[1] Mao ZHANG, Xia ZHANG, Guangcheng HU et al. Applicability analysis of remote sensing based drought indices in drought monitoring of apple in Luochuan. Remote Sensing Technology and Application, 36, 187-197(2021).
[2] Lu LIU, Zhaoxia GUO, Qian CHAI et al. Assess ment of freezing risk at apple florescence in Shaanxi Province. Agricultural Research in the Arid Areas, 27, 251-255(2009).
[3] Meixiu LI, Yingning LIU, Yanli LI. Main meteorological disasters and countermeasures of fruit industry in Shaanxi Province. Shaanxi Journal of Agricultural Sciences, 60-62(2006).
[4] Yanxi ZHAO, Dengpan XIAO, Huizi BAI et al. Research progress on the response and adaptation of crop phenology toclimate change in China. Progress in Geography, 38, 224-235(2019).
[5] Yujie LIU, Quansheng GE, Junhu DAI. Research progress in crop phenology under global climate change. Acta Geographica Sinica, 75, 14-24(2020).
[6] Qinfeng BAI, Zhiguo HUO, Jinghong WANG et al. Simulation and distribution of flower stage in main production areas of Fuji apple in China. Chinese Journal of Agrometeorology, 41, 423-435(2020).
[7] R DARVYSHIRE. A global evaluation of apple flowering phenology models for climate adaptation. Agricultural and Forest Meteorology, 240-241, 67-77(2017).
[8] Jinyong PU, Xiaoying YAO, Xiaohong YAO et al. Impacts of climatewarming on phenological period and growth of apple tree in Loess-Plateau of Gansu Province. Chinese Journal of Agrometeorology, 2008, 181-183+187.
[9] Xiumei MENG, Shikui LI, Wanfu TONG et al. A method of forec-asting the blooming date of apple trees. Acta Horticulturae Sinica, 159-164(1983).
[10] Mingce MAO, Minru LIU, Chuangye JIANG et al. A Study on relationship bet ween air temperature and early blooming time of Malus. Chinese Journal ofAgrometeorology, 2005, 123-128.
[11] Xingmin LI, Qingfeng BAI. Prediction model for beginning of apple flowering period in fruit growing areas of Shanxi Province. Chinese Journal of Agrometeorolog, 30, 417-420(2009).
[12] Zhenghua CHAO, Mingliang CHE, Shengfang HOU. Brief review of vegetation phenological information extraction software based on time series remote sensing data. Remote Sensing for Natural Resources, 33, 19-25(2021).
[13] D Q FAN, X S ZHAO, W Q ZHU et al. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data. Progress in Geography, 35, 304-319(2016).
[14] L SUN, F GAO, D H XIE et al. Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach. Remote Sensing of Environment, 112156(2020).
[15] D BASLER. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe. Agricultural and Forest Meteorology, 217, 10-21(2016).
[16] S Y WANG, X C LU, X CHENG et al. Limitations and Challenges of MODIS-Derived Phenological Metrics Across Different Landscapes in Pan-Arctic Regions. Remote Sen-sing, 10, 1784-1784(2018).
[17] B CHEN, Y F JIN, P BROW et al. An enhanced bloom index for quantifying floral phenology using multi-scale remote sensingobservations. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 108-120(2019).
[18] Z ZHANG, Y S LOU, O A MOSES et al. Hyperspectralremote sensing to quantify the flowering phenology of winter wheat. Spectroscopy Letters, 52, 389-397(2019).
[19] E F BERRA, R GAULTON. Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunitiesin the intercomparison of in-situ and satellite phenological metrics. Forest Ecology and Management, 480, 118663(2021).
[20] J P GUZMAN, J DASH, P M ATKINSON. Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sensing of Environment, 205, 71-84(2018).
[21] A PEIRS. Prediction of the optimal picking date of different apple cultivars by means of VIS/NIR-spectroscopy. Postharvest Biology and Technology, 21, 189-199(2001).
[22] A PEIRS, A SCHENK, B M NICOLAı̈. Effect of natural variability among apples on the accuracy of VIS-NIR calibration models for optimal harvest date predictions. Postharvest Biology and Technology, 35, 1-13(2004).
[23] A NOORMETS. Phenology of Ecosystem Processes. N Y, 10(2009).
[24] L C LIU, R Y CAO, M G SHEN et al. How does scale effect influence spring vegetation phenology estimated from satellite-derived vegetation indexes?. Remote Sensing, 11, 2137-2137(2019).
[25] J M HANES. Land surface phenology/ Biophysical Applications of Satellite Remote Sensing, 99-125(2014).
[26] T SAKAMOTO. A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sensing of Environment, 114, 2146-2159(2010).
[27] FENG GAO, MC ANDERSON, Xiaoyang ZHANG et al. Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sensing of Environment, 188, 9-25(2017).
[28] FENG GAO, J MASEK, M SCHWALLER et al. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44, 2207-2218(2006).
[29] T Hilker. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113, 1613-1627(2009).
[30] X L ZHU, E H HELMER, F GAO et al. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172, 165-177(2016).