References
- Ackermann, N. 2014. Growing Stock Volume Estimation in Temperate Forested Areas Using a Fusion Approach with SAR Satellites Imagery. Cham: Springer.
- Asrar, G., Fuchs, M., Kanemasu, E., Hatfield, J. 1984. “Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat 1.” Agronomy journal, 76: pp. 300–306.
- Awad, M., and Khanna, R. 2015. “Support vector regression.” In Efficient Learning Machines, 67–80. Cham: Springer.
- Baghdadi, N., Boyer, N., Todoroff, P., El Hajj, M., and Bégué, A. 2009. “Potential of SAR Sensors Terrasar-X, Asar/Envisat and Palsar/Alos for monitoring sugarcane crops on Reunion Island.” Remote Sensing of Environment, Vol. 113(No. 8): pp. 1724–1738. doi:https://doi.org/10.1016/j.rse.2009.04.005.
- Betbeder, J., Fieuzal, R., and 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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9(No. 6): pp. 2540–2553. doi:https://doi.org/10.1109/JSTARS.2016.2541169.
- Breiman, L. 2001. “Random forests.” Machine Learning, Vol. 45(No. 1): pp. 5–32. doi:https://doi.org/10.1023/A:1010933404324.
- Campos-Taberner, M., Moreno-Martínez, Á., García-Haro, F.J., Camps-Valls, G., Robinson, N.P., Kattge, J., and Running, S.W. 2018. “Global estimation of biophysical variables from Google Earth Engine Platform.” Remote Sensing, Vol. 10(No. 8): pp. 1167. doi:https://doi.org/10.3390/rs10081167.
- Cortes, C., and Vapnik, V. 1995. “Support-vector networks.” Machine Learning, Vol. 20(No. 3): pp. 273–297. doi:https://doi.org/10.1007/BF00994018.
- Dangeti, P. 2017. Statistics for Machine Learning. Birmingham: Packt Publishing Ltd.
- Daughtry, C., Walthall, C., Kim, M., De Colstoun, E.B., and McMurtrey Iii, J. 2000. “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance.” Remote Sensing of Environment, Vol. 74(No. 2): pp. 229–229. doi:https://doi.org/10.1016/S0034-4257(00)00113-9.
- Fensholt, R., and Sandholt, I. 2003. “Derivation of a shortwave infrared water stress index from Modis near-and shortwave infrared data in a semiarid environment.” Remote Sensing of Environment, Vol. 87(No. 1): pp. 111–121. doi:https://doi.org/10.1016/j.rse.2003.07.002.
- Fieuzal, R., Baup, F., and Marais-Sicre, C. 2013. “Monitoring wheat and rapeseed by using synchronous optical and radar satellite data—From temporal signatures to crop parameters estimation.” Advances in Remote Sensing, Vol. 02(No. 2): pp. 162–180. doi:https://doi.org/10.4236/ars.2013.22020.
- Gao, B.-C. 1996. “Ndwi—A normalized difference water index for remote sensing of vegetation liquid water from space.” Remote Sensing of Environment, Vol. 58(No. 3): pp. 257–266. doi:https://doi.org/10.1016/S0034-4257(96)00067-3.
- Ghasemi, N., Sahebi, M.R., and Mohammadzadeh, A. 2011. “A review on biomass estimation methods using synthetic aperture radar Data.” International Journal of Geomatics and Geosciences, Vol. 1(No. 4): pp. 776–788.
- Gitelson, A.A., Gritz, Y., and Merzlyak, M.N. 2003. “Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves.” Journal of Plant Physiology, Vol. 160(No. 3): pp. 271–282. doi:https://doi.org/10.1078/0176-1617-00887.
- Gitelson, A.A., Kaufman, Y.J., and Merzlyak, M.N. 1996. “Use of a green channel in remote sensing of global vegetation from Eos-Modis.” Remote Sensing of Environment, Vol. 58(No. 3): pp. 289–298. doi:https://doi.org/10.1016/S0034-4257(96)00072-7.
- Harfenmeister, K., Spengler, D., and Weltzien, C. 2019. “Analyzing temporal and spatial characteristics of crop parameters using Sentinel-1 backscatter data.” Remote Sensing, Vol. 11(No. 13): pp. 1569. doi:https://doi.org/10.3390/rs11131569.
- Homayouni, S., McNairn, H., Hosseini, M., Jiao, X., and Powers, J. 2019. “Quad and compact multitemporal C-Band Polsar observations for crop characterization and monitoring.” International Journal of Applied Earth Observation and Geoinformation, Vol. 74: pp. 78–87. doi:https://doi.org/10.1016/j.jag.2018.09.009.
- Hornik, K., Stinchcombe, M., and White, H. 1989. “Multilayer feedforward networks are universal approximators.” Neural Networks, Vol. 2(No. 5): pp. 359–366. doi:https://doi.org/10.1016/0893-6080(89)90020-8.
- Hosseini, M., and McNairn, H. 2017. “Using multi-polarization C-and L-Band synthetic aperture radar to estimate biomass and soil moisture of wheat fields.” International Journal of Applied Earth Observation and Geoinformation, Vol. 58: pp. 50–64. doi:https://doi.org/10.1016/j.jag.2017.01.006.
- Hosseini, M., McNairn, H., Merzouki, A., and Pacheco, A. 2015. “Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C-and L-Band radar data.” Remote Sensing of Environment, Vol. 170 pp. 77–89. doi:https://doi.org/10.1016/j.rse.2015.09.002.
- Huang, G.-B. 2003. “Learning capability and storage capacity of two-hidden-layer feedforward networks.” IEEE Transactions on Neural Networks, Vol. 14(No. 2): pp. 274–281. no. doi:https://doi.org/10.1109/TNN.2003.809401.
- Huang, Y., Walker, J.P., Gao, Y., Wu, X., and Monerris, A. 2016. “Estimation of vegetation water content from the radar vegetation index at L-Band.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 54(No. 2): pp. 981–989. doi:https://doi.org/10.1109/TGRS.2015.2471803.
- Huete, A. 1988. “Huete, Ar a Soil-Adjusted Vegetation Index (Savi).” Remote Sensing of Environment, Vol. 25(No. 3): pp. 295–309. doi:https://doi.org/10.1016/0034-4257(88)90106-X.
- Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., and Ferreira, L.G. 2002. “Overview of the radiometric and biophysical performance of the Modis vegetation indices.” Remote Sensing of Environment, Vol. 83(No. 1–2): pp. 195–213. doi:https://doi.org/10.1016/S0034-4257(02)00096-2.
- Hunt, M.L., Blackburn, G.A., Carrasco, L., Redhead, J.W., and Rowland, C.S. 2019. “High resolution wheat yield mapping using Sentinel-2.” Remote Sensing of Environment, Vol. 233: pp. 111410. doi:https://doi.org/10.1016/j.rse.2019.111410.
- Inoue, Y., Kurosu, T., Maeno, H., Uratsuka, S., Kozu, T., Dabrowska-Zielinska, K., and Qi, J. 2002. “Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables.” Remote Sensing of Environment, Vol. 81(No. 2–3): pp. 194–204. doi:https://doi.org/10.1016/S0034-4257(01)00343-1.
- Jia, M., Tong, L., Zhang, Y., and Chen, Y. 2014. “Rice biomass estimation using radar backscattering data at S-Band.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7(No. 2): pp. 469–479. doi:https://doi.org/10.1109/JSTARS.2013.2282641.
- Jiao, X., Kovacs, J.M., Shang, J., McNairn, H., Walters, D., Ma, B., and Geng, X. 2014. “Object-oriented crop mapping and monitoring using multi-temporal polarimetric Radarsat-2 data.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 96: pp. 38–46. doi:https://doi.org/10.1016/j.isprsjprs.2014.06.014.
- Jiao, X., McNairn, H., Shang, J., Pattey, E., Liu, J., and Champagne, C. 2011. “The sensitivity of Radarsat-2 Polarimetric SAR data to corn and soybean Leaf Area Index.” Canadian Journal of Remote Sensing, Vol. 37(No. 1): pp. 69–81. doi:https://doi.org/10.5589/m11-023.
- Jin, Z., Azzari, G., You, C., Di Tommaso, S., Aston, S., Burke, M., and Lobell, D.B. 2019. “Smallholder maize area and yield mapping at national scales with Google Earth Engine.” Remote Sensing of Environment, Vol. 228 pp. 115–128. doi:https://doi.org/10.1016/j.rse.2019.04.016.
- Jin, X., Yang, G., Xu, X., Yang, H., Feng, H., Li, Z., Shen, J., Lan, Y., and Zhao, C. 2015. “Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and Radarsar-2 Data.” Remote Sensing, Vol. 7(No. 10): pp. 13251–13272. doi:https://doi.org/10.3390/rs71013251.
- Jordan, C.F. 1969. “Derivation of leaf‐area index from quality of light on the forest floor.” Ecology, Vol. 50(No. 4): pp. 663–666. doi:https://doi.org/10.2307/1936256.
- Kross, A., McNairn, H., Lapen, D., Sunohara, M., and Champagne, C. 2015. “Assessment of Rapideye vegetation indices for estimation of Leaf Area Index and biomass in corn and soybean crops.” International Journal of Applied Earth Observation and Geoinformation, Vol. 34 pp. 235–248. doi:https://doi.org/10.1016/j.jag.2014.08.002.
- Luntz, A. 1969. “On Estimation of characters obtained in statistical procedure of recognition.” Technicheskaya Kibernetica, Vol. 3
- Madhiarasan, M., and Deepa, S. 2017. “Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting.” Artificial Intelligence Review, Vol. 48(No. 4): pp. 449–471. doi:https://doi.org/10.1007/s10462-016-9506-6.
- Mahdianpari, M., Mohammadimanesh, F., McNairn, H., Davidson, A., Rezaee, M., Salehi, B., and Homayouni, S. J. 2019. “Mid-season crop classification using dual-, compact-, and full-polarization in preparation for the Radarsat Constellation Mission (RCM).” R. S, Vol. 11(No. 13): pp. 1582. doi:https://doi.org/10.3390/rs11131582.
- Mandal, D., Kumar, V., McNairn, H., Bhattacharya, A., and Rao, Y. 2019. “Joint estimation of Plant Area Index (PAI) and wet biomass in wheat and soybean from C-Band Polarimetric SAR Data.” International Journal of Applied Earth Observation and Geoinformation, Vol. 79: pp. 24–34. doi:https://doi.org/10.1016/j.jag.2019.02.007.
- Mandal, D., Kumar, V., Bhattacharya, A., Rao, Y., and McNairn, H. 2018. “CropBiophysical parameters estimation with a multi-target inversion scheme using the Sentinel-1 SAR Data.” Paper presented at the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. doi:https://doi.org/10.1109/IGARSS.2018.8518700.
- Mansaray, L.R., Zhang, K., and Kanu, A.S. 2020. “Dry biomass estimation of paddy rice with Sentinel-1a satellite data using machine learning regression algorithms.” Computers and Electronics in Agriculture, Vol. 176 pp. 105674. doi:https://doi.org/10.1016/j.compag.2020.105674.
- Mao, H., Meng, J., Ji, F., Zhang, Q., and Fang, H. 2019. “Comparison of machine learning regression algorithms for cotton Leaf Area Index retrieval using Sentinel-2 spectral bands.” Applied Sciences, Vol. 9(No. 7): pp. 1459. doi:https://doi.org/10.3390/app9071459.
- McNairn, H., Hochheim, K., and Rabe, N. 2004. “Applying polarimetric radar imagery for mapping the productivity of wheat crops.” Canadian Journal of Remote Sensing, Vol. 30(No. 3): pp. 517–524. doi:https://doi.org/10.5589/m03-068.
- McNairn, H., and Shang, J. 2016. “A review of multitemporal Synthetic Aperture Radar (SAR) for crop monitoring.” In Multitemporal Remote Sensing, 317–40. Cham: Springer.
- McNairn, H., Tom, J., Powers, J., Bélair, J., Berg, A., Bullock, A., Colliander, A., Cosh, A., Kim, M., and Ramata, S. 2016. “Experimental Plan SMAP Validation Experiment 2016 in Manitoba, Canada (Smapvex16-Mb).” http://smapvex16-mb.espaceweb.usherbrooke.ca/.
- Ouattara, B., Forkuor, G., Zoungrana, B.J., Dimobe, K., Danumah, J., Saley, B., and Tondoh, J. E. J. I. J. o R. S. 2020. “Crops monitoring and yield estimation using sentinel products in semi-arid smallholder irrigation schemes.” International Journal of Remote Sensing, Vol. 41(No. 17): pp. 6527–6549. no. doi:https://doi.org/10.1080/01431161.2020.1739355.
- Phan, T.N., Kuch, V., and Lehnert, L.W. 2020. “Land cover classification using Google Earth Engine and Random Forest Classifier—The role of image composition.” Remote Sensing, Vol. 12(No. 15): pp. 2411. doi:https://doi.org/10.3390/rs12152411.
- Punalekar, S.M., Verhoef, A., Quaife, T.L., Humphries, D., Bermingham, L., and Reynolds, C.K. 2018. “Application of Sentinel-2a data for pasture biomass monitoring using a physically based Radiative Transfer Model.” Remote Sensing of Environment, Vol. 218: pp. 207–220. doi:https://doi.org/10.1016/j.rse.2018.09.028.
- Reisi Gahrouei, O., McNairn, H., Hosseini, M., and Homayouni, S. 2020. “Estimation of crop biomass and Leaf Area Index from multitemporal and multispectral imagery using machine learning approaches.” Canadian Journal of Remote Sensing, Vol. 46(No. 1): pp. 84–99. doi:https://doi.org/10.1080/07038992.2020.1740584.
- Reisi-Gahrouei, O., Homayouni, S., McNairn, H., Hosseini, M., and Safari, A. 2019. “Crop biomass estimation using multi regression analysis and neural networks from multitemporal L-Band polarimetric synthetic aperture radar data.” International Journal of Remote Sensing, Vol. 40(No. 17): pp. 6822–6840. doi:https://doi.org/10.1080/01431161.2019.1594436.
- Richards, J. A. 2009. Remote Sensing with Imaging Radar. Vol. 1. Cham: Springer.
- Richardson, A.J., and Wiegand, C. 1977. “Distinguishing vegetation from soil background information.” Photogrammetric Engineering and Remote Sensing, Vol. 43(No. 12): pp. 1541–1552. no.
- Rondeaux, G., Steven, M., and Baret, F. 1996. “Optimization of soil-adjusted vegetation indices.” Remote Sensing of Environment, Vol. 55(No. 2): pp. 95–107. no. doi:https://doi.org/10.1016/0034-4257(95)00186-7.
- Rousel, J., Haas, R., Schell, J., and Deering, D. 1973. “Monitoring vegetation systems in the great plains with ERTS.” Paper presented at the Proceedings of the Third Earth Resources Technology Satellite—1 Symposium; NASA SP-351
- Sammut, C., and Webb, G. I. 2011. Encyclopedia of Machine Learning. Cham: Springer Science & Business Media.
- Sharifi, A., and Hosseingholizadeh, M. 2020. “Application of Sentinel-1 data to estimate height and biomass of rice crop in Astaneh-Ye Ashrafiyeh, Iran.” Journal of the Indian Society of Remote Sensing, Vol. 48(No. 1): pp. 11–19. doi:https://doi.org/10.1007/s12524-019-01057-8.
- Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., and Skakun, S. 2017. “Large scale crop classification using Google Earth Engine platform.” Paper presented at the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). doi:https://doi.org/10.1109/IGARSS.2017.8127801.
- Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., and Homayouni, S. 2020. “Support vector machine vs. random forest for remote sensing image classification: A meta-analysis and systematic review.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 13: pp. 6308–6325. doi:https://doi.org/10.1109/JSTARS.2020.3026724.
- Thieme, A., Yadav, S., Oddo, P.C., Fitz, J.M., McCartney, S., King, L., Keppler, J., McCarty, G.W., and Hively, W.D. 2020. “Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay Watershed.” Remote Sensing of Environment, Vol. 248: pp. 111943. doi:https://doi.org/10.1016/j.rse.2020.111943.
- Tian, H., Meng, M., Wu, M., and Niu, Z. 2019. “Mapping Spring Canola and Spring Wheat Using Radarsat-2 and Landsat-8 Images with Google Earth Engine.” Current Science, Vol. 116(No. 2): pp. 291–298. doi:https://doi.org/10.18520/cs/v116/i2/291-298.
- Vasilev, I., Slater, D., Spacagna, G., Roelants, P., and Zocca, V. 2019. Python deep learning: Exploring deep learning techniques and neural network architectures with Pytorch, Keras, and Tensorflow. Birmingham: Packt Publishing Ltd.
- Venkatappa, M., Sasaki, N., Anantsuksomsri, S., and Smith, B. 2020. “Applications of the Google Earth Engine and phenology-based threshold classification method for mapping forest cover and carbon stock changes in SIEM Reap Province, Cambodia.” Remote Sensing, Vol. 12(No. 18): pp. 3110. doi:https://doi.org/10.3390/rs12183110.
- Vincini, M., Frazzi, E., and D’Alessio, P. 2008. “A Broad-Band Leaf Chlorophyll Vegetation Index at the Canopy Scale.” Precision Agriculture, Vol. 9(No. 5): pp. 303–319. no. doi:https://doi.org/10.1007/s11119-008-9075-z.
- Wang, J., Xiao, X., Bajgain, R., Starks, P., Steiner, J., Doughty, R.B., and Chang, Q. 2019. “Estimating Leaf Area Index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 154: pp. 189–201. doi:https://doi.org/10.1016/j.isprsjprs.2019.06.007.
- Wiseman, G., McNairn, H., Homayouni, S., and Shang, J. 2014. “Radarsat-2 Polarimetric SAR response to crop biomass for agricultural production monitoring.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7(No. 11): pp. 4461–4471. doi:https://doi.org/10.1109/JSTARS.2014.2322311.
- Wu, F., Wang, C., Zhang, H., Zhang, B., and Tang, Y. 2011. “Rice crop monitoring in South China with Radarsat-2 Quad-Polarization SAR Data.” IEEE Geoscience and Remote Sensing Letters, Vol. 8(No. 2): pp. 196–200. doi:https://doi.org/10.1109/LGRS.2010.2055830.
- Xiong, J., Thenkabail, P.S., Gumma, M.K., Teluguntla, P., Poehnelt, J., Congalton, R.G., Yadav, K., and Thau, D. 2017. “Automated cropland mapping of continental Africa Using Google Earth Engine cloud computing.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 126 pp. 225–244. doi:https://doi.org/10.1016/j.isprsjprs.2017.01.019.
- Zeng, H., Wu, B., Wang, S., Musakwa, W., Tian, F., Mashimbye, Z.E., Poona, N., and Syndey, M. 2020. “A synthesizing land-cover classification method based on Google Earth Engine: A case study in Nzhelele and Levhuvu Catchments, South Africa.” Chinese Geographical Science, Vol. 30(No. 3): pp. 397–409. doi:https://doi.org/10.1007/s11769-020-1119-y.
- Zhang, X., Long, T., He, G., and Guo, Y. 2019. “Gobal forest cover mapping using Landsat and Google Earth Engine cloud computing.” Paper presented at the 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). doi:https://doi.org/10.1109/Agro-Geoinformatics.2019.8820469.
- Zhu, G., Ju, W., Chen, J., and Liu, Y. 2014. “A Novel Moisture Adjusted Vegetation Index (MAVI) to reduce background reflectance and topographical effects on LAI retrieval.” PLoS One, Vol. 9(No. 7): pp. e102560. doi:https://doi.org/10.1371/journal.pone.0102560.