References
- Atzberger, C. (2013). Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. Remote Sens., 5, 949–981.10.3390/rs5020949
- Bolton, D. K., Friedl, M. A. (2013). Forecasting crop yield using remotely sensed vegetation indices and crop phenologymetrics. Agric. For. Meteorol., 173, 74–84.10.1016/j.agrformet.2013.01.007
- Clement, S., Lassman, F., Barley, E., Evans-Lacko, S., Williams, P., Yamaguchi, S., Slade, M., Rüsch, N., Thornicroft, G. (2013). Mass media interventions for reducing mental health-related stigma (Review). The Cochrane Library, (7).
- De la Casa, A., Ovando, G., Bressanini, L., Martínez, J., Díaz, G., Miranda, C. (2018). Soybean crop coverage estimation from NDVI images with different spatial resolution evaluate yield variability in a plot. ISPRS J. Photogramm. Remote Sens., 146, 531–547.10.1016/j.isprsjprs.2018.10.018
- Dempewolf, J., Adusei, B., Becker-Reshef, I., Hansen, M., Potapov, P., Khan, A., Barker, B. (2014). WheatyieldforecastingforPunjabProvincefromvegetation index time series and historic crop statistics. Remote Sens., 6, 9653–9675. FAOSTAT (2018). website. http://www.fao.org/faostat/en/#data/QC/Query date: 2020. 05.
- Ferencz, Cs., Bognár, P., Lichtenberge, J., Hamar, D., Tarcsai, GY., Timár, G., Molnár, G., Pásztor, Sz., Steinbach, P., Székely, B., Ferencz, O. E., Ferencz-Árkos, I. (2004). Crop yield estimation by satellite remote sensing. Int. J. Remote Sens., 25(20), 4113–4149.10.1080/01431160410001698870
- Labus, M. P., Nielsen, G. A., Lawrence, R. L., Engel, R., Long, D. S. (2002). Wheat yield estimates using multi-temporal NDVI satellite imagery. International Journal of Remote sensing, 23(20), 4169-4180.10.1080/01431160110107653
- Marti, J., Bort, J., Slafer, G. A., Araus, J. L. (2007). Can wheat yield be assessed by early measurements of normalized difference vegetation index? Annals of Applied Biology, 150, 253–257.10.1111/j.1744-7348.2007.00126.x
- 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, 385–393.10.1016/j.agrformet.2010.11.012
- Nagy, A., Fehér, J., Tamás, J. (2018).Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics. Computers and Electronics in Agriculture, 151, 41–49.10.1016/j.compag.2018.05.035
- Panda, S. S., Ames, D. P., Panigrahi, S. (2010). Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sens., 2, 673–696.10.3390/rs2030673
- Szász, G. (2005). Termésingadozást kiváltó éghajlati változékonyság a Kárpát-medencében. “Agro-21” füzetek, (40) 33–69.
- Tamás, J., Nagy, A., Fehér, J. (2015). Agricultural biomass monitoring on water sheds based on remotely sensed data. Water Science and Technology, 72(12), 2212–2220.10.2166/wst.2015.42326676009
- Tewkesbury, A. P., Comber, A. J., Tate, N. J., Lamb, A., Fisher, P. F. (2015). A critical synthesis of remotely sensed optical image changed detection techniques. Remote Sensing of Environment, 160, 1–14.10.1016/j.rse.2015.01.006
- Tiecheng, B., Nannan, Z., Benoit, M., Youqi, C. (2019). Jujube yield prediction method combining Landsat 8 Vegetation Index and the phenological length. Computers and Electronics in Agriculture, 162, 1011–1027.
- Vicente-Serrano, S. M., Cabello, D., Tomás-Burguera, M., Martín-Hernández, N., Beguería, S., Azorin-Molina, C., Kenawy, A. E. (2015). Droughtvariability and land degradation in semiarid regions: assessment using remote sensing data and drought indices (1982–2011). Remote Sens., 7, 4391–4423.10.3390/rs70404391