We examined the relationship between satellite measurements of solar-induced chlorophyll fluorescence (SIF) and several meteorological drought indices, including the multi-time-scale standard precipitation index (SPI) and the Palmer drought severity index (PDSI), to evaluate the potential of using SIF to monitor and assess drought. We found significant positive relationships between SIF and drought indices during the growing season (from June to September). SIF was found to be more sensitive to short-term SPIs (one or two months) and less sensitive to long-term SPI (three months) than were the normalized difference vegetation index (NDVI) or the normalized difference water index (NDWI). Significant correlations were found between SIF and PDSI during the growing season for the Great Plains. We found good consistency between SIF and flux-estimated gross primary production (GPP) for the years studied, and synchronous declines of SIF and GPP in an extreme drought year (2012). We used SIF to monitor and assess the drought that occurred in the Great Plains during the summer of 2012, and found that although a meteorological drought was experienced throughout the Great Plains from June to September, the western area experienced more agricultural drought than the eastern area. Meanwhile, SIF declined more significantly than NDVI during the peak growing season. Yet for senescence, during which time the reduction of NDVI still went on, the reduction of SIF was eased. Our work provides an alternative to traditional reflectance-based vegetation or drought indices for monitoring and assessing agricultural drought.

Landsat 8, the most recently launched satellite of the series, promises to maintain the continuity of Landsat 7. However, in addition to subtle differences in sensor characteristics and vegetation index (VI) generation algorithms, VIs respond differently to the seasonality of the various types of vegetation cover. The purpose of this study was to elucidate the effects of these variations on VIs between Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+). Ground spectral data for vegetation were used to simulate the Landsat at-senor broadband reflectance, with consideration of sensor band-pass differences. Three band-geometric VIs (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI)) and two band-transformation VIs (Vegetation Index based on the Universal Pattern Decomposition method (VIUPD), Tasseled Cap Transformation Greenness (TCG)) were tested to evaluate the performance of various VI generation algorithms in relation to multi-sensor continuity. Six vegetation types were included to evaluate the continuity in different vegetation types. Four pairs of data during four seasons were selected to evaluate continuity with respect to seasonal variation. The simulated data showed that OLI largely inherits the band-pass characteristics of ETM+. Overall, the continuity of band-transformation derived VIs was higher than band-geometry derived VIs. VI continuity was higher in the three forest types and the shrubs in the relatively rapid growth periods of summer and autumn, but lower for the other two non-forest types (grassland and crops) during the same periods.

The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m) to EO-1 Hyperion (with a spatial resolution of 30 m). The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC), pixel-based support vector machine (PSVM), hybrid maximum likelihood classifier (HMLC), and hybrid support vector machine (HSVM) were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the hybrid classifier.

Soil contamination by arsenic and heavy metals is an increasingly severe environmental problem. Efficiently investigation of soil contamination is the premise of soil protection and further the foundation of food security. Visible and near-infrared reflectance spectroscopy (VNIRS) has been widely used in soil science, due to its rapidity and convenience. With different spectrally active soil characteristics, soil reflectance spectra exhibit distinctive curve forms, which may limit the application of VNIRS in estimating contaminant elements in soil. Consequently, spectral clustering was applied to explore the potential of classification in estimating soil contaminant elements. Spectral clustering based on different distance measure methods and elements with different contamination levels were exploited. In this study, soil samples were collected from Hunan Province, China and 74 reflectance spectra of air-dried soil samples over 350–2500 nm were used to predict nickel (Ni) and zinc (Zn) concentrations. Spectral clustering was achieved by K-means clustering based on squared Euclidean distance and Cosine of spectral angle, respectively. The prediction model was calibrated with the combination of Genetic algorithm and partial least squares regression (GA-PLSR). The prediction accuracy shows that the prediction of Ni and Zn concentrations in soil was improved to different extents by the two clustering methods and the clustering based on squared Euclidean distance had better performance over the clustering relied on Cosine of the spectral angle. The result reveals the potential of spectral classification in predicting soil Ni and Zn concentrations. A selected subset of the 74 soil spectra was used to further explore the potential of spectral classification in estimating Zn concentrations. The prediction was dramatically improved by clustering based on squared Euclidean distance. Additionally, analysis on distance measure methods indicates that Euclidean distance is more suitable to describe the difference between the collected soil reflectance spectra, which brought the better performance of the clustering based on squared Euclidean distance.

Capturing spatial and temporal dynamics is a key issue for many remote-sensing based applications. Consequently, several image-blending algorithms that can simulate the surface reflectance with high spatial-temporal resolution have been developed recently. However, the performance of the algorithm against the effect of temporal interval length between the base and simulation dates has not been reported. In this study, our aim was to evaluate the effect of different temporal interval lengths on the accuracy using the widely used blending algorithm, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), based on Landsat, Moderate-resolution Imaging Spectroradiometer (MODIS) images and National Land Cover Database (NLCD). Taking the southwestern continental United States as the study area, a series of experiments was conducted using two schemes, which were the assessment of STARFM with (i) a fixed base date and varied simulation date and (ii) varied base date and specific simulation date, respectively. The result showed that the coefficient of determination (R²), Root Mean Squared Error (RMSE) varied, and overall trend of R² decreased along with the increasing temporal interval between the base and simulation dates for six land cover types. The mean R² value of cropland was lowest, whereas shrub had the highest value for two schemes. The result may facilitate selection of an appropriate temporal interval when using STARFM.

With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis results) participate. Reflectance spectra of subjects’ tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity. Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation. To measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with 100 times Hold-out method and 67 times Leave-one-out method are 88.05% and 97.01%, respectively. Experimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.