به کارگیری الگوریتم ادغام مکانی-زمانی در استخراج شاخص های طیفی گیاه در مراحل رشد برنج (مورد مطالعه: شمال شهرستان ساری)

1.Jafari Sayadi, F. 2016. Application of remote sensing for estimating rice cultivation and water consumption. M.Sc. Thesis, Sari Agricultural Sciences and Natural Resources University (SANRU). (In Persian)

2.Ahmadi, K., Abaszadeh, H., Hatami, F., Abdshah, H. and Kazemian, A. 2019. Agricultural statistics report, 2017-2018 for crop yields. Crops. Ministry of Jihad Agriculture, Deputy of Planning and Economy. Information and Communication Technology Center. 163p. (In Persian)

3.Behrang Manesh, M., Khosravi, H., Azarnivand, H. and Senatore, A. 2019. Quantifying the trend of vegetation changes using remote sensing (Case study: Fars Province). J. Plant Ecosyst. Conservation. 7: 15. 295-318. (In Persian)

4.Pordel, F., Ebrahimi, A.A. and Azizi,Z. 2017. Evaluating spatio-temporal phytomass changes using vegetation index derived from Landsat 8 (Case study: Mrajan rangeland, Boroujen). J. Rangeland. 2: 166-178. (In Persian)

5.Sanaeinejad, H., Nassiri Mahallati, M., Zare, H., Salehnia, N. and Ghaemi, M. 2014. Wheat yield estimation using Landsat images and field observation: A case study in Mashhad. J. Plant Prod.20: 4. 45-63. (In Persian)

6.Rouse, J.W., Haas, R.H., Schell, J.A.and Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. P 309-317, In: S. C. Freden (eds), 3^rd Earth Resource Technology Satellite (ERTS), Symposium. Washington. D.C. USA.

7.Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. of Environ. J. 25: 295-309.

8.Nuarsa, I.W., Nishio, F. and Hongo, C. 2011. Spectral characteristics and mapping of rice plants using multi-temporal Landsat data. Agric. Sci. J.3: 54-67.

9.Jafari Sayadi, F., Gholami Sefidkhohi, M.A. and Ziyaeetabar Ahmadi, M.K. 2018. Leaf area index and crop coefficient estimation from operational land imager (OLI) sensor data. J. Water Res. Agric. 32: 395-404. (In Persian)

10.Geo, F., Anderson, M.C., Kustas, W.P. and Wang, Y. 2012. Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference. App. Remote Sens. J.6: 1-15.

11.Ihlen, V. and Zanter, K. 2019. Landsat 8 (L8) data handbook. Department of the Interior U.S. Geological Survey (USGS). South Dakota, USA. 96p.

12.Hoersch, B. 2015. SENTINEL-2 user handbook. European Space Agency (ESA).Europe. 64p.

13.Attarchi, S. and Poorakbar, N. 2020. Preliminary comparative assessment of Sentinel 2 and Landsat 8 (MSI and OLI sensors) images. Sepehr J. 29: 114. 67-78. (In Persian)

14.Rakhsh Mahpour, A. 2016. Evaluating Spatial-Temporal image fusion algorithms for MODIS and Landsat data in the land cover application. M.Sc. Thesis, Ferdowsi University of Mashhad. (In Persian)

15.Fu, D., Chen, B., Wang, J., Zhu, X. and Hilker, T. 2013. An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model. Remote Sens. J. 5: 6346-6360.

16.Walker, J.J., De Beurs, K.M., Wynne, R.H. and Gao, F. 2012. Evaluation of Landsat and MODIS data fusion products for analysis of dry landforest phenology. Rem. Sens. Environ.117: 381-393.

17.Wu, M., Wu, C., Huang, W., Niu, Z., Wang, C., Li, W. and Hao, P. 2016. An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery. Info Fusion, J. 31: 14-25.

18.Mokhtari, A., Noory, H., Vazifedoust, M., Palouj, M., Bakhtiari, A., Barikani, E., Zabihi Afrooz, R.A., Fereydooni, F., Sadeghi Naeni, A., Pourshakouri, F. Badiehneshin, A.R. and Afrasiabian, Y. 2019. Evaluation of single crop coefficient curves derived from Landsat satellite image for major crops in Iran. Agric. Water Manag. J. 218: 234-249.

19.Moreno-Martίnez, Á., Izquierdo-Verdiguier, E., Maneta, M.P., Camps-Valls, G., Rpbinson, N., Muñoz-Marί, J., Sedano, F., Clinton, N. and Running, S. W. 2020. Multispectral high resolution sensor fusion for smoothing andgap-filling in the cloud. Remote Sens. Environ. J. 247: 1-19.

20.Guo, Y., Wang, C., Lei, S., Yang, J. and Zhao, Y. 2020. A framework of spatio-temporal fusion algorithm selection for Landsat NDVI time series construction. Geo-Inf, J. 665: 1-21.

21.Kumar Ranjan, A. and Ranjan Parida, B. 2021. Predicting paddy yield at spatial scale using optical and Synthetic Aperture Radar (SAR) based satellite data in conjunction with field-based crop cutting experiment (CCE) data. Remote Sens. 42: 2046-2071.

22.Vermote, E.F., Roger, J.C. and Ray, J.P. (2015). MODIS surface reflectance user's guide (MOD 09). MODIS land surface reflectance science computing facility. 35p.

23.Samadzadegan, F., Tabib Mahmoudi, F. and Bigdeli, B. 2014. Data fusion in remote sensing concepts and techniques. Tehran Univ. Press. 2^th Edition. 275p. (In Persian)

24.Bazrgar Bojestani, A. and Akhoondzadeh Hanzaii, M. 2018. ESTARFM model for fusion of LST products of MODIS and ASTER sensors to retrieve the high resolution land surface temperature map. J. Geo_Sci & Tec (jgst). 7: 4. 147-161. (In Persian)

25.Palaniswany, K.M. and Gomez, K.A. 1974. Length-width method for estimating leaf area of rice. Agron. J.66: 430-433.

26.Yoshida, S. 1981. Fundamentals of rice crop science. The international rice research institute (IRRI). Philippines. 269p.

27.Wang, X., Mosley, C.T., Frankenberger, J.R. and Kladivko, E.J. 2006. Subsurface drain flow and crop yield predictions
for different drain spacings using DRAINMOD. Agric. Water. Manag.J. 79: 113-136.

28.Castro, A.I., Six, J., Plant, R.E. and Peǹa, J.M. 2018. Mapping crop calendar events and phenology-related metrics at the parcel level by object-based image analysis OBIA of MODIS-NDVI time-series (A case study in central California). Remote Sens. J. 10: 1-21.

29.Wang, J. Huang, J.F., Wang, X.Z., Jin, M.T., Zhou, Z., Guo, Q.Y., Zhao, Z.W., Huang, W.J., Zhang, Y. and Song, X.D. 2015. Estimation of rice phenology date using interated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images. Zhejiang Univ. J. Biomed. Biotechnol. 16: 832-844.

30.Bakhshandeh, A., Hoseyni, M., Farzin, N. and Pirdashti, H. 2016. Introducing a simple and fast method for estimating rice leaf area. P 1-4, In: H, Pirdashti (eds), 17^th National Rice Conference, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari. (In Persian)

31.Campos-Taberner, M., Garcia-Haro, F.J., Busetto, L., Ranghetti, L., Martinez, B., Amoaro Gilabert, M., Camps-Valls, G., Camacho, F. and Boschetti, M. 2018. A critical comparison of remote sensing leaf area index estimates over rice-cultivated areas: from Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT polar system. Remote Sens. J. 10: 1-23.

32.Wei, C., Chen, J., Chen, J. M., Yu, J.C., Cheng, C., Lai, Y.J., Chiang, P. N., Hong, C.Y., Tsai, M.J. and Wang, N. 2020. Evaluating relationships of standing stock, LAI and NDVI at a subtropical reforestation site in southern Taiwan using field and satellite data. Forest Res. J. 31: 1-10.

33.Rees, W.G., Golubeva, E.I., Tutubalina, O.V., Zimin, M.V. and Derkacheva, A.A. 2020. Relationship between leaf area index and NDVI for subarctic deciduous vegetation. Int. Remote Sens. J. pp. 22-41.

34.Xie, D., Zhang, J., Zhu, X., Pan, Y., Liu, H., Yuan, Z. and Yun, Y. 2016. An improved STARFM with help an unmixing-based method to generate high spatial and temporal resolution remote sensing data in complex heterogeneous regions. Sensors, J. 16: 1-19.

35.Mokhtari, S., Pirmoradian, N., Vazifehdoost, M. and Davatgar, N. 2013. Increasing accuracy of regional rice yield estimation by improvement of spatial resolution of leaf area index maps in VSM vegetative model. Guilan, J. Cereal Res. 2: 209-221. (In Persian)