Using normalised difference vegetation index in classification and agroecological zoning of spring row crops
Keywords:
agrometeorology; crop mapping; maize; soybeans; sunflower; remote sensing.
Abstract
Remote sensing is an important branch of modern science and technology with various applications in different branches of life sciences. Its application in agriculture is focused mainly on crop monitoring and yield prediction. However, the value of remote sensing in the systems of automated crop mapping and agroecological zoning of plant species is increasing. The main purpose of this study is to establish the possibility of using normalised difference vegetation index in the main spring row crops, namely maize, soybeans, sunflower, to precisely classify the fields with each crop, and to evaluate the best agroecological zones for their cultivation in rainfed conditions in Ukraine. The study was carried out using the data on the normalised difference vegetation index for the period May – November 2018 from 750 fields and experimental plots, randomly scattered over the territory of Ukraine with equal representation by every administrative district of the country. The index values were calculated using combined Landsat-8 and Sentinel-2 images, with further generalisation for every crop and region. Multiclass linear discriminant analysis and canonical discriminant analysis were applied to determine whether it is possible to distinguish between the studied crops using the values of the normalised difference vegetation index as the only input. As a result, it was established that the best zone for crop cultivation is the west of the country: NDVI values for the growing season averaged to 0.34 for sunflower, 0.36 for soybeans, and 0.36 for maize, respectively. The worst growing conditions, based on the lowest NDVI values, were observed in the east for sunflower (0.26) and maize (0.25), but the minimum NDVI for soybeans (0.27) was observed in the south. Regarding the classification problem, it was found that the highest importance for the classification of crops is attributed to the values of the normalised difference vegetation index, recorded in August. The supervised learning using canonical discriminant function resulted in mediocre predictive performance of the multiple linear function with general classification accuracy of 56.5%. The best accuracy of classification was achieved for sunflower (70.4%), while it is difficult to distinguish between maize and soybeans because these crops have quite similar intra-seasonal dynamics of the vegetation index (classification accuracy was 46.8% and 52.4%, respectively; the total number of incorrectly predicted samples in the “maize-soybeans” group was 134 or 26.8%). The main limitation of this study is its single year basis, notwithstanding the fact that the year of the study was characterized as a typical one for most territory of Ukraine in terms of meteorological conditions. Therefore, more studies are required to clarify the possibility of a classification between maize and soybeans based on remote sensing data.References
Bala, S. K., Choudhury, B. U., Sood, A., Bains, G. S., & Mukherjee, J. (2009). Characterization of agro-ecological zones of Punjab state using remote sensing and GIS tools. In: ISPRS/XXXVIII-8/W3 Workshop Proceedings: Impact of Climate Change on Agriculture. Ahmedabad. Pp. 331–335.
Bastiaanssen, W. G., Molden, D. J., & Makin, I. W. (2000). Remote sensing for irrigated agriculture: Examples from research and possible applications. Agricultural Water Management, 46(2), 137–155.
Bellone, T., Boccardo, P., & Perez, F. (2009). Investigation of vegetation dynamics using long-term normalized difference vegetation index time-series. American Journal of Environmental Sciences, 5(4), 461.
Bukhovets, A. G., Semin, E. A., Kucherenko, M. V., & Yablonovskaya, S. I. (2020). Dynamic model of crops’ normalized difference vegetation index in Central Federal District environment. IOP Conference Series: Earth and Environmental Science, 548(4), 042019.
Cabrera-Bosquet, L., Molero, G., Stellacci, A. N. N. A., Bort, J., Nogués, S., & Araus, J. (2011). NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Research Communications, 39(1), 147–159.
Chen, X., Xun, Y., Li, W., & Zhang, J. (2010). Combining discriminant analysis and neural networks for corn variety identification. Computers and Electronics in Agriculture, 71, S48–S53.
Chen, Y., Lu, D., Moran, E., Batistella, M., Dutra, L. V., Sanches, I. D. A., da Silva, R. F. B., Huang, J., Luiz, A. J. B., & de Oliveira, M. A. F. (2018). Mapping croplands, cropping patterns, and crop types using MODIS time-series data. International Journal of Applied Earth Observation and Geoinformation, 69, 133–147.
Cruz-Castillo, J. G., Ganeshanandam, S., MacKay, B. R., Lawes, G. S., Lawoko, C. R. O., & Woolley, D. J. (1994). Applications of canonical discriminant analysis in horticultural research. HortScience, 29(10), 1115–1119.
Damian, J. M., Pias, O. H. D. C., Cherubin, M. R., Fonseca, A. Z. D., Fornari, E. Z., & Santi, A. L. (2019). Applying the NDVI from satellite images in delimiting management zones for annual crops. Scientia Agricola, 77(1), e20180055.
de Souza, C. H. W., Mercante, E., Johann, J. A., Lamparelli, R. A. C., & Uribe-Opazo, M. A. (2015). Mapping and discrimination of soya bean and corn crops using spectro-temporal profiles of vegetation indices. International Journal of Remote Sensing, 36(7), 1809–1824.
Everitt, B. S., & Skrondal, A. (2010). The Cambridge dictionary of statistics. 4th edition. Cambridge University Press, Cambridge.
Fotheringham, A. S., & Reeds, L. G. (1979). An application of discriminant analysis to agricultural land use prediction. Economic Geography, 55(2), 114–122.
Galik, O. I., & Basiuk, T. O. (2014). Dovidkovi dani z klimatu Ukrajiny [The reference book on the climate of Ukraine]. Publishing House of the National University of Water Economy and Environmental Use, Rivne (in Ukrainian).
Green, G. H., Boze, B. V., Choundhury, A. H., & Power, S. (1998). Using logistic regression in classification. Marketing Research, 10(3), 5.
Hangbin, Z., Xiaoping, Y., & Jialin, L. (2011). MODIS data based NDVI seasonal dynamics in agro-ecosystems of south bank Hangzhouwan bay. African Journal of Agricultural Research, 6(17), 4025–4033.
Hao, P., Wang, L., Zhan, Y., & Niu, Z. (2016). Using moderate-resolution temporal NDVI profiles for high-resolution crop mapping in years of absent ground reference data: A case study of bole and manas counties in Xinjiang, China. ISPRS International Journal of Geo-Information, 5(5), 67.
Henik, J. J. (2012). Utilizing NDVI and remote sensing data to identify spatial variability in plant stress as influenced by management. Iowa State University, Ames.
Herbei, M. V., & Florin, S. A. L. A. (2015). Use Landsat image to evaluate vegetation stage in sunflower crops. AgroLife Scientific Journal, 4(1), 79–86.
Holdridge, L. R. (1959). Simple method for determining potential evapotranspiration from temperature data. Science, 130(3375), 572–572.
Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6.
Jiang, D., Wang, N. B., Yang, X. H., & Wang, J. H. (2003). Study on the interaction between NDVI profile and the growing status of crops. Chinese Geographical Science, 13, 62–65.
Justice, C. O., Townshend, J. R. G., Holben, B. N., & Tucker, E. C. (1985). Analysis of the phenology of global vegetation using meteorological satellite data. International Journal of Remote Sensing, 6(8), 1271–1318.
Li, C., Li, H., Li, J., Lei, Y., Li, C., Manevski, K., & Shen, Y. (2019). Using NDVI percentiles to monitor real-time crop growth. Computers and Electronics in Agriculture, 162, 357–363.
Li, T., Zhu, S., & Ogihara, M. (2006). Using discriminant analysis for multi-class classification: An experimental investigation. Knowledge and Information Systems, 10, 453–472.
Lin, M. L., & Perng, C. H. (2011). The impact of terrain on NDVI dynamics of corn field using generalized estimating equations and time-series MODIS images. In: 2011 IEEE International Geoscience and Remote Sensing Symposium. Vancouver. Pp. 3342–3345.
López-Granados, F., Gómez-Casero, M. T., Pena-Barragán, J. M., Jurado-Expósito, M., & Garcia-Torres, L. (2010). Classifying irrigated crops as affected by phenological stage using discriminant analysis and neural networks. Journal of the American Society for Horticultural Science, 135(5), 465–473.
Lykhovyd, P. (2021). Irrigation needs in Ukraine according to current aridity level. Journal of Ecological Engineering, 22(8), 11–18.
Lykhovyd, P. V. (2021). Seasonal dynamics of normalized difference vegetation index in some winter and spring crops in the South of Ukraine. Agrology, 4(4), 187–193.
Lykhovyd, P., Vozhehova, R., & Lavrenko, S. (2022). Annual NDVI dynamics observed in industrial tomato grown in the south of Ukraine. Modern Phytomorphology, 16, 160–164.
Matthew, C., Lawoko, C. R. O., Korte, C. J., & Smith, D. (1994). Application of canonical discriminant analysis, principal component analysis, and canonical correlation analysis as tools for evaluating differences in pasture botanical composition. New Zealand Journal of Agricultural Research, 37(4), 509–520.
Menenti, M., Azzali, S., Verhoef, W., & Van Swol, R. (1993). Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images. Advances in Space Research, 13(5), 233–237.
Neeti, N., Rogan, J., Christman, Z., Eastman, J. R., Millones, M., Schneider, L., Nickl, E., Schmook, B., Turner II, B. L., & Ghimire, B. (2012). Mapping seasonal trends in vegetation using AVHRR-NDVI time series in the Yucatán Peninsula, Mexico. Remote Sensing Letters, 3(5), 433–442.
Ouzemou, J. E., El Harti, A., Lhissou, R., El Moujahid, A., Bouch, N., El Ouazzani, R., Bachaoui, E. M., & El Ghmari, A. (2018). Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system. Remote Sensing Applications: Society and Environment, 11, 94–103.
Paruelo, J. M., & Lauenroth, W. K. (1998). Interannual variability of NDVI and its relationship to climate for North American shrublands and grasslands. Journal of Biogeography, 25(4), 721–733.
Patel, N. R. (2003). Remote sensing and GIS application in agro-ecological zoning. In: Satellite remote sensing and GIS application in agro-ecological zoning. Proceedings of the Training Workshop. Dehra Dun. Pp. 213–235.
Pichura, V., Domaratskiy, Y., Potravla, L., Biloshkurenko, O., & Dobrovol’skiy, A. (2023). Application of the research on spatio-temporal differentiation of a vegetation index in evaluating sunflower hybrid plasticity and growth-regulators in the steppe zone of Ukraine. Journal of Ecological Engineering, 24(6), 144–165.
Pinar, M. Ö., & Erpul, G. (2019). Monitoring land cover changes during different growth stages of semi-arid cropping systems of wheat and sunflower by NDVI and LAI. In: 2019 8th International Conference on Agro-Geoinformatics. Istanbul. Pp. 1–5.
Rouse Jr., J. W., Haas, R. H., Deering, D. W., Schell, J. A., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. E75-10354). Remote Sensing Center Texas A&M University, Texas.
Satish, G., & Sahu, P. (2017). Discriminant analysis: A tool for identifying significant socio-economic correlates in farming system-a case study. Journal of Crop and Weed, 13(1), 42–45.
Shao, Y., Lunetta, R. S., Ediriwickrema, J., & Iiames, J. (2010). Mapping cropland and major crop types across the Great Lakes Basin using MODIS-NDVI data. Photogrammetric Engineering and Remote Sensing, 76(1), 73–84.
Silleos, N., Misopolinos, N., & Perakis, K. (1992). Relationships between remote sensing spectral indices and crops discrimination. Geocarto International, 7(2), 41–51.
Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136.
Tamás, A., Kovács, E., Horváth, É., Juhász, C., Radócz, L., Rátonyi, T., & Ragán, P. (2023). Assessment of NDVI dynamics of maize (Zea mays L.) and its relation to grain yield in a polyfactorial experiment based on remote sensing. Agriculture, 13(3), 689.
Usha, K., & Singh, B. (2013). Potential applications of remote sensing in horticulture – A review. Scientia Horticulturae, 153, 71–83.
Varmaghani, A., & Eichinger, W. E. (2016). Early‐season classification of corn and soybean using bayesian discriminant analysis on satellite images. Agronomy Journal, 108(5), 1880–1889.
Venancio, L. P., Filgueiras, R., da Cunha, F. F., dos Santos Silva, F. C., dos Santos, R. A., & Mantovani, E. C. (2020). Mapping of corn phenological stages using NDVI from OLI and MODIS sensors. Semina: Ciências Agrárias, Londrina, 41(5), 1517–1534.
Verhulst, N., Govaerts, B., Sayre, K. D., Deckers, J., François, I. M., & Dendooven, L. (2009). Using NDVI and soil quality analysis to assess influence of agronomic management on within-plot spatial variability and factors limiting production. Plant and Soil, 317, 41–59.
Wang, R., Cherkauer, K., & Bowling, L. (2016). Corn response to climate stress detected with satellite-based NDVI time series. Remote Sensing, 8(4), 269.
Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402.
Yin, X., McClure, A., & Tyler, D. (2010). Relationships of plant height and canopy NDVI with nitrogen nutrition and yields of corn. In: Proceedings of the 19th World Congress of Soil Science, Soil Solutions for a Changing World. Brisbane. Pp. 40–42.
Zhang, H., Chang, J., Zhang, L., Wang, Y., Li, Y., & Wang, X. (2018). NDVI dynamic changes and their relationship with meteorological factors and soil moisture. Environmental Earth Sciences, 77, 582.
Zhang, H., Lan, Y., Suh, C. P., Westbrook, J. K., Lacey, R., & Hoffmann, W. C. (2012). Differentiation of cotton from other crops at different growth stages using spectral properties and discriminant analysis. Transactions of the ASABE, 55(4), 1623–1630.
Zhong, L., Hu, L., Yu, L., Gong, P., & Biging, G. S. (2016). Automated mapping of soybean and corn using phenology. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 151–164.
Bastiaanssen, W. G., Molden, D. J., & Makin, I. W. (2000). Remote sensing for irrigated agriculture: Examples from research and possible applications. Agricultural Water Management, 46(2), 137–155.
Bellone, T., Boccardo, P., & Perez, F. (2009). Investigation of vegetation dynamics using long-term normalized difference vegetation index time-series. American Journal of Environmental Sciences, 5(4), 461.
Bukhovets, A. G., Semin, E. A., Kucherenko, M. V., & Yablonovskaya, S. I. (2020). Dynamic model of crops’ normalized difference vegetation index in Central Federal District environment. IOP Conference Series: Earth and Environmental Science, 548(4), 042019.
Cabrera-Bosquet, L., Molero, G., Stellacci, A. N. N. A., Bort, J., Nogués, S., & Araus, J. (2011). NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Research Communications, 39(1), 147–159.
Chen, X., Xun, Y., Li, W., & Zhang, J. (2010). Combining discriminant analysis and neural networks for corn variety identification. Computers and Electronics in Agriculture, 71, S48–S53.
Chen, Y., Lu, D., Moran, E., Batistella, M., Dutra, L. V., Sanches, I. D. A., da Silva, R. F. B., Huang, J., Luiz, A. J. B., & de Oliveira, M. A. F. (2018). Mapping croplands, cropping patterns, and crop types using MODIS time-series data. International Journal of Applied Earth Observation and Geoinformation, 69, 133–147.
Cruz-Castillo, J. G., Ganeshanandam, S., MacKay, B. R., Lawes, G. S., Lawoko, C. R. O., & Woolley, D. J. (1994). Applications of canonical discriminant analysis in horticultural research. HortScience, 29(10), 1115–1119.
Damian, J. M., Pias, O. H. D. C., Cherubin, M. R., Fonseca, A. Z. D., Fornari, E. Z., & Santi, A. L. (2019). Applying the NDVI from satellite images in delimiting management zones for annual crops. Scientia Agricola, 77(1), e20180055.
de Souza, C. H. W., Mercante, E., Johann, J. A., Lamparelli, R. A. C., & Uribe-Opazo, M. A. (2015). Mapping and discrimination of soya bean and corn crops using spectro-temporal profiles of vegetation indices. International Journal of Remote Sensing, 36(7), 1809–1824.
Everitt, B. S., & Skrondal, A. (2010). The Cambridge dictionary of statistics. 4th edition. Cambridge University Press, Cambridge.
Fotheringham, A. S., & Reeds, L. G. (1979). An application of discriminant analysis to agricultural land use prediction. Economic Geography, 55(2), 114–122.
Galik, O. I., & Basiuk, T. O. (2014). Dovidkovi dani z klimatu Ukrajiny [The reference book on the climate of Ukraine]. Publishing House of the National University of Water Economy and Environmental Use, Rivne (in Ukrainian).
Green, G. H., Boze, B. V., Choundhury, A. H., & Power, S. (1998). Using logistic regression in classification. Marketing Research, 10(3), 5.
Hangbin, Z., Xiaoping, Y., & Jialin, L. (2011). MODIS data based NDVI seasonal dynamics in agro-ecosystems of south bank Hangzhouwan bay. African Journal of Agricultural Research, 6(17), 4025–4033.
Hao, P., Wang, L., Zhan, Y., & Niu, Z. (2016). Using moderate-resolution temporal NDVI profiles for high-resolution crop mapping in years of absent ground reference data: A case study of bole and manas counties in Xinjiang, China. ISPRS International Journal of Geo-Information, 5(5), 67.
Henik, J. J. (2012). Utilizing NDVI and remote sensing data to identify spatial variability in plant stress as influenced by management. Iowa State University, Ames.
Herbei, M. V., & Florin, S. A. L. A. (2015). Use Landsat image to evaluate vegetation stage in sunflower crops. AgroLife Scientific Journal, 4(1), 79–86.
Holdridge, L. R. (1959). Simple method for determining potential evapotranspiration from temperature data. Science, 130(3375), 572–572.
Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6.
Jiang, D., Wang, N. B., Yang, X. H., & Wang, J. H. (2003). Study on the interaction between NDVI profile and the growing status of crops. Chinese Geographical Science, 13, 62–65.
Justice, C. O., Townshend, J. R. G., Holben, B. N., & Tucker, E. C. (1985). Analysis of the phenology of global vegetation using meteorological satellite data. International Journal of Remote Sensing, 6(8), 1271–1318.
Li, C., Li, H., Li, J., Lei, Y., Li, C., Manevski, K., & Shen, Y. (2019). Using NDVI percentiles to monitor real-time crop growth. Computers and Electronics in Agriculture, 162, 357–363.
Li, T., Zhu, S., & Ogihara, M. (2006). Using discriminant analysis for multi-class classification: An experimental investigation. Knowledge and Information Systems, 10, 453–472.
Lin, M. L., & Perng, C. H. (2011). The impact of terrain on NDVI dynamics of corn field using generalized estimating equations and time-series MODIS images. In: 2011 IEEE International Geoscience and Remote Sensing Symposium. Vancouver. Pp. 3342–3345.
López-Granados, F., Gómez-Casero, M. T., Pena-Barragán, J. M., Jurado-Expósito, M., & Garcia-Torres, L. (2010). Classifying irrigated crops as affected by phenological stage using discriminant analysis and neural networks. Journal of the American Society for Horticultural Science, 135(5), 465–473.
Lykhovyd, P. (2021). Irrigation needs in Ukraine according to current aridity level. Journal of Ecological Engineering, 22(8), 11–18.
Lykhovyd, P. V. (2021). Seasonal dynamics of normalized difference vegetation index in some winter and spring crops in the South of Ukraine. Agrology, 4(4), 187–193.
Lykhovyd, P., Vozhehova, R., & Lavrenko, S. (2022). Annual NDVI dynamics observed in industrial tomato grown in the south of Ukraine. Modern Phytomorphology, 16, 160–164.
Matthew, C., Lawoko, C. R. O., Korte, C. J., & Smith, D. (1994). Application of canonical discriminant analysis, principal component analysis, and canonical correlation analysis as tools for evaluating differences in pasture botanical composition. New Zealand Journal of Agricultural Research, 37(4), 509–520.
Menenti, M., Azzali, S., Verhoef, W., & Van Swol, R. (1993). Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images. Advances in Space Research, 13(5), 233–237.
Neeti, N., Rogan, J., Christman, Z., Eastman, J. R., Millones, M., Schneider, L., Nickl, E., Schmook, B., Turner II, B. L., & Ghimire, B. (2012). Mapping seasonal trends in vegetation using AVHRR-NDVI time series in the Yucatán Peninsula, Mexico. Remote Sensing Letters, 3(5), 433–442.
Ouzemou, J. E., El Harti, A., Lhissou, R., El Moujahid, A., Bouch, N., El Ouazzani, R., Bachaoui, E. M., & El Ghmari, A. (2018). Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system. Remote Sensing Applications: Society and Environment, 11, 94–103.
Paruelo, J. M., & Lauenroth, W. K. (1998). Interannual variability of NDVI and its relationship to climate for North American shrublands and grasslands. Journal of Biogeography, 25(4), 721–733.
Patel, N. R. (2003). Remote sensing and GIS application in agro-ecological zoning. In: Satellite remote sensing and GIS application in agro-ecological zoning. Proceedings of the Training Workshop. Dehra Dun. Pp. 213–235.
Pichura, V., Domaratskiy, Y., Potravla, L., Biloshkurenko, O., & Dobrovol’skiy, A. (2023). Application of the research on spatio-temporal differentiation of a vegetation index in evaluating sunflower hybrid plasticity and growth-regulators in the steppe zone of Ukraine. Journal of Ecological Engineering, 24(6), 144–165.
Pinar, M. Ö., & Erpul, G. (2019). Monitoring land cover changes during different growth stages of semi-arid cropping systems of wheat and sunflower by NDVI and LAI. In: 2019 8th International Conference on Agro-Geoinformatics. Istanbul. Pp. 1–5.
Rouse Jr., J. W., Haas, R. H., Deering, D. W., Schell, J. A., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. E75-10354). Remote Sensing Center Texas A&M University, Texas.
Satish, G., & Sahu, P. (2017). Discriminant analysis: A tool for identifying significant socio-economic correlates in farming system-a case study. Journal of Crop and Weed, 13(1), 42–45.
Shao, Y., Lunetta, R. S., Ediriwickrema, J., & Iiames, J. (2010). Mapping cropland and major crop types across the Great Lakes Basin using MODIS-NDVI data. Photogrammetric Engineering and Remote Sensing, 76(1), 73–84.
Silleos, N., Misopolinos, N., & Perakis, K. (1992). Relationships between remote sensing spectral indices and crops discrimination. Geocarto International, 7(2), 41–51.
Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136.
Tamás, A., Kovács, E., Horváth, É., Juhász, C., Radócz, L., Rátonyi, T., & Ragán, P. (2023). Assessment of NDVI dynamics of maize (Zea mays L.) and its relation to grain yield in a polyfactorial experiment based on remote sensing. Agriculture, 13(3), 689.
Usha, K., & Singh, B. (2013). Potential applications of remote sensing in horticulture – A review. Scientia Horticulturae, 153, 71–83.
Varmaghani, A., & Eichinger, W. E. (2016). Early‐season classification of corn and soybean using bayesian discriminant analysis on satellite images. Agronomy Journal, 108(5), 1880–1889.
Venancio, L. P., Filgueiras, R., da Cunha, F. F., dos Santos Silva, F. C., dos Santos, R. A., & Mantovani, E. C. (2020). Mapping of corn phenological stages using NDVI from OLI and MODIS sensors. Semina: Ciências Agrárias, Londrina, 41(5), 1517–1534.
Verhulst, N., Govaerts, B., Sayre, K. D., Deckers, J., François, I. M., & Dendooven, L. (2009). Using NDVI and soil quality analysis to assess influence of agronomic management on within-plot spatial variability and factors limiting production. Plant and Soil, 317, 41–59.
Wang, R., Cherkauer, K., & Bowling, L. (2016). Corn response to climate stress detected with satellite-based NDVI time series. Remote Sensing, 8(4), 269.
Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402.
Yin, X., McClure, A., & Tyler, D. (2010). Relationships of plant height and canopy NDVI with nitrogen nutrition and yields of corn. In: Proceedings of the 19th World Congress of Soil Science, Soil Solutions for a Changing World. Brisbane. Pp. 40–42.
Zhang, H., Chang, J., Zhang, L., Wang, Y., Li, Y., & Wang, X. (2018). NDVI dynamic changes and their relationship with meteorological factors and soil moisture. Environmental Earth Sciences, 77, 582.
Zhang, H., Lan, Y., Suh, C. P., Westbrook, J. K., Lacey, R., & Hoffmann, W. C. (2012). Differentiation of cotton from other crops at different growth stages using spectral properties and discriminant analysis. Transactions of the ASABE, 55(4), 1623–1630.
Zhong, L., Hu, L., Yu, L., Gong, P., & Biging, G. S. (2016). Automated mapping of soybean and corn using phenology. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 151–164.
Published
2023-11-19
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
