Cai, Z., Jönsson, P., Jin, H., & Eklundh, L. (2017). Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data. Remote Sensing, 9(12), 1271. https://doi.org/10.3390/rs9121271
Caparros-Santiago, J.A., Rodriguez-Galiano, V., & Dash, J. (2021). Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 171, 330-347. https://doi.org/10.1016/j.isprsjprs.2020.11.019
Chen, X., Wang, W., Chen, J., Zhu, X., Shen, M., Gan, L., & Cao, X. (2020). Does any phenological event defined by remote sensing deserve particular attention? An examination of spring phenology of winter wheat in Northern China. Ecological Indicators, 116, 106456. https://doi.org/10.1016/j.ecolind.2020.106456
Han, H., Bai, J., Ma, G., & Yan, J. (2020). Vegetation phenological changes in multiple landforms and responses to climate change. ISPRS International Journal of Geo-Information, 9(2), 111. https://doi.org/10.3390/ijgi9020111
Huang, X., Liu, J., Zhu, W., Atzberger, C., & Liu, Q. (2019). The optimal threshold and vegetation index time series for retrieving crop phenology based on a modified dynamic threshold method.
Remote Sensing,
11(23), 2725.
https://doi.org/10.3390/rs11232725
Huete, A.R., Liu, H.Q., Batchily, K.V., & Van Leeuwen, W.J.D.A. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote sensing of environment, 59(3), 440-451. https://doi.org/10.1016/S0034-4257(96)00112-5
Izadi, H., Savari Mombeni, A., & Savari, M. (2022). Explaining the factors affecting the protection behavior of Zagros forests using the Norm Activation Model (NAM). Iranian Journal of Forest, 14(3), 307-327. https://doi.org/10.22034/ijf.2022.313453.1811 (In persian)
Kiapasha, K.H., Darvishsefat, A.A., Zargham, N., Julien, Y., Sobrino, J.A., & Nadi, M. (2017). Shifts of Start and End of Season in Response to Air Temperature Variation Based on Gimms Dataset in Hyrcanian Forests. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 155-160. https://doi.org/10.5194/isprs-archives-XLII-4-W4-155-2017
Kowalski, K., Senf, C., Hostert, P., & Pflugmacher, D. (2020). Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series. International Journal of Applied Earth Observation and Geoinformation, 92, 102172. https://doi.org/10.1016/j.jag.2020.102172
Liu, J., Zhu, W., Atzberger, C., Zhao, A., Pan, Y., & Huang, X. (2018). A phenology-based method to map cropping patterns under a wheat-maize rotation using remotely sensed time-series data. Remote Sensing, 10(8), 1203. https://doi.org/10.3390/rs10081203
Masihpoor, M., Darvishsefat, A.A., Rahmani, R., & Fateh, P. (2021). Phenological parameters trend of the southern Zagros forests based on MODIS-NDVI time series during 2000-2017. Iranian Journal of Forest, 12(4), 577-590. https://doi.org/10.22034/ijf.2021.127807. (In persian)
Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors, 7(11), 2636-2651. https://doi.org/10.3390/s7112636
McCloy, K.R. (2010). Development and evaluation of phenological change indices derived from time series of image data.
Remote Sensing,
2(11), 2442-2473.
https://doi.org/10.3390/rs2112442
Miri, N., Fatehi, P., Darvishsefat, A.A., Bavaghar, M.P., & Homolová, L. (2024). Leaf area index estimation in the Zagros forests of Iran using Sentinel-2 image and Gaussian Process Regression. Iranian Journal of Forest and Poplar Research, 31(4). https://doi.org/10.22092/ijfpr.2023.364041.2129 (In persian)
Salehi, A., & Eriksson, L.O. (2010). A Management Model for Persian Oak A Model for Management of Mixed Coppice Stands of Semiarid Forests of Persian Oak. Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 2(1), 20-29.
Son, N.T., Chen, C.F., Chen, C.R., Minh, V.Q., & Trung, N.H. (2014). A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agricultural and forest meteorology, 197, 52-64. https://doi.org/10.1016/j.agrformet.2014.06.007
Stanimirova, R., Cai, Z., Melaas, E.K., Gray, J.M., Eklundh, L., Jönsson, P., & Friedl, M.A. (2019). An empirical assessment of the MODIS land cover dynamics and TIMESAT land surface phenology algorithms. Remote Sensing, 11(19), 2201. https://doi.org/10.3390/rs11192201
Sun, Q., Jiao, Q., Qian, X., Liu, L., Liu, X., & Dai, H. (2021). Improving the retrieval of crop canopy chlorophyll content using vegetation index combinations. Remote Sensing, 13(3), 470. https://doi.org/10.3390/rs13030470
Testa, S., Soudani, K., Boschetti, L., & Mondino, E.B. (2018). MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests. International journal of applied earth observation and geoinformation, 64, 132-144. https://doi.org/10.1016/j.jag.2017.08.006
Tian, J., Zhu, X., Chen, J., Wang, C., Shen, M., Yang, W., ... & Li, Z. (2021). Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency.
ISPRS Journal of Photogrammetry and Remote Sensing,
180, 29-44.
https://doi.org/10.1016/j.isprsjprs.2021.08.003
Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
Vaiphasa, C. (2006). Consideration of smoothing techniques for hyperspectral remote sensing. ISPRS journal of photogrammetry and remote sensing, 60(2), 91-99. https://doi.org/10.1016/j.isprsjprs.2005.11.002
Verrall, B., & Pickering, C.M. (2020). Alpine vegetation in the context of climate change: A global review of past research and future directions. Science of the Total Environment, 748, 141344. https://doi.org/10.1016/j.scitotenv.2020.141344
Vrieling, A., Meroni, M., Darvishzadeh, R., Skidmore, A.K., Wang, T., Zurita-Milla, R., ... & Paganini, M. (2018). Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island. Remote sensing of environment, 215, 517-529. https://doi.org/10.1016/j.rse.2018.03.014
Xu, X., Conrad, C., & Doktor, D. (2017). Optimising phenological metrics extraction for different crop types in Germany using the moderate resolution imaging spectrometer (MODIS). Remote Sensing, 9(3), 254. https://doi.org/10.3390/rs9030254
Zarei, A.R., Shabani, A., & Mahmoudi, M.R. (2019). Comparison of the climate indices based on the relationship between yield loss of rain-fed winter wheat and changes of climate indices using GEE model. Science of the Total Environment, 661, 711-722. https://doi.org/10.1016/j.scitotenv.2019.01.204
Zhang, Q., Kong, D., Shi, P., Singh, V.P., & Sun, P. (2018). Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). Agricultural and forest meteorology, 248, 408-417. https://doi.org/10.1016/j.agrformet.2017.10.026
Zhang, J., Zhao, J., Wang, Y., Zhang, H., Zhang, Z., & Guo, X. (2020). Comparison of land surface phenology in the Northern Hemisphere based on AVHRR GIMMS3g and MODIS datasets. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 1-16. https://doi.org/10.1016/j.isprsjprs.2020.08.020
Zhao, J., Zhang, H., Zhang, Z., Guo, X., Li, X., & Chen, C. (2015). Spatial and temporal changes in vegetation phenology at middle and high latitudes of the Northern Hemisphere over the past three decades. Remote Sensing, 7(8), 10973-10995. https://doi.org/10.3390/rs70810973
Zhou, X., Geng, X., Yin, G., Hänninen, H., Hao, F., Zhang, X., & Fu, Y.H. (2020). Legacy effect of spring phenology on vegetation growth in temperate China.
Agricultural and Forest Meteorology,
281, 107845.
https://doi.org/10.1016/j.agrformet.2019.107845
