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Distinguishing Heavy-Metal Stress Levels in Rice Using Synthetic Spectral Index Responses to Physiological Function Variations., , , and . IEEE J Sel. Topics in Appl. Earth Observ. and Remote Sensing, 10 (1): 75-86 (2017)A New Vegetation Index Based on Multitemporal Sentinel-2 Images for Discriminating Heavy Metal Stress Levels in Rice., , , and . Sensors, 18 (7): 2172 (2018)Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology., , , and . Sensors, 18 (3): 860 (2018)Regional heavy metal pollution in crops by integrating physiological function variability with spatio-temporal stability using multi-temporal thermal remote sensing., , , and . Int. J. Appl. Earth Obs. Geoinformation, (2016)Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model., , , , and . Comput. Geosci., 37 (10): 1642-1652 (2011)Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm., , , , and . Comput. Geosci., (2015)Energy Efficient User Association, Resource Allocation and Caching Deployment in Fog Radio Access Networks., , , , and . IEEE Trans. Veh. Technol., 71 (2): 1846-1856 (2022)Primal-Dual Learning for Cross-Layer Resource Management in Cell-Free Massive MIMO IIoT., , , , , and . IEEE Internet Things J., 9 (18): 17026-17034 (2022)Decomposition of long time-series fraction of absorbed photosynthetically active radiation signal for distinguishing heavy metal stress in rice., , , , and . Comput. Electron. Agric., (July 2022)Online Forest Disturbance Detection at the Sub-Annual Scale Using Spatial Context From Sparse Landsat Time Series., , , , , and . IEEE Trans. Geosci. Remote. Sens., (2022)