A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests.

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one-third of terrestrial gross primary productivity (GPP). However, the continental-scale leaf phenology-photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC-LUE) and proposed a novel leaf age-dependent LUE model (denoted as LA-LUE model). At the site level, the LA-LUE model (average R2 = .59, average root-mean-square error [RMSE] = 1.23 gC m-2 day-1) performs better than the EC-LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 = .18; average RMSE = 1.87 gC m-2 day-1). At the continental scale, the monthly GPP estimates from the LA-LUE model are consistent with FLUXCOM GPP data (R2 = .80; average RMSE = 1.74 gC m-2 day-1), and satellite-based GPP data retrieved from the global Orbiting Carbon Observatory-2 (OCO-2) based solar-induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 = .64; average RMSE = 1.90 gC m-2 day-1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 = .78; average RMSE = 1.88 gC m-2 day-1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite-based LUE model and provides a feasible implementation for mapping the continental-scale GPP seasonality over the entire TEFs.

Monitoring urban black-odorous water by using hyperspectral data and machine learning.

Economic development, population growth, industrialization, and urbanization dramatically increase urban water quality deterioration, and thereby endanger human life and health. However, there are not many efficient methods and techniques to monitor urban black and odorous water (BOW) pollution. Our research aims at identifying primary indicators of urban BOW through their spectral characteristics and differentiation. This research combined ground in-situ water quality data with ground hyperspectral data collected from main urban BOWs in Guangzhou, China, and integrated factorial data mining and machine learning techniques to investigate how to monitor urban BOW. Eight key water quality parameters at 52 sample sites were used to retrieve three latent dimensions of urban BOW quality by factorial data mining. The synchronically measured hyperspectral bands along with the band combinations were examined by the machine learning technique, Lasso regression, to identify the most correlated bands and band combinations, over which three multiple regression models were fitted against three latent water quality indicators to determine which spectral bands were highly sensitive to three dimensions of urban BOW pollution. The findings revealed that the many sensitive bands were concentrated in higher hyperspectral band ranges, which supported the unique contribution of hyperspectral data for monitoring water quality. In addition, this integrated data mining and machine learning approach overcame the limitations of conventional band selection, which focus on a limited number of band ratios, band differences, and reflectance bands in the lower range of infrared region. The outcome also indicated that the integration of dimensionality reduction with feature selection shows good potential for monitoring urban BOW. This new analysis framework can be used in urban BOW monitoring and provides scientific data for policymakers to monitor it.

Global pattern for the effect of climate and land cover on water yield.

Research results on the effects of land cover change on water resources vary greatly and the topic remains controversial. Here we use published data worldwide to examine the validity of Fuh's equation, which relates annual water yield (R) to a wetness index (precipitation/potential evapotranspiration; P/PET) and watershed characteristics (m). We identify two critical values at P/PET=1 and m=2. m plays a more important role than P/PET when m<2, and a lesser role when m>2. When P/PET<1, the relative water yield (R/P) is more responsive to changes in m than it is when P/PET>1, suggesting that any land cover changes in non-humid regions (P/PET<1) or in watersheds of low water retention capacity (m<2) can lead to greater hydrological responses. m significantly correlates with forest coverage, watershed slope and watershed area. This global pattern has far-reaching significance in studying and managing hydrological responses to land cover and climate changes.

[Method of Remote Sensing Identification for Mineral Types Based on Multiple Spectral Characteristic Parameters Matching].

The traditional mineral mapping methods with remote sensing data, based on spectral reflectance matching techniques, shows low accuracy, for obviously being affected by the image quality, atmospheric and other factors. A new mineral mapping method based on multiple types of spectral characteristic parameters is presented in this paper. Various spectral characteristic parameters are used together to enhanced the stability in the situation of atmosphere and environment background affecting. AVIRIS (Airborne Visible Infrared Imaging Spectrometer) data of Nevada Cuprite are selected to determine the mineral types with this method. Typical mineral spectral data are also obtained from USGS (United States Geological Survey) spectral library to calculate the spectral characteristic parameters. A mineral identification model based on multiple spectral characteristic parameters is built by analyzing the various characteristic parameters, and is applied in the mineral mapping experiment in Cuprite area. The mineral mapping result produced by Clark et al. in 1995 is used to evaluate the effect of this method, results show, that mineral mapping results with this method can obtain a high precision, the overall mineral identification accuracy is 78.96%.

[Estimation of water clarity in offshore marine areas based on modified semi-analysis spectra model].

The main objectives of the research described in the present paper are to develop a semi-analysis model of water clarity for case 2 waters without inputting the absorption and scattering coefficient, which are not easy to be obtained for offshore marine areas so far. Based on the Zsd (Secchi depth)inversion theory, a simple semi-analysis spectra model was established for offshore seawater clarity by analyzing the relationship between vertical diffuse attenuation coefficient K(d) (490) and the beam attenuation coefficient c(490) with remote sensing reflectance. This semi-analysis spectra model needed two band reflectance ratios on- ly, while tidal correction was produced for this model to improve the precision of the retrieving results. The semi-analysis spectra model was applied to ASD hyperspectral reflectance data measured in the Pearl River Estuary Ecological Zone (October 21, 23, 2012, November 2, 2012; N=20) and the Xuwen Coral Reef Protection Zone (January 13, 14, 2013, N=25) which covered different water body of tidal times and different pollution sources. The results indicated that the changing tendency of predicted values was consistent with the synchronous measurement values after comparing them. However, water clarity calculated by the ASD hyperspectral reflectance measured in spring tidal time, generated 0. 4 m deviation compared with in-situ water clarity, while water clarity calculated by the ASD hyperspectral reflectance measured in neap tidal time is close to the in-situ water clarity. So the tidal correction coefficient of 0.4 was further applied for the model. After modification, the coefficient of determination between the inversed and measured water clarity was 0. 663, the average absolute error was 0. 14 m and the average relative error was 19.5%. Research demonstrated that this semi-analysis inversion algorithm just needs two band reflectance ratio to complete the inversion of water clarity, which is simple and works relatively well for lower clarity (less than 2 meters) waters compared to He' (2004) and Doron' (2011) algorithms.