Huazhong Ren

Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands

Fractional vegetation cover (FVC) is one of the most important criteria for surface vegetation status. This criterion corresponds to the complement of gap fraction unity at the nadir direction and accounts for the amount of horizontal vegetation distribution. This study aims to directly validate the accuracy of FVC products over crops at coarse resolutions (1 km) by employing field measurements and high-resolution data. The study area was within an oasis in the Heihe Basin, Northwest China, where the Heihe Watershed Allied Telemetry Experimental Research was conducted. Reference FVC was generated through upscaling, which fitted field-measured data with spaceborne and airborne data to retrieve high-resolution FVC, and then high-resolution FVC was aggregated with a coarse scale. The fraction of green vegetation cover product (i.e., GEOV1 FVC) of SPOT/VEGETATION data taken during the GEOLAND2 project was compared with reference data. GEOV1 FVC was generally overestimated for crops in the study area compared with our estimates. Reference FVC exhibits a systematic uncertainty, and GEOV1 can overestimate FVC by up to 0.20. This finding indicates the necessity of reanalyzing and improving GEOV1 FVC over croplands.

Improved Methods for Spectral Calibration of On-Orbit Imaging Spectrometers

Accurate radiometric and spectral calibrations of hyperspectral remote sensing instruments are essential for optimum data processing and exploitation. Two improved methods for the refinement of the spectral calibration of air- and spaceborne imaging spectrometers are presented in this paper. Both spectral channel position and width can be retrieved by modeling the atmospheric absorption features around 760, 940, 1140, and 2060 nm without making use of external atmospheric or surface parameters. A sensitivity analysis based on synthetic data demonstrated that, for each of the two methods, the root-mean-square errors to be expected were less than 0.18 nm for the retrieval of channel wavelength center and less than 0.8 nm for channel full-width at half-maximum. The application of the proposed methods to a real Hyperion data set showed quite-similar cross-track variations in the spectral calibration for the two methods, although relatively large differences in magnitude were found near the 940- and 1140-nm H2O absorption features. The significant improvement of the reflectance spectra derived after the refinement of the instrument spectral calibration confirms the good performance of the proposed methods.

A portable Multi-Angle Observation System

This paper presents a portable Multi-Angle Observation System (MAOS) to quickly collect bi-directional reflectance factor (BRF) and directional thermal radiance of land surface along with the spectroradiometer and thermal radiometer. The new system is able to make more than 13 zenith measurements in six minutes at an arbitrary azimuth direction, with the angle-controlling accuracy better than 2°. More observations are sampled in the hot-spot direction. All operations of the MAOS and data-processing are automatically controlled by the computer. Field campaign of winter wheat canopy shows that the MAOS had captured the angular variations of the BRF.

Atmospheric water vapor retrieval from Landsat 8 thermal infrared images

Atmospheric water vapor (wv) is required for the accurate retrieval of the land surface temperature from remote sensing data and other applications. This work aims to estimate wv from Landsat 8 Thermal InfraRed Sensor (TIRS) images using a new modified split-window covariance-variance ratio (MSWCVR) method on the basis of the brightness temperatures of two thermal infrared bands. Results show that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.3 g/cm2 for dry atmosphere (wv <2 g/cm2) conditions and better than 0.5 g/cm2 for wet atmosphere conditions. The method was applied at different locations with dry and moist atmospheres and was validated at 42 ground sites using AERONET (Aerosol Robotic Network) ground-measured data and MODIS (Moderate Resolution Imaging Spectroradiometer) products. The results show that the retrieved wv from the TIRS data is highly correlated with the wv of AERONET and MODIS but is generally larger. This difference was probably attributed to the uncertainty of radiometric calibration and stray light coming outside from field of view of TIRS instrument in the current images. Consequently, the data quality and radiometric calibration of the TIRS data should be improved in the future.

Scale Effect in Indirect Measurement of Leaf Area Index

Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beers law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one.

A new narrow band vegetation index for characterizing the degree of vegetation stress due to copper: the copper stress vegetation index (CSVI)

ABSTRACT This study proposed a new narrow band index to characterize the Cu (Copper) stress degree on vegetation (Copper Stress Vegetation Index, CSVI). Firstly, the spectral reflectance and biochemical data of wheat, pea, locust and ash were analysed using Pearson correlation coefficient (r) to select wavelengths sensitive to Cu stress. The calculated Pearson correlation coefficients suggested that the reflectance near 550 nm and 700 nm correlated positively with Cu contents in leaves and solutions, and negative correlation was present in the range of 800–900 nm. Secondly, the selected wavelengths of 550 nm, 700 nm, and 850 nm were used to establish CSVI, and it was compared with existing popular vegetation indices (VIs) related to heavy metal stress (Normalized Difference Vegetation Index (NDVI), Red-Edge Position (REP), Difference Vegetation Index (DVI), Photochemical Reflectance Index (PRI)) by calculating Pearson correlation coefficient between VIs and Cu contents in leaves and solutions. Thirdly, verifications of CSVI on other vegetations were conducted, and the performance of CSVI was also compared with that of NDVI, REP, DVI, and PRI. The results suggested that CSVI showed significant correlation with Cu stress degree, and the correlation of CSVI was much stronger than that of other VIs for all the tested vegetations. The proposed CSVI characterizes the Cu stress degree on vegetation with advantages of better effectiveness, straightforward calculation, and robustness for different vegetations. This study focused on the spectral reflectance at the leaf scale, so it is expected that future work extends it to canopy scale and mixed-pixel scale.

Split-Window algorithm for estimating land surface temperature from Landsat 8 TIRS data

On the basis of the thermal infrared radiative transfer theory, this paper addressed the retrieval of Land Surface Temperature (LST) from Landsat 8-the latest satellite in the Landsat Data Continuity Mission (LDCM) project in two thermal infrared channels, using the Generalized Split-Window (GSW) algorithm. Meanwhile, a linear bidirectional reflectance distribution function (BRDF) models were used to estimate the emissivity according to different surface classification. A series of ranging of typical surface emissivity and the atmospheric water vapor content (WV) were used into an accurate atmospheric radiative transfer model MODTRAN 4.3 to derive the coefficients in the algorithm. The simulation result showed the LST estimated by the algorithm with the Root Mean Square Error (RMSE) is 1.26K for the all ranges of the atmospheric WV and the results could be better in lower atmospheric WV condition.

Spatial scaling transformation modeling based on fractal theory for the leaf area index retrieved from remote sensing imagery

Abstract. This paper proposes a scaling transfer model based on fractal theory to retrieve the leaf area index (LAI) at different spatial resolutions and to evaluate the scaling bias on the LAI retrieved from coarse resolution images. The LAI scaling transfer model was developed by establishing the double logarithmic linear relationship between the scale n (spatial resolution) and average LAIs of the image at different scales. Thereafter, the influences of four factors, namely, coefficients of LAI retrieval model, image size, spatial resolution, and image standard deviation, which may have impact on the scaling transfer model were analyzed. The results indicated that the scaling transfer model performed well in estimating LAI with a determination coefficient (R2) value of 96.99% and in evaluating the scaling bias with a root-mean-square error of 0.0188. The scaling transfer model was considerably influenced by the image standard deviation. As the model parameter, the fractal dimension of image was highly correlated with the standard deviation of the normalized difference vegetation index image. Results indicated that the proposed method based on fractal theory is feasible for LAI spatial scaling transformation.

A modified vegetation index based algorithm for thermal imagery sharpening

Land surface temperature (LST) at both high spatial and high temporal resolution is required for routine monitoring of surface energy fluxes. Disaggregating LST to the NDVI-pixel resolution is possible because of significant inverse relationship between LST and vegetation indices. A modified algorithm (SWISF) has been proposed for thermal imagery sharpening, in which multiple least-squares regression relationships between LST and vegetation indices were acquired for bins of pixels with different soil wetness index values. Applying both SWISF and Distrad which is originally proposed by Kustas et al. to simulated thermal maps at 360m resolution and sharpening down to 90m shows that the new algorithm slightly outperform the old one. Moreover, DisTrad does not have the ability to consider the fact that two pairs of pixels with the same NDVI difference may have distinct LST difference under different soil moisture conditions, while SWISF algorithm could consider it to some extent.

Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS) program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL), and Fraction of Photosynthetically Active Radiation (FPAR), to investigate the dominant factor for GPP dynamics. Results demonstrated that (1) the spatial distribution of accumulated GPP (AG) in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2) interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3) interannual changes of accumulated GPP in the growing season (AGG) at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (p < 0.05) during the period of 1982–2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4) an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas.