Synergy of optical and synthetic aperture radar data for early-stage crop yield estimation: a case study over a state of Germany

References

• Alexakis DD, Mexis FD, Vozinaki AE, Daliakopoulos IN, Tsanis IK. 2017. Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approach. Sensors. 17(6):1455.
   PubMed Web of Science ®Google Scholar
• Ameline M, Fieuzal R, Betbeder J, Berthoumieu J-F, Baup F. 2018. 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(12):4747–4760.
   Web of Science ®Google Scholar
• Ayala-Silva T, Beyl CA. 2005. Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency. Adv Space Res. 35(2):305–317.
   PubMed Web of Science ®Google Scholar
• Bargiel D. 2017. A new method for crop classification combining time series of radar images and crop phenology information. Remote Sens Environ. 198:369–383.
   Web of Science ®Google Scholar
• Betbeder J, Fieuzal R, Baup F. 2016. Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield. IEEE J Sel Top Appl Earth Obs Remote Sens. 9(6):2540–2553.
   Web of Science ®Google Scholar
• Bould C. 1965. Soil and plant nutrient content in relation to crop yield. Proc Nutr Soc. 24(1):21–29.
   Web of Science ®Google Scholar
• Breiman L. 2001. Random forests. Mach Learn. 45:5–32.
   Web of Science ®Google Scholar
• Cai Y, Guan K, Lobell D, Potgieter AB, Wang S, Peng J, Xu T, Asseng S, Zhang Y, You L, et al. 2019. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agric For Meteorol. 274:144–159.
   Web of Science ®Google Scholar
• Chen D, Huang J, Jackson TJ. 2005. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near-and short-wave infrared bands. Remote Sens Environ. 98(2–3):225–236.
   Web of Science ®Google Scholar
• Choudhary KK, Pandey V, Murthy CS, Poddar MK. 2019. Synergetic use of optical, microwave and thermal satellite data for non-parametric estimation of wheat grain yield. Int Arch Photogramm Remote Sens Spat Inf Sci. 42:195–199.
   Google Scholar
• Christensen LK, Jørgensen RN. 2003. Spatial reflectance at sub-leaf scale discriminating NPK stress characteristics in barley using multiway partial least squares regression. 2003 ASAE Annual Meeting, Vol. 1. American Society of Agricultural and Biological Engineers.
   Google Scholar
• Dong H, Kong X, Li W, Tang W, Zhang D. 2010. Effects of plant density and nitrogen and potassium fertilization on cotton yield and uptake of major nutrients in two fields with varying fertility. Field Crops Res. 119(1):106–113.
   Web of Science ®Google Scholar
• ESA. 2019. Science toolbox exploration platform. https://step.esa.int/main/snap-7-0-released/. [Accessed 2019 June].
   Google Scholar
• Fieuzal R, Baup F. 2017. Forecast of wheat yield throughout the agricultural season using optical and radar satellite images. Int J Appl Earth Obs Geoinf. 59:147–156.
   Web of Science ®Google Scholar
• Fieuzal R, Sicre CM, Baup F. 2017a. 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.
   Web of Science ®Google Scholar
• Fieuzal R, Sicre CM, Baup F. 2017b. Estimation of sunflower yield using a simplified agrometeorological model controlled by optical and SAR satellite data. IEEE J Sel Top Appl Earth Obs Remote Sens. 10(12):5412–5422.
   Web of Science ®Google Scholar
• Fuehring HD, Mazaheri A, Bybordi M, Khan AKS. 1966. Effect of soil moisture depletion on crop yield and stomatal infiltration 1. Agron J. 58(2):195–198.
   Web of Science ®Google Scholar
• Gao B-C. 1996. NDWI – a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ. 58(3):257–266.
   Web of Science ®Google Scholar
• Ghosh P, Mandal D, Bhattacharya A, Nanda MK, Bera S. 2018. Assessing crop monitoring potential of Sentinel-2 in a spatio-temporal scale. Int Arch Photogramm Remote Sens Spatial Inf Sci. 42:227–231.
   Google Scholar
• Gitelson AA, Gritz Y, Merzlyak MN. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol. 160(3):271–282.
   PubMed Web of Science ®Google Scholar
• Gitelson AA, Kaufman YJ, Merzlyak MN. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ. 58(3):289–298.
   Web of Science ®Google Scholar
• Gitelson AA, Keydan GP, Merzlyak MN. 2006. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys Res Lett. 33(11).
   PubMed Web of Science ®Google Scholar
• Gitelson AA, Merzlyak MN. 1997. Remote estimation of chlorophyll content in higher plant leaves. Int J Remote Sens. 18(12):2691–2697.
   Web of Science ®Google Scholar
• Gitelson AA, Merzlyak MN, Chivkunova OB. 2001. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem Photobiol. 74(1):38–45.
   PubMed Web of Science ®Google Scholar
• Gitelson AA, Zur Y, Chivkunova OB, Merzlyak MN. 2002. Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochem Photobiol. 75(3):272–281.
   PubMed Web of Science ®Google Scholar
• Guan K, Wu J, Kimball JS, Anderson MC, Frolking S, Li B, Hain CR, Lobell DB. 2017. 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.
   Web of Science ®Google Scholar
• Hachani A, Ouessar M, Paloscia S, Santi E, Pettinato S. 2019. Soil moisture retrieval from Sentinel-1 acquisitions in an arid environment in Tunisia: application of artificial neural networks techniques. Int J Remote Sens. 40(24):9159–9180.
   Web of Science ®Google Scholar
• Hosseini M, McNairn H, Mitchell S, Dingle Robertson L, Davidson A, Homayouni S. 2019. Synthetic aperture radar and optical satellite data for estimating the biomass of corn. Int J Appl Earth Obs Geoinf. 83:101933.
   Web of Science ®Google Scholar
• Hunt ML, Blackburn GA, Carrasco L, Redhead JW, Rowland CS. 2019. High resolution wheat yield mapping using Sentinel-2. Remote Sens Environ. 233:111410.
   Web of Science ®Google Scholar
• Jackson TJ, Chen D, Cosh M, Li F, Anderson M, Walthall C, Doriaswamy P, Hunt ER. 2004. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sens Environ. 92(4):475–482.
   Web of Science ®Google Scholar
• Jin X, Li Z, Yang G, Yang H, Feng H, Xu X, Wang J, Li X, Luo J. 2017. Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm. ISPRS J Photogramm Remote Sens. 126:24–37.
   Web of Science ®Google Scholar
• Kira O, Linker R, Gitelson A. 2015. Non-destructive estimation of foliar chlorophyll and carotenoid contents: focus on informative spectral bands. Int J Appl Earth Obs Geoinf. 38:251–260.
   Web of Science ®Google Scholar
• Liu L, Wang J, Bao Y, Huang W, Ma Z, Zhao C. 2006. Predicting winter wheat condition, grain yield and protein content using multi-temporal EnviSat-ASAR and Landsat TM satellite images. Int J Remote Sens. 27(4):737–753.
   Web of Science ®Google Scholar
• Mandal D, Bhattacharya A, Rao YS. 2021. Radar remote sensing for crop biophysical parameter estimation. Singapore: Springer.
   Google Scholar
• Mandal D, Kumar V, McNairn H, Bhattacharya A, Rao YS. 2019. Joint estimation of Plant Area Index (PAI) and wet biomass in wheat and soybean from C-band polarimetric SAR data. Int J Appl Earth Obs Geoinf. 79:24–34.
   Web of Science ®Google Scholar
• Mandal D, Rao YS. 2020. SASYA: An integrated framework for crop biophysical parameter retrieval and within-season crop yield prediction with SAR remote sensing data. Remote Sens Appl: Soc Environ. 20:100366.
   Google Scholar
• Masoni A, Ercoli L, Mariotti M. 1996. Spectral properties of leaves deficient in iron, sulfur, magnesium, and manganese. Agron J. 88(6):937–943.
   Web of Science ®Google Scholar
• McNairn H, Champagne C, Shang J, Holmstrom D, Reichert G. 2009. Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS J Photogramm Remote Sens. 64(5):434–449.
   Web of Science ®Google Scholar
• Mee CY, Siva KB, Ahmad HM. 2017. Detecting and monitoring plant nutrient stress using remote sensing approaches: a review. Asian J Plant Sci. 16(1):1–8.
   Google Scholar
• Meier U. 1997. Growth stages of mono-and dicotyledonous plants. Berlin: Blackwell Wissenschafts-Verlag.
   Google Scholar
• Mkhabela MS, Bullock P, Raj S, Wang S, Yang Y. 2011. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agric For Meteorol. 151(3):385–393.
   Web of Science ®Google Scholar
• Mkhabela MS, Mkhabela MS, Mashinini NN. 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(1–2):1–9.
   Web of Science ®Google Scholar
• Muthy CS, Jonna S, Raju PV. 1994. Crop yield prediction in command area using satellite data. GISdevelopment.net, AARS, ACRS.
   Google Scholar
• Pantazi XE, Moshou D, Alexandridis T, Whetton RL, Mouazen AM. 2016. Wheat yield prediction using machine learning and advanced sensing techniques. Comput Electron Agric. 121:57–65.
   Web of Science ®Google Scholar
• Peñuelas J, Filella I. 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci. 3(4):151–156.
   Web of Science ®Google Scholar
• Ponzoni FJ, De JL, Goncalves M. 1999. Spectral features associated with nitrogen, phosphorus, and potassium deficiencies in Eucalyptus saligna seedling leaves. Int J Remote Sens. 20(11):2249–2264.
   Web of Science ®Google Scholar
• Prasad AK, Chai L, Singh RP, Kafatos M. 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. Int J Appl Earth Obs Geoinf. 8(1):26–33.
   Web of Science ®Google Scholar
• Rashidi M, Seyfi K. 2007. Effect of water stress on crop yield and yield components of cantaloupe. Int J Agric Biol. 9:271–273.
   Google Scholar
• Ren J, Chen Z, Zhou Q, Tang H. 2008. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. Int J Appl Earth Obs Geoinf. 10(4):403–413.
   Web of Science ®Google Scholar
• Richardson AD, Duigan SP, Berlyn GP. 2002. An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist. 153(1):185–194.
   Web of Science ®Google Scholar
• Rouse JW Jr, Haas RH, Deering DW, Schell JA, Harlan JC. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec Publ. 351:309.
   Google Scholar
• Seo B, Lee J, Lee K-D, Hong S, Kang S. 2019. Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA. Field Crops Res. 238:113–128.
   Web of Science ®Google Scholar
• Setiyono T, Quicho E, Gatti L, Campos-Taberner M, Busetto L, Collivignarelli F, García-Haro F, Boschetti M, Khan N, Holecz F. 2018. Spatial rice yield estimation based on MODIS and Sentinel-1 SAR data and ORYZA crop growth model. Remote Sens. 10(2):293.
   Google Scholar
• Shrestha R, Di L, Eugene GY, Kang L, Shao Y-z, Bai Y-q. 2017. Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer. J Integr Agric. 16(2):398–407.
   Web of Science ®Google Scholar
• Simova-Stoilova L, Stoyanova Z, Demirevska-Kepova K. 2001. Ontogenic changes in leaf pigments, total soluble protein and Rubisco in two barley varieties in relation to yield. Bulg J Plant Physiol. 27(1–2):15–24.
   Google Scholar
• Sims DA, Gamon JA. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ. 81(2–3):337–354.
   Web of Science ®Google Scholar
• Soria-Ruiz J, Fernandez-Ordonez Y, McNairn H, Pei-Gee PH. 2009. Corn monitoring and crop yield using optical and microwave remote sensing. In: Pei-Gee Peter H, editor. Geoscience and remote sensing. London: IntechOpen; p. 598.
   Google Scholar
• Thornton PK, Bowen WT, Ravelo AC, Wilkens PW, Farmer G, Brock J, Brink JE. 1997. Estimating millet production for famine early warning: an application of crop simulation modelling using satellite and ground-based data in Burkina Faso. Agric For Meteorol. 83(1–2):95–112.
   Web of Science ®Google Scholar
• Thornton PK, Wilkens PW. 1998. Risk assessment and food security. In: Gordon YT, Gerrit H, Philip KT, editors. Understanding options for agricultural production. New York: Springer; p. 329–345.
   Google Scholar
• Tumbo SD, Wagner DG, Heinemann PH. 2002. Hyperspectral-based neural network for predicting chlorophyll status in corn. Trans ASAE. 45(3):825.
   Google Scholar
• Ustin SL, Gitelson AA, Jacquemoud S, Schaepman M, Asner GP, Gamon JA, Zarco-Tejada P. 2009. Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sens Environ. 113:S67–S77.
   Web of Science ®Google Scholar
• Vose MD. 1999. The simple genetic algorithm: foundations and theory. Massachusetts: MIT Press.
   Google Scholar
• Vreugdenhil M, Wagner W, Bauer-Marschallinger B, Pfeil I, Teubner I, Rüdiger C, Strauss P. 2018. Sensitivity of Sentinel-1 backscatter to vegetation dynamics: an Austrian case study. Remote Sens. 10(9):1396.
   Google Scholar
• Wall L, Larocque D, Léger P-M. 2008. The early explanatory power of NDVI in crop yield modelling. Int J Remote Sens. 29(8):2211–2225.
   Web of Science ®Google Scholar
• Wiseman G, McNairn H, Homayouni S, Shang J. 2014. RADARSAT-2 polarimetric SAR response to crop biomass for agricultural production monitoring. IEEE J Sel Top Appl Earth Obs Remote Sens. 7(11):4461–4471.
   Web of Science ®Google Scholar
• Wood CW, Reeves DW, Himelrick DG. 1993. Relationships between chlorophyll meter readings and leaf chlorophyll concentration, N status, and crop yield: a review. Proc Agron Soc N Z. 23:1–9.
   Google Scholar
• Wu X, Tang Y, Li C, Wu C, Huang G. 2015. Chlorophyll fluorescence and yield responses of winter wheat to waterlogging at different growth stages. Plant Prod Sci. 18(3):284–294.
   Web of Science ®Google Scholar
• Yao X, Zhu Y, Tian Y, Feng W, Cao W. 2010. Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. Int J Appl Earth Obs Geoinf. 12(2):89–100.
   Web of Science ®Google Scholar
• Zhang H, Oweis T. 1999. Water–yield relations and optimal irrigation scheduling of wheat in the Mediterranean region. Agric Water Manage. 38(3):195–211.
   Web of Science ®Google Scholar
• Zhang X, Liu F, He Y, Gong X. 2013. Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging. Biosyst Eng. 115(1):56–65.
   Web of Science ®Google Scholar
• Zhang XJ, Li MZ. 2008. Analysis and estimation of the phosphorus content in cucumber leaf in greenhouse by spectroscopy. Guang Pu Xue Yu Guang Pu Fen Xi. 28(10):2404–2408.
   PubMedGoogle Scholar
• Zhuo W, J, Huang Li Li R, Huang X, Gao X, Zhang D. Zhu 2018. Assimilating SAR and optical remote sensing data into WOFOST model for improving winter wheat yield estimation. 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics). IEEE. p. 1–5.
   Google Scholar