[1] |
Kim J S, Seo G S, Jang H W, et al. Correlation analysis between Korean spring drought and large-scale teleconnection patterns for drought forecasting[J]. KSCE Journal of Civil Engineering, 2017, 21(1): 458-466.
doi: 10.1007/s12205-016-0580-8
|
[2] |
Mishra A, Desai V. Drought forecasting using stochastic models[J]. Stochastic Environmental Research and Risk Assessment, 2005, 19(5): 326-339.
doi: 10.1007/s00477-005-0238-4
|
[3] |
Wilhite D A, Svoboda M D, Hayes M J. Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness[J]. Water resources management, 2007, 21(5): 763-77.
doi: 10.1007/s11269-006-9076-5
|
[4] |
Mishra A K, Singh V P. A review of drought concepts[J]. Journal of Hydrology, 2010, 391(1-2): 202-216.
doi: 10.1016/j.jhydrol.2010.07.012
|
[5] |
Frankenberg C. New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity[J]. Geophysical Research Letters, 2011, 38(17): 351-365.
|
[6] |
Joiner J, Guanter L, Lindstrot R, et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near- infrared satellite measurements: methodology, simulations, and application to GOME-2[J]. Atmospheric Measurement Techniques, 2013, 6(10): 2803-2823.
doi: 10.5194/amt-6-2803-2013
|
[7] |
Guanter L, Rossini M, Colombo R, et al. Using field spectroscopy to assess the potential of statistical approaches for the retrieval of sun-induced chlorophyll fluorescence from ground and space[J]. Remote Sensing of Environment, 2013, 133: 52-61.
doi: 10.1016/j.rse.2013.01.017
|
[8] |
Joiner J, Yoshida Y, Vasilkov A, et al. First observations of global and seasonal terrestrial chlorophyll fluorescence from space[J]. Biogeosciences, 2011, 8(3): 637-651.
doi: 10.5194/bg-8-637-2011
|
[9] |
刘新杰, 刘良云. 叶绿素荧光的 GOSAT 卫星遥感反演[J]. 遥感学报, 2013, 17(6): 1518-1532.
|
|
Liu X J, Liu L Y. Retrieval of chlorophyll fluorescence from GOSAT TANSO-FTS data based on weighted least square fitting[J]. Journal of Remote Sensing, 2013, 17(6): 1518-1532.
|
[10] |
Zhang Z, Xu W, Qin Q, et al. Downscaling solar-induced chlorophyll fluorescence based on convolutional neural network method to monitor agricultural drought[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(2): 1012-1028.
doi: 10.1109/TGRS.36
|
[11] |
Zhang Z, Xu W, Shi Z, et al. Establishment of a comprehensive drought monitoring index based on multi-source remote sensing data and agricultural drought monitoring[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 2113-2126.
doi: 10.1109/JSTARS.4609443
|
[12] |
Joiner J, Guanter L, Lindstrot R, et al. Global monitoring of terrestrial chlorophyll fluorescence frommoderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2[J]. Atmospheric Measurement Techniques, 2013, 6(10): 2803-2823.
doi: 10.5194/amt-6-2803-2013
|
[13] |
王彦良, 刘艳华, 王文杰. 基于遥感技术的河南省农业旱情监测研究[J]. 测绘与空间地理信息, 2013, 36(9): 128-130+133.
|
|
Wang Y L, Liu Y H, Wang W J. Study on Henan agricultural drought monitoring based on remote sensing technology[J]. Geomatics & Spatial Information Technology, 2013, 36(9): 128-130+133.
|
[14] |
Li X, Xiao J. A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data[J]. Remote Sensing, 2019, 11(5): 517.
doi: 10.3390/rs11050517
|
[15] |
孙洪泉, 吕娟, 苏志诚, 等. 分位数法对多指标干旱等级划分一致性的作用[J]. 灾害学, 2017, 32(2): 13-17.
|
|
Sun H Q, Lv J, Su Z C, et al. The effectiveness of the Quantile method on the consistency of the drought classification by multiple indices[J]. Journal of Catastrophology, 2017, 32(2): 13-17.
|