Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models

References

• Abdi AM. 2019. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GISci Remote Sens. 57:1–20.
   Web of Science ®Google Scholar
• Aghighi H, Azadbakht M, Ashourloo D, Shahrabi HS, Radiom S. 2018. Machine learning regression techniques for the silage maize yield prediction using time-series images of Landsat 8 OLI. IEEE J Sel Top Appl Earth Obs Remote Sens. 11(12):4563–4577.
   Web of Science ®Google Scholar
• Altman NS. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. Am Statist. 46(3):175–185.
   Web of Science ®Google Scholar
• Breiman L. 1997. Arcing the edge. Statistics Department, University of California at Berkeley. Technical Report 486.
   Google Scholar
• Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32.
   Web of Science ®Google Scholar
• Campos-Taberner M, García-Haro F, Busetto L, Ranghetti L, Martínez B, Gilabert MA, Camps-Valls G, Camacho F, Boschetti M. 2018. A critical comparison of remote sensing leaf area index estimates over rice-cultivated areas: from Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT polar system. Remote Sens. 10(5):763.
   Web of Science ®Google Scholar
• Campos-Taberner M, García-Haro FJ, Camps-Valls G, Grau-Muedra G, Nutini F, Crema A, Boschetti M. 2016. Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote Sens Environ. 187:102–118.
   Web of Science ®Google Scholar
• Chang CC, Lin CJ. 2011. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2(3):27.
   Web of Science ®Google Scholar
• Chen JM, Black TA. 1992. Defining leaf-area index for non-flat leaves. Plant Cell Environ. 15(4):421–429.
   Web of Science ®Google Scholar
• Das B, Sahoo RN, Pargal S, Krishna G, Verma R, Chinnusamy V, Sehgal VK, Gupta VK. 2019. Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands. Geocarto Int. 34:1–18.
   Web of Science ®Google Scholar
• Dawe D, Pandey S, Nelson A. 2010. Emerging trends and spatial patterns of rice production. Rice in the global economy: strategic research and policy issues for food security. Los Baños (Philippines): International Rice Research Institute (IRRI).
   Google Scholar
• Elert E. 2014. Rice by the numbers: a good grain. Nature. 514(7524):S50–S51.
   PubMed Web of Science ®Google Scholar
• FAO. 2011. FAOSTATS [accessed 2011 Dec 15]. www.faostat.fao.org.
   Google Scholar
• Ferrant S, Selles A, Le Page M, Herrault P-A, Pelletier C, Al-Bitar A, Mermoz S, Gascoin S, Bouvet A, Saqalli M, et al. 2017. Detection of irrigated crops from Sentinel-1 and Sentinel-2 data to estimate seasonal groundwater use in south India. Remote Sens. 9(11):1119.
   Web of Science ®Google Scholar
• Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83(1-2):195–213.
   Web of Science ®Google Scholar
• IRRI. 2008 Jan. Press release [Online]. [accessed 2008 Jan 20]. http://www.irri.org/media/press/press.asp?id=168.
   Google Scholar
• Khush GS. 2005. What it will take to feed 5.0 billion rice consumers in 2030. Plant Mol Biol. 59(1):1–6.
   PubMed Web of Science ®Google Scholar
• Kuhn M. 2008. Building predictive models in R using the caret package. J Stat Soft. 28(5):1–26.
   PubMed Web of Science ®Google Scholar
• Lee B, Kwon H, Miyata A, Lindner S, Tenhunen J. 2016. Evaluation of a phenology-dependent response method for estimating leaf area index of rice across climate gradients. Remote Sens. 9(1):20.
   Web of Science ®Google Scholar
• Li CR, Jia YY, Hu J, Li ZY. 2008. An analysis of the prospects of HJ-1 optical satellites in remote sensing application. Remote Sens Land Resour. 3:1–3.
   Google Scholar
• Li J, Chen X, Tian L, Huang J, Feng L. 2015. Improved capabilities of the Chinese high-resolution remote sensing satellite GF-1 for monitoring suspended particulate matter (SPM) in inland waters: radiometric and spatial considerations. ISPRS J Photogramm Remote Sens. 106:145–156.
   Web of Science ®Google Scholar
• Li S, Yuan F, Ata-Ui-Karim ST, Zheng H, Cheng T, Liu X, Tian Y, Zhu Y, Cao W, Cao Q. 2019. Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sens. 11(15):1763.
   Web of Science ®Google Scholar
• Mansaray LR, Huang W, Zhang D, Huang J, Li J. 2017. Mapping rice fields in urban Shanghai, southeast China, using Sentinel-1A and Landsat 8 datasets. Remote Sens. 9(3):257.
   Web of Science ®Google Scholar
• Mansaray LR, Wang F, Huang J, Yang L, Kanu S. 2019. Accuracies of support vector machine (SVM) and random forest (RF) in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets. Geocarto Int. 34:1–21.
   Web of Science ®Google Scholar
• Mansaray LR, Yang L, Kabba VT, Kanu AS, Huang J, Wang F. 2019. Optimising rice mapping in cloud-prone environments by combining quad-source optical with Sentinel-1A microwave satellite imagery. GISci Remote Sens. 56:1–22.
   Web of Science ®Google Scholar
• Mansaray LR, Zhang D, Zhou Z, Huang J. 2017. Evaluating the potential of temporal Sentinel-1A data for paddy rice discrimination at local scales. Remote Sens Lett. 8(10):967–976.
   Web of Science ®Google Scholar
• Mohanty S, Wassmann R, Nelson A, Moya P, Jagadish S. 2013. Rice and climate change: significance for food security and vulnerability. Int Rice Res Inst. 14: 1–11.
   Google Scholar
• Monnet JM, Chanussot J, Berger F. 2011. Support vector regression for the estimation of forest stand parameters using airborne laser scanning. IEEE Geosci Remote Sens Lett. 8(3):580–584.
   Web of Science ®Google Scholar
• Muthayya S, Sugimoto JD, Montgomery S, Maberly GF. 2014. An overview of global rice production, supply, trade, and consumption. Ann NY Acad Sci. 1324(1):7–14.
   PubMed Web of Science ®Google Scholar
• Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira JM. 2012. Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest. For Ecol Manage. 275:117–129.
   Web of Science ®Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Vanderplas J. 2011. Scikit-learn: machine learning in Python. J Mach Learn Res. 12:2825–2830.
   Web of Science ®Google Scholar
• Purevdorj M, Kubo M. 2005. The future of rice production, consumption and seaborne trade: synthetic prediction method. J Food Distrib Res. 36:250–259.
   Google Scholar
• Siegmann B, Jarmer T. 2015. Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data. Int J Remote Sens. 36(18):4519–4534.
   Web of Science ®Google Scholar
• Toming K, Kutser T, Laas A, Sepp M, Paavel B, Nõges T. 2016. First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sens. 8(8):640.
   Web of Science ®Google Scholar
• Wang FM, Huang JF, Lou ZH. 2011. A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands. Precision Agric. 12(3):439–447.
   Web of Science ®Google Scholar
• Wang J, Huang J, Gao P, Wei C, Mansaray LR. 2016. Dynamic mapping of rice growth parameters using HJ-1 CCD time series data. Remote Sens. 8(11):931.
   Web of Science ®Google Scholar
• Wang J, Huang J-f, Wang X-z, Jin M-t, Zhou Z, Guo Q-y, Zhao Z-w, Huang W-j, Zhang Y, Song X-d. 2015. Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images. J Zhejiang Univ Sci B. 16(10):832–844.
   PubMed Web of Science ®Google Scholar
• Xiao X, Boles S, Frolking S, Li C, Babu JY, Salas W, Moore B, III. 2006. Mapping paddy rice agriculture in South and Southeast Asia using multi- temporal MODIS images. Remote Sens Environ. 100(1):95–113.
   Web of Science ®Google Scholar
• Xiao ZQ, Liang SL, Wang JD, Jiang B, Li XJ. 2011. Real-time retrieval of leaf area index from MODIS time series data. Remote Sens Environ. 115(1):97–106.
   Web of Science ®Google Scholar
• Zhang X, Wu B, Ponce-Campos G, Zhang M, Chang S, Tian F. 2018. Mapping up-to-date paddy rice extent at 10 M resolution in China through the integration of optical and synthetic aperture radar images. Remote Sens. 10(8):1200.
   Web of Science ®Google Scholar