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Numerous drought indices have been developed to monitor drought conditions. However, different drought indices have differing characteristics, and are suitable for specific environments. The aim of this study was to compare the occurrence of drought, as detected by remote sensing across the Continental United States (CONUS). We used drought events during 2011 and 2012 to compare various indices developed for the study of drought phenomena. Three in situ drought indices, the Palmer drought severity index (PDSI), Z-index and standardized precipitation indices (SPI) with different time scales were used to evaluate drought conditions in different climate divisions. The drought indices compared in this study include the vegetation condition index (VCI), the temperature condition index (TCI), the perpendicular drought index (PDI) and modified PDI (MPDI) derived from moderate-resolution imaging spectroradiometer (MODIS) data, the precipitation condition index (PCI) derived from tropical rainfall measuring mission (TRMM) data, and the soil moisture condition index (SMCI) derived from advanced microwave scanning radiometer - earth observing system (AMSR-E). Other synthesized drought indices, which combine indices such as VCI, TCI, SMCI and PCI, were also compared in this study. These included the vegetation health index (VHI), temperature vegetation dryness index (TVDI), scaled drought condition index (SDCI), the microwave integrated drought index (MIDI), the synthesized drought index (SDI), the optimized meteorological drought index (OMDI), and the optimized vegetation drought index (OVDI). The results include a wide variety of drought conditions based on different drought indices. Meteorological drought indices indicated that more regions were under severe drought than agricultural drought indices did. Drought indices appear to be more similar to standard drought indices than PDI, MPDI and TVDI. The results also indicate that different indices have strengths and weaknesses in different climates across CONUS. SMCI has a good correlation with short-term SPI, and the sensitivity of SMCI is strongly dependent on terrain, as it performs worse in regions with heavy tree cover than in regions with a low density of vegetation. TCI, VCI, PDI and MPDI are more similar to 3-month SPI data, but correlate weakly with station-based indices located in areas of high precipitation, higher soil permeability, large-scale agriculture and forests. PCI is more strongly correlated with short-term drought conditions in almost all climate divisions than other single indices. VCI would be more reliable if there were only red and NIR surface reflectance bands available, while VHI would be a better choice if only NDVI and LST data were available. Condition index-based drought indices (PCI, TCI, VCI, SMCI, VHI, SDCI, SDCI, MIDI, OVDI, OMDI) performed better than other categories of drought indices, and the use of time series analysis may be a contributing factor to this difference in performance.

Oblique imaging and unmanned aerial vehicles (UAV) are two state-of-the-art remote sensing (RS) techniques that are undergoing explosive development. While their synthesis means more possibilities for applications such as urban forestry and urban greening, the related methods for data processing and information extraction, e.g. individual tree detection, are still in short supply. In order to help fill this technical gap, this study focused on developing a new method applicable for detection of individual trees in UAV oblique images. The planned algorithm is composed of three steps: 1) classification based on K-means clustering and RGB-based vegetation index derivation to acquire vegetation cover maps, 2) suggestion of new feature parameters by synthesizing texture and color parameters to identify vegetation distribution, 3) individual tree detection based on marker-controlled watershed segmentation and shape analysis. The evaluations based on the images within residential environments indicated that the commission and omission errors are less than 32% and 26%, respectively. The results have basically validated the proposed method.

Soil albedo is a critical parameter affecting the Earth\'s climate and environment. In remote sensing data, analysis of the soil bidirectional reflectance distribution function (BRDF) has to be known. Several models for bidirectional reflectance over soil have been developed. The Hapke bidirectional reflectance model has been widely used for soil modeling. Polarization of radiation reflected by soil carries important information of soil properties. The polarized light always goes with the bidirectional reflectance. Therefore, polarization reflectance of a ground target carries equivalent important information as bidirectional reflectance. Detecting multiangle polarization information of soil becomes a new method in quantitative remote sensing., In this paper, we analyzed the existence of polarization on the soil surface in a 2 pi space and compared the bidirectional reflectance with the bidirectional polarized reflectance. We then developed a new polarized BRDF model of soil as the bidirectional polarization distribution function (BPDF) model. The BPDF model helps to improve classification and quantitative analysis of soil.

Abstract—Sunlit vegetation and shaded vegetation are inseparable parts for most remotely sensed images, and the presence of shadows affects high spatial resolution remote sensing and multiangle remote sensing data. Shadows can lead to either a reduction in or a total loss of information in an image. This can potentially lead to the corruption of biophysical parameters derived from pixel values, such as vegetation indexes (VIs). VIs are widely used in remote sensing inversion applications. If the effects of shadows are not properly accounted for, retrieval may be uncertain when using a VI to calculate vegetation parameters. One of the major reasons that the effects of shadows are easy to be ignored in remote sensing is the spatial resolution of the measurement. High spatial and spectral resolutions are typically difficult to achieve simultaneously, and images that have one tend to not have the other. A ground-based imaging spectrometer brings a turning point to solve this problem as it can obtain both high spatial and high spectral resolutions to obtain feature and shadow images simultaneously. The resolution of the system used here was 1 mm at a height of 1 m, and the spectral resolution was better than 5 nm. For each pixel, the spectral curve of the image was almost a pure-pixel spectral curve, which allowed the differentiation of sunlit and shaded areas. To investigate the effects of shadows on different indexes, 14 hyperspectral VIs were calculated.Moreover, the vegetation fractional coverage calculated using the same 14 VIs was compared. The results show that shadows affect not only each narrowband of a VI but also vegetation parameters.

Phyllosilicate belongs to hydrated silica, which is a principal form of hydrous minerals on the martian surface. It’s also an indicator in comparing different sediments and degree of aqueous alteration. Therefore, it’s essential to establish its recognition model for studying the geologic evolution of the Mars. Short-wave infrared (SWIR) spectral bands and thermal infrared (TIR) spectral bands have distinct spectral response to the mineral groups and ions, so they have distinctive advantages in detecting minerals. However the method of combining SWIR and TIR to recognize phyllosilicate is rarely studied. Based on the USGS spectral library, facing Compact Reconnaissance Imaging Spectrometer for Mars(CRISM) and Thermal Emission Imaging System(THEMIS),we conducted the research on the mechanism of the spectral response of phyllosilicate, and established the SWIR and TIR identification model respectively, then combined the SWIR and TIR spectral features to build the combined recognition model of phyllosilicate with Fisher discriminant analysis. The results of cross validation show that the identification accuracy of combined model is the highest, which can correctly classify 90.6% of the mineral samples and improve the identification precision of phyllosilicate effectively.

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.

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Remotely sensed solar-induced chlorophyll fluorescence (SIF) has been considered an ideal probe in monitoring global vegetation photosynthesis. However, challenges in accurate estimate of faint SIF (less than 5% of the total reflected radiation in near infrared bands) from the observed apparent reflected radiation greatly limit its wide applications. Currently, the telluric O2-B (~688nm) and O2-A (~761nm) have been proved to be capable of SIF retrieval based on Fraunhofer line depth (FLD) principle. They may still work well even using conventional ground-based commercial spectrometers with typical spectral resolutions of 2~5 nm and high enough signal-to-noise ratio (e.g., the ASD spectrometer). Nevertheless, almost all current FLD based algorithms were mainly developed for O2-A, a few concentrating on the other SIF emission peak in O2-B. One of the critical reasons is that it is very difficult to model the sudden varying reflectance around O2-B band located in the red-edge spectral region (about 680-800 nm). Diurnal

This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades. China has made great achievements since starting in this promising field in the early 1980s. A series of advanced hyperspectral imaging systems ranging from ground to airborne and satellite platforms have been designed, built, and operated. These include the field imaging spectrometer system (FISS), the Modular Airborne Imaging Spectrometer (MAIS), and the Chang'E-I Interferometer Spectrometer (IIM). In addition to developing sensors, Chinese scientists have proposed various novel image processing techniques. Applications of hyperspectral imaging in China have been also performed including mineral exploration in the Qilian Mountains and oil exploration in Xinjiang province. To promote the development of HRS, many generic and professional software tools have been developed. These tools such as the Hyperspectral Image Processing and Analysis System (HIPAS) incorporate a number of special algorithms and features designed to take advantage of the wealth of information contained in HRS data, allowing them to meet the demands of both common users and researchers in the scientific community.