[1] ZHANG Mao, ZHANG Xia, HU Guangcheng 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] LIU Lu, GUO Zhaoxia, CHAI Qian et al. Assess ment of freezing risk at apple florescence in Shaanxi Province. Agricultural Research in the Arid Areas, 27, 251-255(2009).

[3] LI Meixiu, LIU Yingning, LI Yanli. Main meteorological disasters and countermeasures of fruit industry in Shaanxi Province. Shaanxi Journal of Agricultural Sciences, 60-62(2006).

[4] ZHAO Yanxi, XIAO Dengpan, BAI Huizi et al. Research progress on the response and adaptation of crop phenology toclimate change in China. Progress in Geography, 38, 224-235(2019).

[5] LIU Yujie, GE Quansheng, DAI Junhu. Research progress in crop phenology under global climate change. Acta Geographica Sinica, 75, 14-24(2020).

[6] BAI Qinfeng, HUO Zhiguo, WANG Jinghong 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] DARVYSHIRE R. A global evaluation of apple flowering phenology models for climate adaptation. Agricultural and Forest Meteorology, 240-241, 67-77(2017).

[8] PU Jinyong, YAO Xiaoying, YAO Xiaohong 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] MENG Xiumei, LI Shikui, TONG Wanfu et al. A method of forec-asting the blooming date of apple trees. Acta Horticulturae Sinica, 159-164(1983).

[10] MAO Mingce, LIU Minru, JIANG Chuangye et al. A Study on relationship bet ween air temperature and early blooming time of Malus. Chinese Journal ofAgrometeorology, 2005, 123-128.

[11] LI Xingmin, BAI Qingfeng. Prediction model for beginning of apple flowering period in fruit growing areas of Shanxi Province. Chinese Journal of Agrometeorolog, 30, 417-420(2009).

[12] CHAO Zhenghua, CHE Mingliang, HOU Shengfang. 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] FAN D Q, ZHAO X S, ZHU W Q 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] SUN L, GAO F, XIE D H et al. Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach. Remote Sensing of Environment, 112156(2020).

[15] BASLER D. 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] WANG S Y, LU X C, CHENG X 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] CHEN B, JIN Y F, BROW P 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] ZHANG Z, LOU Y S, MOSES O A et al. Hyperspectralremote sensing to quantify the flowering phenology of winter wheat. Spectroscopy Letters, 52, 389-397(2019).

[19] BERRA E F, GAULTON R. 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] GUZMAN J P, DASH J, ATKINSON P M. Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sensing of Environment, 205, 71-84(2018).

[21] PEIRS A. 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] PEIRS A, SCHENK A, NICOLAı̈ B M. 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] NOORMETS A. Phenology of Ecosystem Processes. N Y, 10(2009).

[24] LIU L C, CAO R Y, SHEN M G et al. How does scale effect influence spring vegetation phenology estimated from satellite-derived vegetation indexes？. Remote Sensing, 11, 2137-2137(2019).

[25] HANES J M. Land surface phenology/ Biophysical Applications of Satellite Remote Sensing, 99-125(2014).

[26] SAKAMOTO T. 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] GAO FENG, ANDERSON MC, ZHANG Xiaoyang 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] GAO FENG, MASEK J, SCHWALLER M 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] Hilker T. 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] ZHU X L, HELMER E H, GAO F et al. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172, 165-177(2016).