پیش بینی وقوع بیماری زنگ نوار گندم با استفاده از مطالعه سری زمانی تصاویر ماهواره سنتینل-2 : مطالعه موردی نینگ یانگ چین

1-Birr, T.; Verreet, J.A.; Klink, H. (2019). Prediction of deoxynivalenol and zearalenone in winter wheat grain in a maize-free crop rotation based on cultivar susceptibility and meteorological factors. J. Plant Dis. Prot., 126, 13–27. 

2-Jarroudi, M.; Kouadio, L.; Bock, C.H.; Jarroudi, M.E.; Junk, J.; Pasquali, M.; Maraite, H.; Delfosse, P. (2016). A threshold-based weather model for predicting stripe rust infection in winter wheat. Plant Dis., 101, 693–703. 

3-Yuan, L.; Pu, R.; Zhang, J.; Wang, J.; Yang, H. (2016). Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale. Precis. Agric., 17, 332–348. 

4-Zhang, J.; Yuan, L.; Nie, C.; Wei, L.; Yang, G. (2014). Forecasting of powdery mildew disease with multi-sources of remote sensing information. In Proceedings of the 2014 The Third International Conference on Agro-Geoinformatics, Beijing, China, 11–14 August 2014; pp. 1–5.

5-Zheng, Q.; Huang, W.; Cui, X.; Yue, S.; Liu, L. (2018). New spectral index for detecting wheat yellow rust using Sentinel-2 multispectral imagery. Sensors, 18, 868. 

6-Dutta, S.; Singh, S.K.; Khullar, M. (2014). A case study on forewarning of yellow rust affected areas on wheat crop using satellite data. J. Indian Soc. Remote Sens., 42, 335–342. 

7-Du, X.; Li, Q.; Shang, J.; Liu, J.; Qian, B.; Jing, Q.; Dong, T.; Fan, D.; Wang, H.; Zhao, L. (2019). Detecting advanced stages of winter wheat yellow rust and aphid infection using RapidEye data in North China Plain. GISci. Remote Sens., 56, 1093–1113. 

8-Pryzant, R.; Ermon, S.; Lobell, D. (2017). In Monitoring Ethiopian wheat fungus with satellite imagery and deep feature learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, 21–26 July 2017; pp. 39– 47.

9-Dengke, L.; Zhao, W.; Feizhou, X. (2019). Occurrence regularity and meteorological influencing factors of wheat stripe rust in Shaanxi province. J. Catastrophol. 34, 59–.56

10-Korhonen, L.; Packalen, P.; Rautiainen, M. (2017). Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sens. Environ., 195, 259–274. 

11-Li, Z.; Yang, P.; Zhou, Q.; Wang, Y.; Wu, W.; Zhang, L.; Zhang, X. (2009). Research on spatiotemporal pattern of crop phenological characteristics and cropping system in North China based on NDVI time series data. Acta Ecol. Sin. 29, 6216–.6226

12-Wang, L.; Liu, J.; Yang, F.; Fu, C.; Teng, F.; Gao, J. (2015). Early recognition of winter wheat area based on GF-1 satellite. Trans. Chin. Soc. Agric. Eng. 31, 194–.102

پیشبینی سیدحمیدرضا وقوع بیماری میرنعمتی  زنگ نوار گندم با استفاده از سری زمانی تصاویر سنتینل-8                998

13-Huang, L.; Ruan, C.; Huang, W.; Shi, Y.; Peng, D.; Ding, W. (2018). Wheat powdery mildew monitoring based on GF-1 remote sensing image and relief-mRMR-GASVM model. Trans. Chin. Soc. Agric. Eng., 34, 167–175.

14-Zhang, L.X.; Wang, J.X.; Zhao, Y.N.; Yang, Z.H. (2004). Combination feature selection based on relief. J. Fudan Univ. Nat. Sci. Ed., 43, 893–898.

15-Jarroudi, M.; Lahlali, R.; Kouadio, L.; Denis, A.; Tychon, B. (2020). Weather-based predictive modeling of wheat stripe rust infection in Morocco. Agronomy, 10, 280. 

16-Roujean, J.-L.; Breon, F.-M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ51, 375–384. 

17-Peñuelas, J.; Inoue, Y. (1999). Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica, 36, 355–360. 

18-Jordan, C.F. Derivation of leaf-area index from quality of light on the forest floor. Ecology 1969, 50, 663–666. 

19-Rondeaux, G.; Steven, M.; Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 55, 95–.701 

20-Broge, N.H.; Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76, 156–172. 

21-Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ., 83, 195–213.