Akumaga, 2017, Validation and testing of the FAO AquaCrop model under different levels of nitrogen fertilizer on rainfed maize in Nigeria, West Africa, Agric. For. Meteorol., 232, 225, 10.1016/j.agrformet.2016.08.011 Allé, 2014, Choice and risks of management strategies of agricultural calendar: application to the maize cultivation in south Benin, Int. J. Innov. Appl. Stud., 7, 1137 Azzari, 2017, Towards fine resolution global maps of crop yields: testing multiple methods and satellites in three countries, Remote Sens. Environ., 10.1016/j.rse.2017.04.014 Baron, 2005, From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact, Philos. Trans. R. Soc. Lond., B, Biol. Sci., 360, 2095, 10.1098/rstb.2005.1741 Bassu, 2014, How do various maize crop models vary in their responses to climate change factors?, Glob. Change Biol., 20, 2301, 10.1111/gcb.12520 Becker-Reshef, 2010, A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data, Remote Sens. Environ., 114, 1312, 10.1016/j.rse.2010.01.010 Bolton, 2013, Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics, Agric. For. Meteorol., 173, 74, 10.1016/j.agrformet.2013.01.007 Breiman, 2001, Random forest, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Burke, 2017, Satellite-based assessment of yield variation and its determinants in smallholder African systems, Proc. Natl. Acad. Sci. U. S. A., 114, 2189, 10.1073/pnas.1616919114 Chakrabarti, 2014, Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 3867, 10.1109/JSTARS.2014.2315999 Chivasa, 2017, Application of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapes: a review, Int. J. Remote Sens., 38, 6816, 10.1080/01431161.2017.1365390 Degenne, 2016, Ocelet: simulating processes of landscape changes using interaction graphs, SoftwareX, 5, 89, 10.1016/j.softx.2016.05.002 Diarisso, 2015, Biomass transfers and nutrient budgets of the agro-pastoral systems in a village territory in south-western Burkina Faso, Nutr. Cycl. Agroecosyst., 101, 295, 10.1007/s10705-015-9679-4 Didan, 2015 Dingkuhn, 2003, Decision support tools for rainfed crops in the Sahel at the plot and regional scales, 127 Drusch, 2012, Sentinel-2: ESA’s optical high-resolution mission for GMES operational services, Remote Sens. Environ., 120, 25, 10.1016/j.rse.2011.11.026 Duncan, 2015, Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing, Glob. Change Biol., 21, 1541, 10.1111/gcb.12660 Durand, 2018, How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield?, Eur. J. Agron., 100, 67, 10.1016/j.eja.2017.01.002 Eyshi Rezaei, 2015, Heat stress in cereals: mechanisms and modelling, Eur. J. Agron., 64, 98, 10.1016/j.eja.2014.10.003 FAO/IIASA/ISRIC/ISSCAS/JRC, 2012 Fieuzal, 2017, Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks, Int. J. Appl. Earth Obs. Geoinf., 57, 14, 10.1016/j.jag.2016.12.011 Forkuor, 2017, Multiscale remote sensing to map the spatial distribution and extent of cropland in the Sudanian Savanna of West Africa, Remote Sens., 2017 Fritz, 2015, Mapping global cropland and field size, Glob. Change Biol., 21, 1, 10.1111/gcb.12838 Gaetano, 2016, Presentation of the Burkina Faso (Koumbia) site activities, JECAM/GEOGLAM Science Meeting Grömping, 2006, Relative importance for linear regression in r: the package relaimpo, J. Stat. Softw., 17, 1, 10.18637/jss.v017.i01 Groten, 1993, NDVI—crop monitoring and early yield assessment of Burkina Faso, Int. J. Remote Sens., 14, 1495, 10.1080/01431169308953983 Gruhier, 2010, Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site, Hydrol. Earth Syst. Sci., 14, 141, 10.5194/hess-14-141-2010 Guan, 2015, What aspects of future rainfall changes matter for crop yields in West Africa?, Geophys. Res. Lett., 42, 8001, 10.1002/2015GL063877 Guan, 2017, Assessing climate adaptation options and uncertainties for cereal systems in West Africa, Agric. For. Meteorol., 232, 291, 10.1016/j.agrformet.2016.07.021 Guillemot, 2016 Holzman, 2014, Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index, Int. J. Appl. Earth Obs. Geoinf., 28, 181, 10.1016/j.jag.2013.12.006 Jackson, 1981, Canopy temperature as a crop water stress indicator, Water Resour. Res., 17, 1133, 10.1029/WR017i004p01133 Jeong, 2016, Random forests for global and regional crop yield predictions, PLoS One, 11, 10.1371/journal.pone.0156571 Jin, 2017, Mapping smallholder yield heterogeneity at multiple scales in Eastern Africa, Remote Sens., 9, 931, 10.3390/rs9090931 Johnson, 2014, 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, 10.1016/j.rse.2013.10.027 Johnson, 2016, Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods, Agric. For. Meteorol., 218–219, 74, 10.1016/j.agrformet.2015.11.003 Kerr, 2010, The SMOS mission: new tool for monitoring key elements ofthe global water cycle, Proc. IEEE, 98, 666, 10.1109/JPROC.2010.2043032 Kogan, 1995, Application of vegetation index and brightness temperature for drought detection, Adv. Space Res., 15, 91, 10.1016/0273-1177(95)00079-T Lebourgeois, 2017, A combined random forest and OBIA classification scheme for mapping smallholder agriculture at different nomenclature levels using multisource data (Simulated Sentinel-2 time series, VHRS and DEM), Remote Sens., 9, 259, 10.3390/rs9030259 Leroux, 2016, Crop monitoring using vegetation and thermal indices for yield estimates: case study of a rainfed cereal in semi-arid West Africa, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 347, 10.1109/JSTARS.2015.2501343 Liaw, 2002, Classification and regression by randomForest, R news, 2, 18 Lobell, 2011, Climate trends and global crop production since 1980, Science, 333, 616, 10.1126/science.1204531 Lobell, 2011, Nonlinear heat effects on African maize as evidenced by historical yield trials, Nat. Clim. Chang., 1, 42, 10.1038/nclimate1043 Lobell, 2015, A scalable satellite-based crop yield mapper, Remote Sens. Environ., 164, 324, 10.1016/j.rse.2015.04.021 Marteau, 2011, The onset of the rainy season and farmers’ sowing strategy for pearl millet cultivation in Southwest Niger, Agric. For. Meteorol., 151, 1356, 10.1016/j.agrformet.2011.05.018 Maselli, 2000, Processing of GAC NDVI data for yield forecasting in the Sahelian region, Int. J. Remote Sens., 21, 3509, 10.1080/014311600750037525 Merlin, 2010, An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data, Remote Sens. Environ., 114, 2305, 10.1016/j.rse.2010.05.007 Meroni, 2013, Remote sensing based yield estimation in a stochastic framework — case study of durum wheat in Tunisia, Remote Sens., 5, 539, 10.3390/rs5020539 Mkhabela, 2005, Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA’s-AVHRR, Agric. For. Meteorol., 129, 1, 10.1016/j.agrformet.2004.12.006 Oettli, 2011, Are regional climate models relevant for crop yield prediction in West Africa?, Environ. Res. Lett., 6, 014008, 10.1088/1748-9326/6/1/014008 Pérez-Hoyos, 2017, Comparison of global land cover datasets for cropland monitoring, Remote Sens., 9, 1118, 10.3390/rs9111118 R Core Team, 2018 Rasmussen, 1992, Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR, Int. J. Remote Sens., 13, 3431, 10.1080/01431169208904132 Rasmussen, 1998, Developing simple, operational, consistent NDVI-vegetation models by applying environmental and climatic information. Part II: crop yield assessment, Int. J. Remote Sens., 19, 119, 10.1080/014311698216468 Ray, 2012, Recent patterns of crop yield growth and stagnation, Nat. Commun., 3, 1293, 10.1038/ncomms2296 Ray, 2013, Yield trends are insufficient to double global crop production by 2050, PLoS One, 8, 10.1371/journal.pone.0066428 Roudier, 2016, Assessing the benefits of weather and seasonal forecasts to millet growers in Niger, Agric. For. Meteorol., 223, 168, 10.1016/j.agrformet.2016.04.010 Sánchez, 2016, A new soil moisture agricultural drought index (SMADI) integrating MODIS and SMOS products: a case of study over the Iberian Peninsula, Remote Sens., 8, 1, 10.3390/rs8040287 Sandholt, 2002, A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status, Remote Sens. Environ., 79, 213, 10.1016/S0034-4257(01)00274-7 Shiferaw, 2011, Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security, Food Secur., 3, 307, 10.1007/s12571-011-0140-5 Sibley, 2014, Testing remote sensing approaches for assessing yield variability among maize fields, Agron. J., 106, 24, 10.2134/agronj2013.0314 Siebert, 2014, Impact of heat stress on crop yield—on the importance of considering canopy temperature, Environ. Res. Lett., 9, 1, 10.1088/1748-9326/9/4/044012 Son, 2012, Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data, Int. J. Appl. Earth Obs. Geoinf., 18, 417, 10.1016/j.jag.2012.03.014 Sultan, 2005, Agricultural impacts of large-scale variability of the West African monsoon, Agric. For. Meteorol., 128, 93, 10.1016/j.agrformet.2004.08.005 Sultan, 2013, Assessing climate change impacts on sorghum and millet yields in the Sudanian and Sahelian savannas of West Africa, Environ. Res. Lett., 8, 9, 10.1088/1748-9326/8/1/014040 Sultan, 2014, Robust features of future climate change impacts on sorghum yields in West Africa, Environ. Res. Lett., 9, 13, 10.1088/1748-9326/9/10/104006 Tarnavsky, 2014, Extension of the TAMSAT satellite-based rainfall monitoring over Africa and from 1983 to present, J. Appl. Meteorol. Climatol., 53, 2805, 10.1175/JAMC-D-14-0016.1 Traoré, 2011, Characterizing and modeling the diversity of cropping situations under climatic constraints in West Africa, Atmos. Sci. Lett., 12, 89, 10.1002/asl.295 Tucker, 1985, Satellite remote sensing of total herbaceous biomass production in the senegalese sahel : 1980-1984, Remote Sens. Environ., 17, 233, 10.1016/0034-4257(85)90097-5 Tucker, 1980, Relationship of spectral data to grain yield variation, Photogramm. Eng. Remote Sens., 46, 657 Unganai, 1998, Drought monitoring and corn yield estimation in Southern Africa from AVHRR Data, Remote Sens. Environ., 63, 219, 10.1016/S0034-4257(97)00132-6 Vintrou, 2014, A comparative study on satellite and model-based crop phenology in West Africa, Remote Sens., 6, 1367, 10.3390/rs6021367 Wan, 2015 Zuur, 2010, A protocol for data exploration to avoid common statistical problems, Methods Ecol. Evol., 1, 3, 10.1111/j.2041-210X.2009.00001.x