Comparative Study on Recognition Models of Black-Odorous Water in Hangzhou Based on GF-2 Satellite Data.

To improve the ability of remote sensing technology in recognizing black-odorous water bodies in Hangzhou, this study analyzed the typical spectral characteristics of black-odorous water in Hangzhou based on measured spectral data and water quality parameters, including the transparency, dissolved oxygen, oxidation reduction potential, and ammonia nitrogen. The single-band threshold method, the normalized difference black-odorous water index (NDBWI) model, the black-odorous water index (BOI) model, and the color purity on a Commission Internationale de L'Eclairage (CIE) model were compared to analyze the spatial and temporal distribution characteristics of the black-odorous water in Hangzhou. The results showed that: (1) The remote sensing reflectance of black-odorous water was lower than that of ordinary water, the spectral curve was gentle, and the wave peak shifted toward the near-infrared direction in the wavelength range of 650-850 nm; (2) Among the aforementioned models, the normalized and improved normalized black-odorous water index methods had a higher accuracy, reaching 87.5%, and the threshold values for black-odorous water identification were 0.14 and 0.1, respectively; (3) From 2015 to 2018, the quantity of black-odorous water in the main urban area of Hangzhou showed a decreasing trend, and black-odorous water was mainly distributed in the Gongshu District and tended to appear in narrow rivers, densely populated areas, and factory construction sites. This study is expected to be of great practical value for the rapid tracking and monitoring of urban black-odorous water by using remote sensing technology for future work.

[A inversion model for remote sensing of leaf water content based on the leaf optical property].

Leaf water content is a fundamental physiological characteristic parameter of crops, and plays an important role in the study of the ecological environment. The aim of the work reported in this paper was to focus upon the retrieval of leaf water content from leaf-scale reflectance spectra by developing a physical inversion model based on the radiative transfer theory and wavelet analysis techniques. A continuous wavelet transform was performed on each of leaf component specific absorption coefficients to pick wavelet coefficients that were identified as highly sensitive to leaf water content and insensitive to other components. In the present study, for identifying the most appropriate wavelet, the six frequently used wavelet functions available within MATLAB were tested. Two biorl. 5 wavelet coefficients observed at the scale of 200 nm are provided with good performance, their wave-length positions are located at 1 405 and 1 488 nm, respectively. Two factors (α and Δ) of the predictive theoretical models based on the biorl. 5 wavelet coefficients of the leaf-scale reflectance spectra were determined by leaf structure parameter N. We built a database composed of thousands of simulated leaf reflectance spectra with the PROSPECT model. The entire dataset was split into two parts, with 60% the calibration subset assigned to calibrating two factors (α and Δ) of the predictive theoretical model. The remaining 40% the validation subset combined with the LOPEX93 experimental dataset used for validating the models. The results showed that the accuracy of the models compare to the statistical regression models derived from the traditional vegetation indices has improved with the highest predictive coefficient of determination (R2) of 0. 987, and the model becomes more robust. This study presented that wavelet analysis has the potential to capture much more of the information contained with reflectance spectra than previous analytical approaches which have tended to focus on using a small number of optimal wavebands while discarding the majority of the spectrum.