Quanjun Jiao

Assessing photosynthetic light-use efficiency using a solar-induced chlorophyll fluorescence and photochemical reflectance index

Photosynthetic light-use efficiency (LUE) is an important indicator of plant photosynthesis, but assessment by remote sensing needs to be further explored. In this study, two protective mechanisms for photosynthesis, chlorophyll fluorescence (ChlF) and heat dissipation in the deep oxidation state of the xanthophyll cycle, were explored to estimate photosynthetic LUE from canopy radiance spectra. Four independent experiments were carried out on summer maize (C4 plant) on 5 July 2008, and winter wheat (C3 plant) on 18 April 2008, 16 April 2010, and 13 May 2010, by synchronously measuring daily canopy radiance spectra and photosynthetic LUE. The competitive relationship between ChlF and photochemical yield made it possible to estimate photosynthetic LUE. LUE–ChlF models were developed for ChlF at 688 nm (R 2 = 0.72) and 760 nm (R 2 = 0.59) based on the experimental data from 13 May 2010 at the Guantao flux site. The LUE–ChlF models were validated by three independent experiments, and the results showed that the LUE–ChlF relationship was weakened, possibly by variation in species, canopy density, and environmental conditions. As an easy, rapid, non-intrusive method, a photochemical reflectance index (PRI) provides an instantaneous assessment of dynamic photosynthetic LUE. The significant negative relationship between non-photochemical quenching and PRI was confirmed. Although there was a significantly positive relationship between LUE and PRI in all four independent experiments, this was evidently weakened by the canopy and environmental conditions. Difference in PRI (ΔPRI) from the minimum reference PRI around noontime can largely eliminate interference factors. The LUE–ΔPRI model was developed based on experimental data from 13 May 2010 at the Guantao flux site (with an R 2 value of 0.85), and validated by the three other independent experiments. The validation result indicated that different species can markedly affect the precision of the PRI difference method.

Monitoring the distribution of C3 and C4 grasses in a temperate grassland in northern China using moderate resolution imaging spectroradiometer normalized difference vegetation index trajectories

Using remote-sensing technologies, this study sought to provide an up-to-date map of C3/C4 distribution representative of temperate grassland in northern China. Several studies focused on the central grasslands of North America have demonstrated that C4 species coverage can be discriminated from C3 species by using time-series vegetation index data based on their phenological differences. Considering that the hydrothermal patterns and C4 percentage of grass flora in the study area and North America are different, we first examined temporal features of C3/C4 communities by using multitemporal moderate resolution imaging spectroradiometer normalized difference vegetation index data throughout the 2010 growing season. It was found that the asynchronous seasonality exhibited by communities with varied C3/C4 compositions also existed in our study region. Based on this asynchrony and separation rate, a hierarchical decision tree was developed to classify four grassland types with varied C3/C4 compositions. As a result, a classification map of the mixed C3/C4 grassland was generated with an overall accuracy of 87.3% and a kappa coefficient of 0.83. The geographic distribution of C3 and C4 species showed that the study area was dominated by C3 grasses, but C4-dominated grasslands accounted for 39% of the land cover. Thus C4 species also made an important contribution to grassland biomass, especially in dry and low-lying saline-alkaline habitats. The results also indicated that the trajectory-based methods for C3/C4 mapping rooted in asynchronous seasonality worked effectively in the climate regimes of both northern China and North America.

A Landsat-5 Atmospheric Correction Based on MODIS Atmosphere Products and 6S Model

The Landsat satellite series represents the longest record of global-scale medium spatial resolution earth observations, and the utility of Landsat data for long-term and/or large-area monitoring depends on accurate and quantitative atmospheric correction to produce a consistently corrected surface reflectance (SR) dataset. In this study, we developed a rapid, automated atmospheric correction procedure based on Moderate Resolution Imaging Spectrometer (MODIS) atmospheric characterization products and the 6S (Second Simulation of a Satellite Signal in the Solar Spectrum) radiative transfer code for Landsat-5 Thematic Mapper (TM) imagery. Three MODIS atmosphere products at the resolution of 0.05°, MOD04, MOD05, and MOD07 were used as input to the 6S radiative transfer model in order to compute the parameters required for atmospheric correction, which were then used to correct TM imagery per-pixel automatically. This method was tested using five multi-date Landsat TM images in Beijing, China, and the atmospheric correction precision was assessed using ground-measured reflectance. The result showed that the SR retrieved from Landsat TM is consistent with the ground measurements, with a mean R2 of 0.773 and a mean root mean square error value of 0.045.

A Novel in Situ FPAR Measurement Method for Low Canopy Vegetation Based on a Digital Camera and Reference Panel

The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter in describing the exchange of fluxes of energy, mass and momentum between the surface and atmosphere. In this study, we present a method to measure FPAR using a digital camera and a reference panel. A digital camera was used to capture color images of low canopy vegetation, which contained a reference panel in one corner of the field of view (FOV). The digital image was classified into photosynthetically active vegetation, ground litter, sunlit soil, shadow soil, and the reference panel. The relative intensity of the incident photosynthetically active radiation (PAR), scene-reflected PAR, exposed background absorbed PAR and the green vegetation-covered ground absorbed PAR were derived from the digital camera image, and then FPAR was calculated. This method was validated on eight plots with four vegetation species using FPAR measured by a SunScan instrument. A linear correlation with a coefficient of determination (R2) of 0.942 and mean absolute error (MAE) of 0.031 was observed between FPAR values derived from the digital camera and measurement using the SunScan instrument. The result suggests that the present method can be used to accurately measure the FPAR of low canopy vegetation.

Comparison of absolute and relative radiometric normalization use Landsat time series images

For most remote sense image applications, variations in solar illumination conditions, atmospheric scattering and absorption, and detector performance need to be normalized, especially in time series analysis such as change detection. For the purpose of radiometric correction, two levels of radiometric correction, absolute and relative, have been developed for remote sense imagery. In this paper, we select the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm as the Atmospheric correction method, and compare it with an automatic method for relative radiometric normalization based on a linear scale invariance of the multivariate alteration detection (MAD) transformation. The performances of both methods are compared using a landsat TM image pairs, the results from the two techniques have been compared both visually and using a measure of the fit based on standard error statistic.

A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index

Remote sensing estimation of leaf chlorophyll content is of importance to crop nutrition diagnosis and yield assessment, yet the feasibility and stability of such estimation has not been assessed thoroughly for mixed pixels. This study analyses the influence of spectral mixing on leaf chlorophyll content estimation using canopy spectra simulated by the PROSAIL model and the spectral linear mixture concept. It is observed that the accuracy of leaf chlorophyll content estimation would be degraded for mixed pixels using the well-accepted approach of the combination of transformed chlorophyll absorption index (TCARI) and optimized soil-adjusted vegetation index (OSAVI). A two-step method was thus developed for winter wheat chlorophyll content estimation by taking into consideration the fractional vegetation cover using a look-up-table approach. The two methods were validated using ground spectra, airborne hyperspectral data and leaf chlorophyll content measured the same time over experimental winter wheat fields. Using the two-step method, the leaf chlorophyll content of the open canopy was estimated from the airborne hyperspectral imagery with a root mean square error of 5.18 μg cm−2, which is an improvement of about 8.9% relative to the accuracy obtained using the TCARI/OSAVI ratio directly. This implies that the method proposed in this study has great potential for hyperspectral applications in agricultural management, particularly for applications before crop canopy closure.

Assessment of spatio-temporal variations in vegetation recovery after the Wenchuan earthquake using Landsat data

The 8.0-magnitude Wenchuan earthquake in 2008 damaged the ecology of northwestern Sichuan, China. This study assessed ecological changes within a few years of the earthquake through satellite observations of vegetation dynamics in the earthquake area. As an ecological indicator, the fractional vegetation cover was extracted using the Normalized Difference Vegetation Index based on multi-year Landsat images and was validated using airborne images. We found that the entire mountainous disaster area had recovered by 68.45xa0% 3xa0years after the 2008 earthquake. After rapid recovery of vegetation in 2009, the recovery process slowed. The vegetation recovery rate (VRR) in the area heavily damaged by landslides was slightly lower but nearly that of the entire disaster area. In addition, because of differences in the proportions of soil and rock in the damaged areas, recovery of vegetation in the southwest portion of the study area was slower than in the northeast areas. Topographic analysis of vegetation recovery patterns indicated that damage to vegetation was closely related to slope, while recovery of vegetation was more sensitive to elevation. The landscape analysis showed that the recovery rate (65.21xa0%) of the excellent vegetation cover type was slower than the overall VRR. This study suggests that vegetation recovery is a slow ecological process and that ecological restoration should be implemented in mountainous regions affected by the earthquake.

Exploring vegetation photosynthetic light-use efficiency using hyperspectral data

Photosynthetic light-use efficiency (LUE) is an important indicator of plant photosynthesis, but assessment by remote sensing needs to be further explored. In principle, chlorophyll fluorescence combined with heat dissipation is an expression of the balance between light harvesting (absorption) and light utilization in the photosynthetic process. The aim of the present study was to examine the above principles using solar-induced chlorophyll fluorescence (ChlF) and photochemical reflectance index (PRI), which is sensitive to dynamic changes in the xanthophyll cycle. LUE-ChlF models were developed for ChlF at 688 nm (R2 = 0.72) and 760 nm (R2 = 0.59) based on the experiment data for winter wheat, which were also validated by three independent experiments, and the validation results showed that the LUEChlF relation was weakened, possibly by different species, canopy density and environmental conditions. Furthermore, the significant negative relation between non-photochemical quenching (NPQ) and PRI was confirmed. However, the PRI-LUE relation was evidently weakened by the canopy and environmental conditions. The PRI difference (ΔPRI) from the minimum reference PRI around noontime could greatly eliminate the interference factors. The LUE-ΔPRI model was developed based on the experiment data for winter wheat (with an R2 value of 0.85), and validated by other three independent experiments.

Estimating fractional vegetation cover in the Wenchuan earthquake disaster area using high-resolution airborne image and Landsat TM image

The fractional vegetation cover (Fvc) can be characterized by strong temporal dynamics that need to be considered for environmental recovery assessment in the Wenchuan earthquake disaster area. This study is to test the capacity of satellite remote sensing images in estimating fractional vegetation cover in the Sichuan disaster area. Two different spectral mixture analysis methods including N* and N*2 transformed from NDVI are applied for fractional vegetation cover estimation using Landsat TM image. High-resolution fractional vegetation cover production from airborne images is used for validation. The result shows that N* has the non linear relationship with airborne fractional vegetation cover values in this study area and N*2 takes a better performance (R2=0.993). In total, retrieved Fvc values from Landsat TM image have a strong relationship to those from airborne image, and can be used in environmental dynamics assessment in the Wenchuan Earthquake disaster area.

Mapping land cover of the Yellow River source using multi-temporal Landsat images

Land cover is a crucial product required to be calibrated, validated and used in various land surface models that provide the boundary conditions for the simulation of climate, carbon cycle and ecosystem change. This paper presented a method to map land cover from multitemporal landsat images using Dempster-Shafer theory of evidence. The method firstly resolved in Gaussian probability density function calculate the basic probability assignment of each single satellite image, then multitemporal landsat images were combined using Dempsters Rule of combination. Finally, a decision rule based on ancillary information is used to make classification decisions. This method had 87.91% overall accuracy for the land cover types compared with the result of the Aerial hyperspectral image classification. The results of this study showed that Dempster-Shafer theory of evidence is an effective tool to map land cover using multitemporal landsat image.