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Algorithm and Feature Selection for VegOut: A Vegetation Condition Prediction Tool., , и . Discovery Science, том 5808 из Lecture Notes in Computer Science, стр. 107-120. Springer, (2009)Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset., , , , , , , и . Remote. Sens., 13 (24): 5074 (2021)Information Mining from Heterogeneous Data Sources: A Case Study on Drought Predictions., , , , , , и . Inf., 8 (3): 79 (2017)Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India., , , , , и . Remote. Sens., 12 (13): 2091 (2020)Imaging Spectrometry and Fluorometry in Support of Flex: What Can We Learn from Multi-Scale Experiments?, , , , , , , , , и 5 other автор(ы). IGARSS, стр. 3931-3934. IEEE, (2018)PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans., , , , , , , , и . Int. J. Appl. Earth Obs. Geoinformation, (2020)The Vegetation Outlook (VegOut): A New Tool for Providing Outlooks of General Vegetation Conditions Using Data Mining Techniques., и . ICDM Workshops, стр. 667-672. IEEE Computer Society, (2007)Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States., , , , , , , , , и 5 other автор(ы). Remote. Sens., 12 (21): 3605 (2020)A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products., , , , , , , , , и . Remote. Sens., 13 (7): 1397 (2021)Preliminary assessment of an integrated SMOS and MODIS application for global agricultural drought monitoring., , , , , , , и . IGARSS, стр. 2000-2003. IEEE, (2017)