Araştırma Makalesi
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Yıl 2022, Cilt: 32 Sayı: 3, 507 - 526, 30.09.2022
https://doi.org/10.29133/yyutbd.1114636

Öz

Kaynakça

  • Ahmed, G. B., Shariff, A.R.M., Balasundram, S.K., & Fikri bin Abdullah, A. (2016). Agriculture land suitability analysis evaluation based multi criteria and GIS approach. IOP Conference Series: Earth and Environmental Science, 37, 012044.
  • Al-Bakri, J.T., & Suleiman, A.S. (2004). NDVI response to rainfall in different ecological zones in Jordan. International Journal of Remote Sensing, 25(19), 3897-3912.
  • Askari, M.S., Cui, J., O’Rourke, S.M., & Holden, N.M. (2015). Evaluation of soil structural quality using VIS–NIR spectra. Soil and Tillage Research, 146, 108–117.
  • Bagheri, N., Ahmadi, H., Alavipanah, S., & Omid, M. (2012). Soil-line vegetation indices for corn nitrogen content prediction. International Agrophysics, 26(2), 103–108.
  • Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., Thompson, T., Lascano, R. J., Li, H., & Moran, M.S. (2000). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, 2000 (Vol. 1619).
  • Blum, W.E.H. (2006). Soil Resources- The basis of human society and the environment. Bodenkultur 57, 197–202.
  • Damian, J.M., Pias, O.H.D.C., Cherubin, M.R., Fonseca, A.Z.D., Fornari, E.Z., Santi, A.L., (2020). Applying the NDVI from satellite images in delimiting management zones for annual crops. Sci. Agric. 77 (1).
  • Dedeoğlu, M., Başayiğit, L., Yüksel, M., & Kaya, F. (2020). Assessment of the vegetation indices on Sentinel-2A images for predicting the soil productivity potential in Bursa, Turkey. Environmental Monitoring and Assessment, 192(1), 1-16.
  • Dengiz, O. (2013). Land Suitability Assessment for Rice Cultivation Based on GIS Modeling. Turkish Journal of Agriculture and Forestry. 37: 326-334, DOI: 10.3906/tar-1204-36.
  • Dengiz, O., & Sağlam, M. (2012). Determination of land productivity index based on parametric approach using GIS technique. Eurasian Journal of Soil Science, 1, 51–57.
  • Doran, J.W., & Parkin, T.B. (1994). Defining and assessing soil quality. Defining soil quality for a sustainable environment, 35, 1-21.
  • Dumanski, J., & Peiretti, R. (2013). Modern concepts of soil conservation. International soil and water conservation research, 1(1), 19-23.
  • Explorer, E. (2000). FS; 083-00; Geological Survey (US).
  • Earth observatory NASA. http://earthobservatory.nasa.gov/Features/MeasuringVegetation.
  • ESRI, (2010). ArcGIS user’s guide. http://www.esri.com
  • FAO, (1983). Land and Water Development Division. Guidelines: land evaluation for rainfed agriculture. Food and Agriculture Organization of the United Nations.
  • FAO (1985). Guideline: land evaluation for irrigated agriculture. FAO Soils Bulletin, No. 55, Rome
  • FAO, I., & ISRIC, I. (2009). Harmonized world soil database (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria. http://www.fao.org/nr/land/soils/harmonized-world-soildatabase/en.
  • Feng, W., Guo, B.-B., Wang, Z. J., He, L., Song, X., Wang, Y. H., & Guo, T.C. (2014). Measuring leaf nitrogen concentration in winter wheat using double-peak spectral reflection remote sensing data. Field Crops Research, 159: 43–52.
  • Fitzgerald, G., Rodriguez, D., & O’Leary, G. (2010). Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—the canopy chlorophyll content index (CCCI). Field Crops Research, 116(3), 318–324.
  • Franch, B., Bautista, A.S., Fita, D., Rubio, C., Tarrazó-Serrano, D., Sánchez, A., Skakun, S., Vermote, E., Becker-Reshef, I., & Uris, A. (2021). Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data. Remote Sensing, 13(20), 4095.
  • Gupta, R.K., Vijayan, D., & Prasad, T.S. (2003). Comparative analysis of red-edge hyperspectral indices. Advances in Space Research, 32(11), 2217–2222.
  • Hillel, D. (2009). The mission of soil science in a changing World. J. Plant Nutr. Soil Sci. 172, 5–9.
  • Hott, M. C., Carvalho, L. M. T. D., Antunes, M. A. H., Santos, P. A. D., Arantes, T. B., Resende, J. C. D., & Rocha, W. S. D. D. (2016). Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor. Pesquisa Agropecuária Brasileira, 51, 858-868.
  • Huang, S., Miao, Y., Yuan, F., Gnyp, M., Yao, Y., Cao, Q., Wang, H., Lenz-Wiedemann, V., & Bareth, G. (2017). Potential of RapidEye and WorldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages. Remote Sensing, 9(3), 227.
  • Hufkens K., Melaas, E.K., Foster, T., Ceballos, F., Robles, M., & Kramer, B. (2019). Monitoring crop phenology using a smartphone based near-surface remote sensing approach. Agricultural and Forest Meteorology, 265: 327-337.
  • Jackson, R.D. (1986). Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology, 24(1), 265–287.
  • Jia, L., Yu, Z., Li, F., Gnyp, M., Koppe, W., Bareth, G., Miao, Y., Chen, X., & Zhang, F. (2011). Nitrogen status estimation of winter wheat by using an Ikonos satellite image in the north china plain. Computer and computing technologis in agriculture V. 5 th IFIP TC5/SIG 5,1 Conference, CCTA 2011 Beijing, Cina, October 2011 Proceedings, Part II.
  • Jiang, L., Liu, Y., Wu, S., & Yang, C. (2021). Analyzing ecological environment change and associated driving factors in China based on NDVI time series data. Ecological Indicators, 129: 107933.
  • Jones, A., Stolbovoy, V., Rusco, E., Gentile, A.R., Gardi, C., Marechal, B., & Montanarella, L. (2009). Climate change in Europe. 2. Impact on soil. A review. Agronomy for sustainable development, 29(3), 423-432.
  • Karlen D.L., Mausbach M.J., Doran J.W., Cline R.G., Harris R.F., & Schuman G.E. (1997). Soil quality: a concept, definition and framework for evaluation. Soil Sci. Soc. Am. J. 61, 4–10.
  • Kerr, J.T., & Ostrovsky, M. (2003). From space to species: ecological applications for remote sensing. Trends in ecology & evolution, 18(6), 299-305.
  • Kokaly, R.F., & Clark, R.N. (1999). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3), 267–287.
  • Kostrzewski, M., Waller, P., Guertin, P., Haberland, J., Colaizzi, P., Barnes, E., Thompson, T., Clarke, T., Riley, E., & Choi, C. (2003). Ground-based remote sensing of water and nitrogen stress. Transactions of the ASAE, 46(1), 29.
  • Lal R. (2008). Soils and sustainable agriculture. A review, Agron. Sustain. Dev. 28, 57–64.
  • Lal R. (2009). Soils and food sufficiency. A review, Agron. Sustain. Dev. 29, 113–133.
  • Larson, W. E., & Pierce, F. J. (1991). Conservation and enhancement of soil quality. In Evaluation for sustainable land management in the developing world: proceedings of the International Workshop on Evaluation for Sustainable Land Management in the Developing World, Chiang Rai, Thailand, 15-21 September 1991. [Bangkok, Thailand: International Board for Soil Research and Management, 1991].
  • Li, Z., Jin, X., Wang, J., Yang, G., Nie, C., Xu, X., & Feng, H. (2015). Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model. International Journal of Remote Sensing, 36(10), 2634–2653.
  • Lichtfouse, E., Navarrete, M., Debaeke, P., Souchère, V., & Alberola, C. (2009). Sustainable Agriculture. Springer, 1st ed., 645 p., ISBN: 978-90-481-2665-1.
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  • Liu, J., Miller, J. R., Haboudane, D., & Pattey, E. (2004). Exploring the relationship between red edge parameters and crop variables for precision agriculture. IEEE International Geoscience and Remote Sensing Symposium (Vol. 2, pp. 1276-1279).
  • Malczewski, J. (2006). Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. International Journal of Applied Earth Observation and Geoinformation, 8(4), 270–277.
  • Matton, N., Canto, G.S., Waldner, F., Valero, S., Morin, D., Inglada, J., Arias, M., Bontemps, S., Koetz, B., & Defourny, P. (2015). An automated method for annual cropland mapping along the season for various globally distributed agrosystems using high spatial and temporal resolution time series. Remote Sensing, 7(10): 13208-13232.
  • Mezera, J., Lukas, V., & Elbl, J. (2017). Evaluation of crop yield spatial variability in relation to variable rate application of fertilizers. MendelNet, 24(1), 2017.000.
  • Mirasi, A., Mahmoudi, A., Navid, H., Valizadeh Kamran, K., & Asoodar, M.A. (2019). Evaluation of sum-NDVI values to estimate wheat grain yields using multi-temporal Landsat OLI data. Geocarto International, 36(12), 1309–1324.
  • Mongkolsawat, C.P., Thirangoon, & Kuptawutinan, P. (2002). A physical evaluation of landsuitability for rice: A methodological study using GIS. Computer Centre, Khon Kaen University, Thailand. http://www. gisdevelopment.net.
  • Moran, M.S., Rahman, A.F., Washburne, J.C., Goodrich, D.C., Weltz, M.A., & Kustas, W.P. (1996). Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland. Agricultural and Forest Meteorology, 80(2), 87–109.
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Determination of the Relationship between Rice Suitability Classes and Satellite Images with Different Time Series for Yeşil Küre Farm Lands

Yıl 2022, Cilt: 32 Sayı: 3, 507 - 526, 30.09.2022
https://doi.org/10.29133/yyutbd.1114636

Öz

In this study, rice land designated for agricultural land suitability indices belonging to the enterprise Yeşil Küre Farm Land with different time series Sentinel-2A satellite images calculated utilizing spectral vegetation index, which are Normalized Difference Vegetation Index and Red Edge Optimized Soil Adjusted Vegetation Index values by statistical comparison of the relationship between rice for monitoring and estimation of potential productivity is presented a different perspective. Firstly, according to the rice suitability assessment for the study area, the area of 5488.9 ha was determined to be suitable for rice cultivation at the S1 and S2 levels, whereas the area of 588.9 ha was determined to be unsuitable. In this study, it was determined that the most successful results for each land conformity class were obtained using the NDVI. In particular, it was determined that August received the highest r2 value (NDVI; 0.8580 and RE-OSAVI; 0.8465) in both vegetation index models at the S1 level, and on the other hand, a higher r2 value was obtained with NDVI.

Kaynakça

  • Ahmed, G. B., Shariff, A.R.M., Balasundram, S.K., & Fikri bin Abdullah, A. (2016). Agriculture land suitability analysis evaluation based multi criteria and GIS approach. IOP Conference Series: Earth and Environmental Science, 37, 012044.
  • Al-Bakri, J.T., & Suleiman, A.S. (2004). NDVI response to rainfall in different ecological zones in Jordan. International Journal of Remote Sensing, 25(19), 3897-3912.
  • Askari, M.S., Cui, J., O’Rourke, S.M., & Holden, N.M. (2015). Evaluation of soil structural quality using VIS–NIR spectra. Soil and Tillage Research, 146, 108–117.
  • Bagheri, N., Ahmadi, H., Alavipanah, S., & Omid, M. (2012). Soil-line vegetation indices for corn nitrogen content prediction. International Agrophysics, 26(2), 103–108.
  • Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., Thompson, T., Lascano, R. J., Li, H., & Moran, M.S. (2000). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, 2000 (Vol. 1619).
  • Blum, W.E.H. (2006). Soil Resources- The basis of human society and the environment. Bodenkultur 57, 197–202.
  • Damian, J.M., Pias, O.H.D.C., Cherubin, M.R., Fonseca, A.Z.D., Fornari, E.Z., Santi, A.L., (2020). Applying the NDVI from satellite images in delimiting management zones for annual crops. Sci. Agric. 77 (1).
  • Dedeoğlu, M., Başayiğit, L., Yüksel, M., & Kaya, F. (2020). Assessment of the vegetation indices on Sentinel-2A images for predicting the soil productivity potential in Bursa, Turkey. Environmental Monitoring and Assessment, 192(1), 1-16.
  • Dengiz, O. (2013). Land Suitability Assessment for Rice Cultivation Based on GIS Modeling. Turkish Journal of Agriculture and Forestry. 37: 326-334, DOI: 10.3906/tar-1204-36.
  • Dengiz, O., & Sağlam, M. (2012). Determination of land productivity index based on parametric approach using GIS technique. Eurasian Journal of Soil Science, 1, 51–57.
  • Doran, J.W., & Parkin, T.B. (1994). Defining and assessing soil quality. Defining soil quality for a sustainable environment, 35, 1-21.
  • Dumanski, J., & Peiretti, R. (2013). Modern concepts of soil conservation. International soil and water conservation research, 1(1), 19-23.
  • Explorer, E. (2000). FS; 083-00; Geological Survey (US).
  • Earth observatory NASA. http://earthobservatory.nasa.gov/Features/MeasuringVegetation.
  • ESRI, (2010). ArcGIS user’s guide. http://www.esri.com
  • FAO, (1983). Land and Water Development Division. Guidelines: land evaluation for rainfed agriculture. Food and Agriculture Organization of the United Nations.
  • FAO (1985). Guideline: land evaluation for irrigated agriculture. FAO Soils Bulletin, No. 55, Rome
  • FAO, I., & ISRIC, I. (2009). Harmonized world soil database (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria. http://www.fao.org/nr/land/soils/harmonized-world-soildatabase/en.
  • Feng, W., Guo, B.-B., Wang, Z. J., He, L., Song, X., Wang, Y. H., & Guo, T.C. (2014). Measuring leaf nitrogen concentration in winter wheat using double-peak spectral reflection remote sensing data. Field Crops Research, 159: 43–52.
  • Fitzgerald, G., Rodriguez, D., & O’Leary, G. (2010). Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—the canopy chlorophyll content index (CCCI). Field Crops Research, 116(3), 318–324.
  • Franch, B., Bautista, A.S., Fita, D., Rubio, C., Tarrazó-Serrano, D., Sánchez, A., Skakun, S., Vermote, E., Becker-Reshef, I., & Uris, A. (2021). Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data. Remote Sensing, 13(20), 4095.
  • Gupta, R.K., Vijayan, D., & Prasad, T.S. (2003). Comparative analysis of red-edge hyperspectral indices. Advances in Space Research, 32(11), 2217–2222.
  • Hillel, D. (2009). The mission of soil science in a changing World. J. Plant Nutr. Soil Sci. 172, 5–9.
  • Hott, M. C., Carvalho, L. M. T. D., Antunes, M. A. H., Santos, P. A. D., Arantes, T. B., Resende, J. C. D., & Rocha, W. S. D. D. (2016). Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor. Pesquisa Agropecuária Brasileira, 51, 858-868.
  • Huang, S., Miao, Y., Yuan, F., Gnyp, M., Yao, Y., Cao, Q., Wang, H., Lenz-Wiedemann, V., & Bareth, G. (2017). Potential of RapidEye and WorldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages. Remote Sensing, 9(3), 227.
  • Hufkens K., Melaas, E.K., Foster, T., Ceballos, F., Robles, M., & Kramer, B. (2019). Monitoring crop phenology using a smartphone based near-surface remote sensing approach. Agricultural and Forest Meteorology, 265: 327-337.
  • Jackson, R.D. (1986). Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology, 24(1), 265–287.
  • Jia, L., Yu, Z., Li, F., Gnyp, M., Koppe, W., Bareth, G., Miao, Y., Chen, X., & Zhang, F. (2011). Nitrogen status estimation of winter wheat by using an Ikonos satellite image in the north china plain. Computer and computing technologis in agriculture V. 5 th IFIP TC5/SIG 5,1 Conference, CCTA 2011 Beijing, Cina, October 2011 Proceedings, Part II.
  • Jiang, L., Liu, Y., Wu, S., & Yang, C. (2021). Analyzing ecological environment change and associated driving factors in China based on NDVI time series data. Ecological Indicators, 129: 107933.
  • Jones, A., Stolbovoy, V., Rusco, E., Gentile, A.R., Gardi, C., Marechal, B., & Montanarella, L. (2009). Climate change in Europe. 2. Impact on soil. A review. Agronomy for sustainable development, 29(3), 423-432.
  • Karlen D.L., Mausbach M.J., Doran J.W., Cline R.G., Harris R.F., & Schuman G.E. (1997). Soil quality: a concept, definition and framework for evaluation. Soil Sci. Soc. Am. J. 61, 4–10.
  • Kerr, J.T., & Ostrovsky, M. (2003). From space to species: ecological applications for remote sensing. Trends in ecology & evolution, 18(6), 299-305.
  • Kokaly, R.F., & Clark, R.N. (1999). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3), 267–287.
  • Kostrzewski, M., Waller, P., Guertin, P., Haberland, J., Colaizzi, P., Barnes, E., Thompson, T., Clarke, T., Riley, E., & Choi, C. (2003). Ground-based remote sensing of water and nitrogen stress. Transactions of the ASAE, 46(1), 29.
  • Lal R. (2008). Soils and sustainable agriculture. A review, Agron. Sustain. Dev. 28, 57–64.
  • Lal R. (2009). Soils and food sufficiency. A review, Agron. Sustain. Dev. 29, 113–133.
  • Larson, W. E., & Pierce, F. J. (1991). Conservation and enhancement of soil quality. In Evaluation for sustainable land management in the developing world: proceedings of the International Workshop on Evaluation for Sustainable Land Management in the Developing World, Chiang Rai, Thailand, 15-21 September 1991. [Bangkok, Thailand: International Board for Soil Research and Management, 1991].
  • Li, Z., Jin, X., Wang, J., Yang, G., Nie, C., Xu, X., & Feng, H. (2015). Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model. International Journal of Remote Sensing, 36(10), 2634–2653.
  • Lichtfouse, E., Navarrete, M., Debaeke, P., Souchère, V., & Alberola, C. (2009). Sustainable Agriculture. Springer, 1st ed., 645 p., ISBN: 978-90-481-2665-1.
  • Liniger, H.P., Studer, R.M., Hauert, C., & Gurtner, M. (2011). Sustainable land management in practice: guidelines and best practices for Sub-Saharan Africa. FAO.
  • Liu, J., Miller, J. R., Haboudane, D., & Pattey, E. (2004). Exploring the relationship between red edge parameters and crop variables for precision agriculture. IEEE International Geoscience and Remote Sensing Symposium (Vol. 2, pp. 1276-1279).
  • Malczewski, J. (2006). Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. International Journal of Applied Earth Observation and Geoinformation, 8(4), 270–277.
  • Matton, N., Canto, G.S., Waldner, F., Valero, S., Morin, D., Inglada, J., Arias, M., Bontemps, S., Koetz, B., & Defourny, P. (2015). An automated method for annual cropland mapping along the season for various globally distributed agrosystems using high spatial and temporal resolution time series. Remote Sensing, 7(10): 13208-13232.
  • Mezera, J., Lukas, V., & Elbl, J. (2017). Evaluation of crop yield spatial variability in relation to variable rate application of fertilizers. MendelNet, 24(1), 2017.000.
  • Mirasi, A., Mahmoudi, A., Navid, H., Valizadeh Kamran, K., & Asoodar, M.A. (2019). Evaluation of sum-NDVI values to estimate wheat grain yields using multi-temporal Landsat OLI data. Geocarto International, 36(12), 1309–1324.
  • Mongkolsawat, C.P., Thirangoon, & Kuptawutinan, P. (2002). A physical evaluation of landsuitability for rice: A methodological study using GIS. Computer Centre, Khon Kaen University, Thailand. http://www. gisdevelopment.net.
  • Moran, M.S., Rahman, A.F., Washburne, J.C., Goodrich, D.C., Weltz, M.A., & Kustas, W.P. (1996). Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland. Agricultural and Forest Meteorology, 80(2), 87–109.
  • MTA, (1974). 1:500000 ölçekli Türkiye Jeoloji Haritası. Harita Genel Müdürlüğü Matbaası, Ankara.
  • Mueller, L., Schindler, U., Mirschel, W., Shepherd, T.G., Ball, B.C., Helming, K., Rogasik J., Eulenstein F., & Wiggering H. (2010). Assessing the productivity function of soils. In Sustainable Agriculture, 2 (pp. 743– 760).
  • Özbek, H., Dinç, U., & Kapur, S. (1974). Çukurova Üniversitesi Yerleşim Sahası Topraklarının Detaylı Etüd ve Haritalaması. Ankara Üniversitesi Basımevi, Ankara.
  • Özcan, H. (2004). Çinko Uygulamasının Bazı Çeltik Çeşitlerinde Verim Đle Tanede Çinko, Fosfor ve Fitin Asidi Konsantrasyonuna Etkisi. A.Ü. Fen Bil. Ens. Toprak Anabilim Dalı, Doktora Tezi, Ankara.
  • Panda, S.S., Ames, D.P., & Panigrahi, S. (2010). Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques. Remote Sensing, 2(3), 673–696.
  • Pettorelli, N., Mysterud, A., Yoccoz, N.G., Langvatn, R., & Stenseth, N.C. (2005a). Importance of climatological downscaling and plant phenology for red deer in heterogeneous landscapes. Proceedings of the Royal Society B: Biological Sciences, 272(1579), 2357-2364.
  • Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.M., Tucker, C.J., & Stenseth, N.C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in ecology & evolution, 20(9), 503-510.
  • Pradipta, D., (2012). Analisis Data Time Series NDVI-SPOT Vegetation Untuk Tanaman Padi (Studi Kasus: Kabupaten Karawang). Institut Pertanian Bogor, Bogor.
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  • Vohland, M., Ludwig, M., Thiele-Bruhn, S., & Ludwig, B. (2014). Determination of soil properties with visible to near- and mid-infrared spectroscopy: effects of spectral variable selection. Geoderma, 223-225: 88–96.
  • Vorobiova, N., & Chernov, A. (2017). Curve fitting of MODIS NDVI time series in the task of early crops identification by satellite images. Procedia engineering, 201: 184-195.
  • Walter, C., & Stützel, H. (2009). A new method for assessing the sustainability of land-use systems (I): Identifying the relevant issues, Ecol. Econ. 68, 1275–1287.
  • Wójtowicz, M., Wójtowicz, A., & Piekarczyk, J. (2016). Application of remote sensing methods in agriculture. Communications in Biometry and Crop Science, 11(1), 31– 50.
  • Wu, C., Niu, Z., Tang, Q., & Huang, W. (2008). Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agricultural and Forest Meteorology, 148(8), 1230–1241.
  • Xue, R., Wang, C., Liu, M., Zhang, D., Li, K., & Li, N. (2019). A new method for soil health assessment based on analytic hierarchy process and meta-analysis. Science of the Total Environment, 650, 2771–2777.
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Toplam 72 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Toprak Bilimi ve Ekolojisi
Bölüm Makaleler
Yazarlar

Orhan Dengiz 0000-0002-0458-6016

Mert Dedeoğlu 0000-0001-8611-3724

Nursaç Serda Kaya 0000-0001-9814-5651

Yayımlanma Tarihi 30 Eylül 2022
Kabul Tarihi 20 Haziran 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 32 Sayı: 3

Kaynak Göster

APA Dengiz, O., Dedeoğlu, M., & Kaya, N. S. (2022). Determination of the Relationship between Rice Suitability Classes and Satellite Images with Different Time Series for Yeşil Küre Farm Lands. Yuzuncu Yıl University Journal of Agricultural Sciences, 32(3), 507-526. https://doi.org/10.29133/yyutbd.1114636

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