Yi Cen

Monitoring and Assessing the 2012 Drought in the Great Plains: Analyzing Satellite-Retrieved Solar-Induced Chlorophyll Fluorescence, Drought Indices, and Gross Primary Production

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.

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

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.

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.

Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image

Hyperspectral (HS) remote sensing has an important role in a wide variety of fields. However, its rapid progress has been constrained due to the narrow swath of HS images. This paper proposes a spectral resolution enhancement method (SREM) for remotely sensed multispectral (MS) image, to generate wide swath HS images using auxiliary multi/hyper-spectral data. Firstly, a set number of spectra of different materials are extracted from both the MS and HS data. Secondly, the approach makes use of the linear relationships between multi and hyper-spectra of specific materials to generate a set of transformation matrices. Then, a spectral angle weighted minimum distance (SAWMD) matching method is used to select a suitable matrix to create HS vectors from the original MS image, pixel by pixel. The final result image data has the same spectral resolution as the original HS data that used and the spatial resolution and swath were also the same as for the original MS data. The derived transformation matrices can also be used to generate multitemporal HS data from MS data for different periods. The approach was tested with three image datasets, and the spectra-enhanced and real HS data were compared by visual interpretation, statistical analysis, and classification to evaluate the performance. The experimental results demonstrated that SREM produces good image data, which will not only greatly improve the range of applications for HS data but also encourage more utilization of MS data.

A Modified Locality-Preserving Projection Approach for Hyperspectral Image Classification

Locality-preserving projection (LPP) is a typical manifold-based dimensionality reduction (DR) method, which has been successfully applied to some pattern recognition tasks. However, LPP depends on an underlying adjacency graph, which has several problems when it is applied to hyperspectral image (HSI) processing. The adjacency graph is artificially created in advance, which may not be suitable for the following DR and classification. It is also difficult to determine an appropriate neighborhood size in graph construction. Additionally, only the information of local neighboring data points is considered in LPP, which is limited for improving classification accuracy. To address these problems, a modified version of the original LPP called MLPP is proposed for hyperspectral remote-sensing image classification. The idea is to select a different number of nearest neighbors for each data point adaptively and to focus on maximizing the distance between nonnearest neighboring points. This not only preserves the intrinsic geometric structure of the data but also increases the separability among ground objects with different spectral characteristics. Moreover, MLPP does not depend on any parameters or prior knowledge. Experiments on two real HSIs from different sensors demonstrate that MLPP is remarkably superior to other conventional DR methods in enhancing classification performance.

Retrieval of Sun-Induced Chlorophyll Fluorescence Using Statistical Method Without Synchronous Irradiance Data

Remote sensing of top-of-canopy (TOC) long-term sun-induced chlorophyll fluorescence (SIF) is necessary to better understand the SIF-photosynthesis relationship. Statistical methods provide an alternative to TOC SIF retrieval, as they are independent of synchronous irradiance measurements and may better describe actual irradiance. This letter aims to evaluate the feasibility of using statistical methods for time series TOC SIF retrieval in the absence of synchronous irradiance measurements. Results show that the training set should include nonfluorescent radiance spectra under a variety of solar zenith angles, and that water vapor is an important contributor of spectral variation within 717–745 nm. On the diurnal scale, atmospheric features trained from irradiance spectra can be used to retrieve SIF values from high-frequency upwelling radiance spectra. Features independently trained from nonfluorescent radiance spectra measured on one day can be used for SIF retrieval on a different day within a relatively short period. Our results show that statistical methods have the potential to simplify ground-based SIF measurements and data processing.

Polarized Spectral Measurement and Analysis of Sedum Spectabile Boreau Using a Field Imaging Spectrometer System

Polarized hyperspectral imaging is a new remote sensing method that combines the benefits of polarization and hyperspectral characteristics. Based on a new self-developed polarized field imaging spectrometer system (FISS-P), we collected polarized hyperspectral images of leaves of Sedum spectabile Boreau. Polarization analysis of the diffuse reflectance standard white plate indicated that the FISS-P produced accurate polarization measurements. Ten related polarization parameters (<i>I</i>, <i>Q</i>, <i>U</i>, DoLP, AoP, <i>R</i>₀, <i>R</i>₆₀, <i>R</i>₁₂₀, <i>R</i>, <i>R</i>_I) were analyzed in this study. The angle of polarization (AoP) spectral curves of the S. spectabile leaf had no unique spectral features. The degree of linear polarization (DoLP) spectral curves displayed distinct spectral characteristics. However, the DoLP and spectral reflectance curves of the leaf displayed contrasting trends. Different parts of the same leaf, or different S. spectabile leaves, produced different spectral curve shapes. Analysis of the five reflectance parameters demonstrated that <i>R</i>₀, <i>R</i>₆₀, <i>R</i>₁₂₀, <i>R</i>_I, and <i>R</i> were consistent for all spectral and spatial aspects.

Estimating Population Density Using DMSP-OLS Night-Time Imagery and Land Cover Data

Population density is an essential indicator of human society. Night-time light (NTL) data provided by the Defense Meteorological Satellite Programs Operational Linescan System (DMSP-OLS) has been widely used in estimating population distribution, due to its capability of indicating human activity. The overglow effect of the DMSP-OLS NTL image caused by reflection of light from adjacent areas and the different population distribution patterns between urban and rural areas have limited its application in estimating population density. Therefore, a method was proposed to reduce the overglow effect and to model urban and rural population densities separately. Moderate resolution imaging spectroradiometer (MODIS) land cover product was applied to reduce the overglow effect and to separate urban and rural areas. In urban area, the extracted urban DMSP-OLS NTL image was used to model population density. In rural area, a slope adjusted human settlement index (SAHSI), based on digital elevation model, MODIS enhanced vegetation index (EVI), and the DMSP-OLS NTL data, was proposed to estimate rural population density. Guangdong Province of China was taken as the study area for it has diverse population densities. The estimation in urban area was compared with population densities derived from normalized difference vegetation index adjusted NTL urban index (VANUI) and EVI adjusted NTL urban index (VANUI-EVI). Population density in the rural area was compared with results from EVI adjusted human settlement index (HSI-EVI) and the NTL data. The mean relative error of the proposed method was 55.14% in urban areas, which was better than VANUI (60.10%) and VANUI-EVI (60.16%), and was 71% in rural areas, which was 6% lower than HSI-EVI and 3% lower than NTL data. The result indicates that the proposed method has the ability to reduce the overglow effect of DMSP-OLS NTL image and to correct the impact of terrain on rural population density estimation.

Light weight airborne imaging spectrometer remote sensing system for mineral exploration in China

Imaging spectrometers provide the unique combination of both spatially contiguous spectra and spectrally contiguous images of the Earths surface that allows spatial mapping of these minerals. One of the successful applications of imaging spectrometers remote sensing identified was geological mapping and mineral exploration. A Light weight Airborne Imaging Spectrometer System (LAISS) has been developed in China. The hardware of the compact LAISS include a VNIR imaging spectrometer, a SWIR imaging spectrometer, a high resolution camera and a position and attitude device. The weight of the system is less than 20kg. The VNIR imaging spectrometer measures incoming radiation in 344 contiguous spectral channels in the 400–1000 nm wavelength range with spectral resolution of better than 5 nm and creates images of 464 pixels for a line of targets with a nominal instantaneous field of view (IFOV) of ~1 mrad. The SWIR imaging spectrometer measures incoming radiation in the 1000–2500 nm wavelength range with spectral resolution of better than 10 nm with a nominal instantaneous field of view (IFOV) of ~2 mrad. The 400 to 2500nm spectral range provides abundant information about many important Earth-surface minerals. A ground mineral scan experiment and an UAV carried flying experiment has been done. The experiment results show the LAISS have achieved relative high performance levels in terms of signal to noise ratio and image quality. The potential applications for light weight airborne imaging spectrometer system in mineral exploration are tremendous.

A nonlinear spectral unmixing method for abundance retrieval of mineral mixtures

Minerals are generally present as intimate mixtures. The spectra of intimate mixtures in visible-infrared are complex function of abundance, grain size, and optical constants et.al, making the linear spectral unmixing model inapplicable. In this paper, we presented a nonlinear unmixing method by combining Shkuratov model (SK99) and Hapke model (H81) to unmix the mineral mixtures. For obtaining the abundances of mineral endmembers, we built up a look-up table (LUT) in the following steps: First, the optical constants were derived by SK99 model and then single scattering albedos of endmembers were computed. Second, the approximation of multiple scattering was derived by the Chandrasekhar H-function. Finally, LUT was established using H81 model. The root-mean-square error (RMSE) was calculated to find the best match between the reflectance of mixtures and LUT. We used the laboratory mineral mixtures to verify the accuracy of abundance estimation. The results show that RMSEs are less than 1% and the absolute errors of abundance retrieval are within 5%. The presented method can retrieve mineral abundance effectively and rapidly. It can be a potential method applying for hyperspectral images of the earth and planetary.