Mapping and yield prediction of castor bean (Ricinus communis) using Sentinel-2A satellite image in a semi-arid region of india
References
- Agri Farming, (2023). Castor cultivation information guide. Retrieved June 4, 2023, from https://www.agrifarming.in/castor-cultivation-information-guide.
- Atkinson, P.M., Tatnall, A.R.L. (1997). Introduction neural networks in remote sensing. Int. J. Remote Sens. 18(4):699-709. https://doi.org/10.1080/014311697218700
- Bégué, A., Arvor, D., Bellon, B., Betbeder, J., Abelleyra, D., Ferraz, R. P. D., Lebourgeois, V., et al., (2018). Remote Sensing and Cropping Practices: A Review. Remote Sens., 10, 99. https://doi.org/10.3390/rs10010099
- Belward, A.S., Skøien, J.O., (2015). Who launched what, when and why; trends in global land-cover observation capacity from civilian Earth observation satellites. ISPRS J. Photogramm. Remote Sens., 103, 115-128. https://doi.org/10.1016/j.isprsjprs.2014.03.009
- Bolton, D.K., Friedl, M.A. (2013). Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol. 173, 74-84. https://doi.org/10.1016/j.agrformet.2013.01.007
- Cavalaris, C., Latterini, F., Stefanoni W., Karamoutis, C., Luigi Pari, L., Alexopoulou, E., (2022) Monitoring Chemical-Induced Ripening of Castor (Ricinus communis L.) by UAS-Based Remote Sensing. Agriculture, 12, 159. https://doi.org/10.3390/agriculture12020159
- Chu, C., Hsu, A.L., Chou, K.H., Bandettini, P., Lin, C., ADNI Initiative (2012). Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. NeuroImage, 60(1):59-70. https://doi.org/10.1016/j.neuroimage.2011.11.066
- Cortes, C., Vapnik, V.N. (1995). Support-vector networks. Mach. Learn. 20:273-297. https://doi.org/10.1007/BF00994018
- Esquerdo, J., Zullo, J., Antunes, J.F.G. (2011). Use of NDVI/AVHRR time-series profiles for soybean crop monitoring in Brazil. Int. J. Remote Sens. 32: 3711-3727. https://doi.org/10.1080/01431161003764112
- Ghazaryan, G., Dubovyk, O., Löw, F., Lavreniuk, M., Kolotii, A., Schellberg, J., Kussul, N., (2018). A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics. Eur. J. Remote Sens., 51:1, 511-524. https://doi.org/10.1080/22797254.2018.1455540.
- Huang. J., Wang, X., Li, X., Tian, H., Pan, Z. (2013). Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA’s-AVHRR. PLoS ONE 8(8): e70816. https://doi.org/10.1371/journal.pone.0070816.
- Huete, A. R., Didan, K., Miura, T., Rodriguez, E., Gao, X., Ferreira, L. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ., 83, 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
- Huete, A., Justice, C., Van Leeuwen, W. (1999). MODIS vegetation index (MOD13), Algorithm theoretical basis document, 3, 213.
- Jaafar, H. H., Ahmed, F. A. (2015). Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands. Int. J. Remote Sens., 36(18), 4570-4589. https://doi.org/10.1080/01431161.2015.1084434.
- Jiang, Z., Huete, A.R, Didan, K., Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 112(10), 3833-3845. https://doi.org/10.1016/j.rse.2008.06.006.
- Kumar, P, Gupta, D. K., Mishra, V.N., Prasad, R. (2015). Comparison of support vector machine, artificial neural network and spectral angle mapper algorithms for crop classification using LISS IV data. Int. J. Remote Sens. 36(6):1604-1617. https://doi.org/10.1080/2150704X.2015.1019015.
- Kumar, P., Prasad, R., Choudhary, A., Mishra, V. N., Gupta, D.K. and Srivastava, P.K. (2017). A statistical significance of differences in classification accuracy of crop types using different classification algorithms. Geocarto Int., 32(2), 206-224. https://doi.org/10.1080/10106049.2015.1132483.
- Lillesand, T.M., Kiefer, R.W., Chipman, J.W. (2008). Remote sensing and image interpretation, 6th Edn. Wiley, New York.
- Liu, J., Shang, J., Qian, B., Huffman, T., Zhang, Y., Dong, T. Jing, Q., Martin, T. (2019). Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada. Remote Sens. 11(20), 2419. https://doi.org/10.3390/rs11202419.
- Lu, D., Mausel, P., Batistella, M., Moran, E. (2004). Comparison of landcover classification methods in the Brazilian Amazon basin. Photogramm Eng Remote Sens 70(6):723-731. https://doi.org/10.14358/PERS.70.6.723.
- Lu, D., Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens., 28(5):823-870. https://doi.org/10.1080/01431160600746456.
- Martins, V. S., Barbosa, C. C. F., De Carvalho, L. A. S., Jorge, D. S. F., Lobo, F. D. L., and Novo, E. M. L. de Mores. (2017). Assessment of atmospheric correction methods for Sentinel-2A MSI images applied to Amazon floodplain lakes. Remote Sens., 9 (4), 322. https://doi.org/10.3390/rs9040322.
- Mas J.F., Flores, J.J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. Int. J. Remote Sens. 29:617-663. https://doi.org/10.1080/01431160701352154.
- Mishra, V.N., Prasad, R., Kumar, P., Gupta, D.K., Srivastava, P.K (2017b). Dual polarimetric C-band SAR data for land use/land cover classification by incorporating textural information. Environ. Earth Sci. 76(1):26. https://doi.org/10.1007/s12665-016-6341-7.
- Mishra, V.N., Prasad, R., Kumar, P., Srivastava, P.K., Rai, P. K., (2017a). Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information. J. Appl. Remote Sens. 11(4), 046003. https://doi.org/10.1117/1.JRS.11.046003.
- Mkhabela, M.S., Bullock, P., Raj, S., Wang, S., Yang, Y. (2011). Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agric. For. Meteorol. 151, 385-393. https://doi.org/10.1016/j.agrformet.2010.11.012.
- Pal, M., Foody, G. M. (2010). Feature selection for classification of hyperspectral data by SVM. IEEE Trans. Geosci. Remote Sens. 48, 2297-2307. https://doi.org/10.1109/TGRS.2009.2039484.
- Pareulo, J. M., Jobbagy, E. G., Sala, O. E., Lauenroth, W. K. and Burke, I. C. (1998). Functional and Structural convergence of temperate grassland and shrubland ecosystems. Ecol Appl, 8 (1), 194-206. https://doi.org/10.1890/1051-0761(1998)008[0194:FASCOT]2.0.CO;2.
- Pijanowski, B.C., Brown, D.G., Shellito, B.A., Manik, G.A. (2002). Using neural networks and GIS to forecast land use changes: a land transformation model. Comput Environ Urban Syst 26:553-575. https://doi.org/10.1016/S0198-9715(01)00015-1.
- Polsot, A.-S., Speedy, A., Kueneman, E., (2004). Good Agricultural Practices-A Working Concept; Background Paper for the FAO Internal Workshop on Good Agricultural Practices; FAO: Rome, Italy, p. 41.
- Puissant, A., Rougiera, S., Andre´, S. (2014). Object-oriented mapping of urban trees using random forest classifiers. Int J Appl Earth Obs Geoinf 26:235–245. http://dx.doi.org/10.1016/j.jag.2013.07.002.
- Richards, J.A, Jia, X. (2006). Remote sensing digital image analysis, 4th edn. Springer, Heidelberg.
- Richards, J.A. (1993). Remote Sensing Digital Image Analysis, Springer.
- Roujean, J. L., Breon, F.M. (1995). Estimating par absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ., 51, 375-384. https://doi.org/10.1016/0034-4257(94)00114-3.
- Shammi, S. A., Meng, Q. (2021). Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecol Indic, 121, 107124, https://doi.org/10.1016/j.ecolind.2020.107124.
- Shiu, Y.-S., Chuang, Y.-C. (2019). Yield Estimation of paddy rice based on satellite imagery: comparison of global and local regression models. Remote Sens. 11, 111. https://doi.org/10.3390/rs11020111.
- Skakun, S., Franch, B., Vermote, E., Roger, J-C., Becker-Reshef, I., Justice, C., Kussul, N., (2017). Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sens. Environ., 195, 244-258. https://doi.org/10.1016/j.rse.2017.04.026.
- Srivastava, P.K., Han, D., Rico-Ramirez, M.A., Bray, M., Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Adv. Space Res. 50:1250-1265. https://doi.org/10.1016/j.asr.2012.06.032.
- Sun, H., Xu, A., Lin, H., Zhang, L., Mei, Y. (2012). Winter wheat mapping using temporal signatures of MODIS vegetation index data. Int. J. Remote Sens., 33(16), 5026-5042. https://doi.org/10.1080/01431161.2012.657366.
- Vapnik, V.N. (1998). Statistical learning theory. Wiley, New York.
- Weier, J. and Herring, D. (2000). Measuring Vegetation (NDVI & EVI). NASA Earth Observatory, Washington DC.
Language: English
Page range: 1 - 23
Submitted on: May 3, 2023
Accepted on: Jun 12, 2023
Published on: Oct 6, 2023
Published by: Czech Society for Landscape Ecology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
Related subjects:
© 2023 Ritesh Kumar, Narendra Singh Bishnoi, Nimish Narayan Gautam, Muskan,, Varun Narayan Mishra, published by Czech Society for Landscape Ecology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.