Hyperspectral spectroscopy based on partial least squares regression (PLSR) is an effective tool for monitoring plant photosynthesis. Despite their wide applications, the robustness of PLSR models on tracing photosynthetic capacity, which varies considerably among different species and at different times, have been far less explored, leading to doubt about whether hyperspectral information can accurately predict the capacity across different species and temporal changes. Ordinary applications of PLSR generally make use of original or integer-order derivative transformed reflected spectra, but recent advances in spectral analysis have revealed that fractional-order derivative transformed spectra could provide more details of spectral signals. In this study, PLSR models based on fractional-order derivatives coupled with different wavelength selection methods were developed to evaluate whether photosynthetic parameters (Vcmax and Jmax) could be correctly predicted from reflectance spectra. The result indicated that the best PLSR models for the Vcmax and Jmax were obtained based on the sensitive wavelengths selected by stepwise regression using the fractional orders of 1.25 and 1.60, respectively. The optimal PLSR models were able to capture the temporal variabilities of Vcmax and Jmax with the R2of 0.62-0.94 and 0.65-0.85, for which the 1605-1845 nm region was consistently used. Meanwhile, these PLSR models have the ability to capture the variations in different species, plant functional types, and biomes. The findings of this study demonstrate that leaf spectra can be successfully used for the timely prediction of variable photosynthetic capacity and provide the fundamentals for monitoring and mapping plant functions from reflected information.

Quantitatively assessing the abundances of the composite minerals in terrestrial granite is crucial to understanding the evolutionary history of the earth’s crust and to mineral exploration as well. Prevalent methods of estimating mineral abundances based on the Hapke model by setting the optical constants of the endmembers ahead of time are no longer applicable to terrestrial granite because of the complexity of natural granite, which leads to remarkable uncertainties in these estimations. In this study, we retrieved specific photometric parameters from the bidirectional reflectance spectra measured at a range of incidence, emergence, and phase angles before they were input into the Hapke model and used to estimate the mineral abundances. Four types of granite samples containing the main granitic minerals (quartz, alkali feldspar, and plagioclase) were used to test the effectiveness of our proposed method. The effects of the particle size and dark minerals on the inversion results using the visible near and shortwave infrared (VNIR-SWIR) wavelengths were also examined. The results show that using the photometric parameters retrieved from multiangle measurements as inputs to the Hapke model can produce accurate estimations of the abundances of quartz, alkali feldspar, and plagioclase in both natural and synthetic granite samples. Furthermore, the results demonstrate that the retrieved particle sizes of the particulate samples are close to the ground measurements. Thus, the proposed approach provides a more accurate and efficient estimation of the compositions of terrestrial granites, making it feasible to quickly assess the abundances of the minerals contained in granite.

Aerosol particles affect the Earth's radiative balance and represent one of the largest uncertainties in climate research. The removal of clouds is the first step and critical in the aerosol retrievals. However, the cloud detection is still challenging. Here, a novel simplified cloud detection algorithm (SCDA) is proposed to identify the cloud and clear-sky over land and based on a simplified radiative transfer model (RTM). The fewer input bands, dynamic thresholds, and only one parameter to be modified are the main advantages of the algorithm, which can be applied to different satellite sensors. In this article, we apply SCDA to the Himawari-8 data in 2016 for preliminary analysis. The detection results are validated using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM) data and the National Centers for Environmental Information (NCEI) ground-based observation data. We also compare the results with the Himawari-8 cloud products from the Japan Aerospace Exploration Agency (JAXA). Compared with CALIPSO VFM data and NCEI ground-based observation data, the correct rate of SCDA cloud detection result is 86.08% and 79.86%, which are higher than that of Himawari-8 cloud products (85.71% and 78.89%). The correct rate of SCDA clear-sky detection result is 88.33% and 87.85%, which are close to the correct rate of Himawari-8 clear-sky products (90.54% and 88.63%). The overall performance of the SCDA is comparable to that of the threshold method for JAXA Himawari-8 cloud products. Therefore, the SCDA can provide accurate cloud mask with only one threshold to be modified and few input parameters.

Quantitative determination of the physical properties of natural granite has been attempted from remotely sensed information, for which the Hapke model is a popular method. However, using the model to retrieve the photometric properties of terrestrial rocks (slab or particulate samples), especially for those with complex surface conditions such as natural granite, remains a challenge. In this study, we have approached the dilemma by coupling both radiative transfer (Hapke’s isotropic multiple scattering approximation (IMSA) model) and an empirical relationship between particle sizes with its critical parameter, the single-scattering albedo (SSA,$\omega$), determined from bidirectional reflectance (BDR) measurements. The results clearly indicated that the particle size of natural granite systematically controlled the BDR, which can be well fit by the Hapke model with varying parameters. The retrieved photometric parameters of the coefficients in the phase function ($b$and$c$) can effectively indicate the scattering behavior of natural granite, but the variations in their values did not strongly correlate with the change in particle sizes. Instead, a good linear relationship between the SSA values and particle sizes has been established. By coupling the relationship into the Hapke model, we found a practical approach to estimate the particle size for measured samples from inversely retrieved SSA. Through this method, we are able to retrieve the physical properties of granite under natural surface conditions, and we foresee that the approach will be widely used in the future.

Partial least-squares (PLS) regression is a popular method for modeling chemical constituents from spectroscopic data and has been widely applied to retrieve leaf chemical components via hyperspectral remote sensing. However, one persistent challenge for applying the PLS regression is the selection of informative spectral bands among the vast array of acquired spectra. No consensus has been reached yet on how to select informative bands regardless of many techniques being proposed. In this paper, we have composited four individual data sets containing a total of 598 leaf samples from various species to evaluate four different band elimination/selection methods. Results revealed that the stepwise-PLS approach was optimal to estimate leaf chlorophyll content even under different spectral resolutions, from which informative bands were identified. Informative bands, in general, include bands inside the near-infrared (NIR), and in addition, one within the blue range and one within the red range. With such combinations, the PLS regression models meet the requirement for accurate leaf chlorophyll estimation. For most PLS regression models, their accuracies decreased with the reduction of spectral resolution, but the stepwise-PLS approach could consistently estimate the chlorophyll content at different spectral resolutions (with R2 ≥ 0.77 for resolutions <; 20 nm). The findings, hence, provide valuable insights for selecting informative spectral bands for PLS analysis and lay a strong foundation for retrieving foliar biochemical content using hyperspectral remote sensing data.

Bell-LaPadula model is the most classical multilevel security access control model, however, the existence of the response blind area in Bell-LaPadula model is a great threat for system. In this paper we propose an improved Bell-LaPadula model combining with obligation mechanism. Response mechanism is also introduced in the improved model which can resolve the disadvantage of response blind area. Furthermore, the security of the improved model and covert channel is analyzed in detail.

Chlorophyll fluorescence is a common approach for understanding leaf photochemical and nonphotochemical processes nondestructively. Among all fluorescence parameters, the photosynthetic electron transport rate (ETR) is a useful indicator of the efficiency of carbon uptake. Traditionally, Pulse Amplitude Modulation (PAM) fluorometry was a key technique for measuring ETR, but it is not applicable for large-scale monitoring. Alternatively, hyperspectral remote sensing has a great potential for this purpose and as thus several indices generated from hyperspectral reflectance have been developed for tracing ETR. In this study, we have extensively examined the performance of twelve early published indices for tracing leaf ETR of a typical deciduous species (Fagus crenata) in a cold-temperate mountainous area. Furthermore, developing of new hyperspectral indices has also been conducted based on both original and first derivative reflected spectra. Results revealed that our proposed index (dmND680, 540, 835) was applicable for both sunlit and shaded leaves and had a high robustness, suggesting its potential for a general application.

Monitoring soil salinization has been a difficult process in arid lands due their large spatial and temporal variability. Hyperspectral remote sensing has offered a potential for faster detection of salinization process but mostly from empirical approaches. In this paper, an integrated approach combining model inversion and empirical regressions has been proposed for soil salt content (SSC) estimation from hyperspectral information obtained from controlled laboratory experiments. All soil samples were artificially salinized using Na2SO4, NaCl, and Na2CO3(99% purity) salts to different levels and to different soil-moisture conditions, since soil moisture often jointly affects reflectance spectra with SSC. Hapke model was calibrated and validated for its simulation on soil reflectance and showed good agreements with measured data. The optimal values of single scattering albedo that was inversely retrieved from the Hapke model had good relationships with SSC at 2000-2200 nm for each treatment even under various soil-moisture conditions. Taking usage of these findings, the integrated approach obtained high accuracies on SSC estimations withR2's of 0.90, 0.86, and 0.72 and slightly droppedR2's of 0.89, 0.81, and 0.67 for NaCl-, Na2SO4-, and Na2CO3-type saline soils under respective dry and wet conditions. TheR2decreased to 0.55 and 0.53 for dry and wet soils when salt types were ignored. The integrated approach provides a novel as well as an efficient way for SSC estimation from reflected spectra, and hence, we foresee its potential applications for large-scale SSC mapping from reflectance measurements.

Retrieval of leaf biochemical parameters from reflectance measurements using model inversion generally faces “ill-posed” problems, which dramatically decreases the estimation accuracy of an inverse model. While the standard approach for model inversion retrieves various parameters simultaneously, usually only based on one merit function, the new approach proposed in this paper assigns a specific merit function for each retrieved parameter. Each merit function is specified in terms of the wavelength domains that the given parameter was found to be specifically sensitive to in an earlier sensitivity analysis. The approach has been validated with both in situ measured data sets and an artificial data set of 10 000 spectra simulated by the PROSPECT model. Results indicate that the new approach greatly improves the performance of inversion models, with root-mean-square error (rmse) values for chlorophyll content (Chl), equivalent water thickness (EWT), and leaf mass per area (LMA), based on the simulated data, of 7.12 μg/cm2, 0.0012 g/cm2, and 0.0019 g/cm2, respectively, compared with 11.36 μg/cm2, 0.0032 g/cm2, and 0.0040 g/cm2when using the standard approach. As for field-measured data sets, the proposed approach also greatly outperformed the standard approach, with respective rmse values of 8.11 μg/cm2, 0.0012 g/cm2, and 0.0008 g/cm2for Chl, EWT, and LMA when all data are pooled, compared with 11.84 μg/cm2, 0.0020 g/cm2, and 0.0027 g/cm2when using the standard approach. Hence, the proposed approach for model inversion can largely alleviate the “ill-posed” problem, and it could be widely applied for retrieving leaf biochemical parameters.