Owning to the nature of flood events, near-real-time flood detection and mapping is essential for disaster prevention, relief, and mitigation. In recent years, the rapid advancement of deep learning has brought endless possibilities to the field of flood detection. However, deep learning relies heavily on training samples and the availability of high-quality flood datasets is rather limited. The present study collected 16 flood events in the Yangtze River Basin and divided them into three categories for different purpose: training, testing, and application. An efficient methodology of dataset-generation for training, testing, and application was proposed. Eight flood events were used to generate strong label datasets with 5296 tiles as flood training samples along with two testing datasets. The performances of several classic convolutional neural network models were evaluated with those obtained datasets, and the results suggested that the efficiencies and accuracies of convolutional neural network models were obviously higher than that of the threshold method. The effects of VH polarization, VV polarization, and the involvement of auxiliary DEM on flood detection were investigated, which indicated that VH polarization was more conducive to flood detection, while the involvement of DEM has a limited effect on flood detection in the Yangtze River Basin. Convolutional neural network trained by strong datasets were used in near-real-time flood detection and mapping for the remaining eight flood events, and weak label datasets were generated to expand the flood training samples to evaluate the possible effects on deep learning models in terms of flood detection and mapping. The experiments obtained conclusions consistent with those previously made on experiments with strong datasets.
Plant water stress can be detected via remote sensing. The objective of the study was to determine which leaf water index is best for assessing leaf water content from the laboratory standpoint. This study investigated the relationship between equivalent water thicknesses (EWT), gravimetric water content (GWC), and plant water concentration in the 350-to 2500-nm reflectance spectral range. A total of 277 leaf samples taken from ten different plants were used as calibration dataset, and 605 leaves from different plants, including LOPEX93 and ANGERS database, were used for validation. Three specific indices were analyzed: simple ratio, normalized ratio, and double difference (Datt type of index). A regression approach based on the iteration method at 5-nm interval was used for model calibration. Three bands index was found the most suitable and was validated by 605 leaf samples: for the linear regression model, the index is ðR 1910 − R 1340 Þ∕ðR 1910 − R 1125 Þ with R 2 ¼ 0.96 and root mean square error ðRMSEÞ ¼ 0.001 ðg∕cm 2 Þ and, for nonlinear regression model the index is ðR 1930 − R 1425 Þ∕ðR 1930 − R 1360 Þ with R 2 ¼ 0.95 and RMSE ¼ 0.001 ðg∕cm 2 Þ for EWT. The newly proposed indices take advantage of being able to eliminate additional noise created by the leaf surface, making them helpful for agricultural-related research.
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