Liangyun Liu

Detecting solar-induced chlorophyll fluorescence from field radiance spectra based on the Fraunhofer line principle

It is difficult to quantify the amount of chlorophyll fluorescence emitted by a leaf or canopy under natural sunlight because the reflected light obscures the fluorescence signal. In this study, two diurnal experiments were conducted on winter wheat (Triticum aestivum L.) and Japan Creeper (Parthenocissus tricuspidata) to detect the solar-induced chlorophyll fluorescence from field radiance spectra. In the separation of the fluorescence emissive signal from canopy radiance spectrum based on Fraunhofer lines, two Fraunhofer lines of the terrestrial oxygen absorption at 688 and 760 nm were observed in the radiance spectra by an Analytical Spectral Devices FieldSpec Pro NIR spectrometer, which largely overlaps the chlorophyll fluorescence emission spectrum of leaves. Therefore, Fraunhofer lines at 688 and 760 nm were selected to detect the emissive fluorescence. The diurnal changes of chlorophyll fluorescence in the two experiments were primarily affected by the diurnal changes of photosynthetically available radiation (PAR). The correlation coefficients (R/sup 2/) were greater than 0.9 for all the relationships between PAR and the solar-induced fluorescence of winter wheat and Japan Creeper at 688 and 760 nm based on Fraunhofer line-depth (FLD), suggesting that the solar-induced fluorescence could closely track the changes of PAR and chlorophyll fluorescence. The relative solar-induced fluorescence based on FLD was negatively related to Fv/Fm measured by an OS1-FL modulated chlorophyll fluorometer. The correlation coefficients (R/sup 2/) were 0.97 at 688 nm and 0.99 at 760 nm for winter wheat, and 0.79 at 688 nm and 0.78 at 760 nm for Japan Creeper. These results demonstrate that the solar-induced fluorescence from plant canopies can be detected from field radiance spectra based on the Fraunhofer line principle.

Predicting winter wheat condition, grain yield and protein content using multi‐temporal EnviSat‐ASAR and Landsat TM satellite images

Since optical and microwave sensors respond to very different target characteristics, their role in crop monitoring can be viewed as complementary. In particular, the all‐weather capability of Synthetic Aperture Radar (SAR) sensors can ensure that data gaps that often exist during monitoring with optical sensors are filled. There were three Landsat Thematic Mapper (TM) satellite images and three Envisat Advanced Synthetic Aperture Radar (ASAR) satellite images acquired from reviving stage to milking stage of winter wheat. These data were successfully used to monitor crop condition and forecast grain yield and protein content. Results from this study indicated that both multi‐temporal Envisat ASAR and Landsat TM imagery could provide accurate information about crop conditions. First, bivariate correlation results based on the linear regression of crop variables against backscatter suggested that the sensitivity of ASAR C‐HH backscatter image to crop or soil condition variation depends on growth stage and time of image acquisition. At the reviving stage, crop variables, such as biomass, Leaf Area Index (LAI) and plant water content (PWC), were significantly positively correlated with C‐HH backscatter (r = 0.65, 0.67 and 0.70, respectively), and soil water content at 5 cm, 10 cm and 20 cm depths were correlated significantly with C‐VV backscatter (r = 0.44, 0.49 and 0.46, respectively). At booting stage, only a significant and negative correlation was observed between biomass and C‐HH backscatter (r = −0.44), and a saturation of the SAR signal to canopy LAI could explain the poor correlation between crop variables and C‐HH backscatter. Furthermore, C‐HH backscatter was correlated significantly with soil water content at booting and milking stage. Compared with ASAR backscatter data, the multi‐spectral Landsat TM images were more sensitive to crop variables. Secondly, a significant and negative correlation between grain yield and ASAR C‐HH & C‐VV backscatter at winter wheat booting stage was observed (r = −0.73 and −0.55, respectively) and a yield prediction model with a correlation coefficient of 0.91 was built based on the Normalized Difference Water Index (NDWI) data from Landsat TM on 17 April and ASAR C‐HH backscatter on 27 April. Finally, grain protein content was found to be correlated significantly with ASAR C‐HH backscatter at milking stage (r = −0.61) and with Structure Insensitive Pigment Index (SIPI) data from Landsat TM at grain‐filling stage (r = 0.53), and a grain protein content prediction model with a correlation coefficient of 0.75 was built based on the C‐HH backscatter and SIPI data.

Estimating winter wheat plant water content using red edge parameters

Remote sensing of plant water content is difficult because the absorption band sensitive to foliar liquid water is also sensitive to the atmospheric vapour. A method using non-water-absorption spectral parameters to evaluate plant water content (PWC) would be valuable. In our experiment, canopy spectra of 48 winter wheat treatments with different varieties, different fertilization and irrigation levels were measured by an ASD FieldSpec FR spectrometer in six different growth stages from erecting stage to milking stage, and the PWCs of the related wheat plant samples were also measured. Significant positive coefficients of correlation were observed between PWC and spectral reflectance in 740–930 nm region in all of the six different growth stages, which indicates that the NIR spectral reflectance increases due to the effect of PWC on the leaf internal structure. This mechanism also affects the red edge spectrum in 680–740 nm region. The spectral reflectance increases more rapidly and the red edge becomes steeper if PWC is higher. The coefficients of correlation between PWC and red edge width, derived from the inverted-Gaussian model, are significant at the 0·999 confidence-level, which is more reliable than WI and NDWI, and the statistical models for PWC based on red edge width were set up in all the six different growth stages. In addition, LAI and canopy chlorophyll density (CCD) are also related to red edge parameter, such as red edge position and red edge width. It seems that PWC plays a more important role in red edge width than LAI and CCD due to the effect of PWC on the leaf internal structure, and that CCD plays a more important role in red edge position than LAI and PWC.

The delineation of agricultural management zones with high resolution remotely sensed data

Remote sensing (RS) techniques have been widely considered to be a promising source of information for land management decisions. The objective of this study was to develop and compare different methods of delineating management zones (MZs) in a field of winter wheat. Soil and yield samples were collected, and five main crop nutrients were analyzed: total nitrogen (TN), nitrate nitrogen (NN), available phosphorus (AP), extractable potassium (EP) and organic matter (OM). At the wheat heading stage, a scene of Quickbird imagery was acquired and processed, and the optimized soil-adjusted vegetation index (OSAVI) was determined. A fuzzy k-means clustering algorithm was used to define MZs, along with fuzzy performance index (FPI), and modified partition entropy (MPE) for determining the optimal number of clusters. The results showed that the optimal number of MZs for the present study area was three. The MZs were delineated in three ways; based on soil and yield data, crop RS information and the combination of soil, yield and RS information. The evaluation of each set of MZs showed that the three methods of delineating zones can all decrease the variance of the crop nutrients, wheat spectral parameters and yield within the different zones. Considering the consistent relationship between the crop nutrients, wheat yield and the wheat spectral parameters, satellite remote sensing shows promise as a tool for assessing the variation in soil properties and yield in arable fields. The results of this study suggest that management zone delineation using RS data was reliable and feasible.

Identifying Crop Leaf Angle Distribution Based on Two-Temporal and Bidirectional Canopy Reflectance

The effect of crop leaf angle on the canopy-reflected spectrum cannot be ignored in the inversion of leaf area index (LAI) and the monitoring of the crop-growth condition using remote-sensing technology. In this paper, experiments on winter wheat (Triticum aestivum L.) were conducted to identify the crop leaf angle distribution (LAD) by two-temporal (erecting and elongation stages) and bidirectional in situ reflected spectrum and the Airborne Multiangle Thermal Infrared (TIR) Visible Near-Infrared (VNIR) Imaging System (AMTIS) images. The distribution characters of the leaf angle for different LAD varieties were expressed using the beta-distribution function and the SAILTH radiative transfer models. The proportion of the leaf angle in 5deg angle classes (from 5deg to 90deg) for erectophile, planophile, and horizontal varieties was dominated by 75deg, 55deg, and 35deg. The different LAD varieties had a similar canopy reflectance in 680 nm (red) and 800 nm (near-infrared band) at the erecting stage, while they had significant differences at the elongation stage. The ratio of the canopy reflectance of 800 nm at the erecting stage [R800(B)] to the canopy reflectance of 800 nm at the elongation stage [R800(A)] was used to identify the different LAD varieties through the selected two-temporal canopy reflectance. A method based on the semiempirical model of the bidirectional reflectance distribution function (BRDF) was also introduced in this paper. The structural parameter-sensitive index (SPEI) was used in this paper for crop LAD identification. SPEI is proved to be more sensitive to identify erectophile, planophile, and horizontal LAD varieties than the structural scattering index and the normalized difference f-index. We found that it is feasible to identify horizontal, planophile, and erectophile LAD varieties of wheat by studying two-temporal and bidirectional canopy-reflected spectrum

Measurement and Analysis of Bidirectional SIF Emissions in Wheat Canopies

Numerous observations and modeling results have shown that there is noticeable directional variation in the solar-induced chlorophyll fluorescence (SIF), and this has not been well investigated. In this paper, 16 multiangular spectral observations were carried out on winter wheat to assess the bidirectional SIF emission. First, the bidirectional SIF emission was retrieved from the spectral measurements made by a high-performance QE Pro spectrometer and an automatic multiangle observation system using the 3FLD algorithm. The bidirectional shape of the SIF emission was found to be similar to that of the canopy reflectance in the solar principal plane, with a mean correlation coefficient of 0.94 and 0.97 at the O2-B and O2-A bands, respectively. The modified Rahman-Pinty-Verstraete (MRPV) model, a semiempirical bidirectional reflectance distribution function (BRDF) model, was then employed to describe the bidirectional variation in the SIF and reflectance with a mean root-mean-square-error value of 0.036 and 0.041 mW m-2sr-1nm-1 for the SIF at the O2-B and O2-A bands, respectively. Finally, both the bidirectional reflectance and SIF were BRDF corrected to nadir using the MRPV model. Most of the directional variation was successfully corrected by this method-the mean correction ratios were 87% and 81% for the reflectance at the O2-B and O2-A bands and 84% and 72% for the SIF at the O2-B and O2-A bands, respectively. Therefore, the SIF emission cannot be regarded as isotropic, and the high similarity between the bidirectional SIF and reflectance, together with the BRDF correction results, indicates that the bidirectional SIF emission can be adjusted using either the BRDF reflectance models or prior knowledge.

Detection of Vegetation Light-Use Efficiency Based on Solar-Induced Chlorophyll Fluorescence Separated From Canopy Radiance Spectrum

Photosynthetic light-use efficiency (LUE) is an important indictor of plant photosynthesis, but it is not yet assessable by remote sensing. The recent research on the separation of solar-induced chlorophyll fluorescence (ChlF) from the hyperspectral data indicates the possibility of detecting LUE. In this study, we presented a novel solution for monitoring LUE from hyperspectral data. Experiments at leaf level and canopy level were carried out on winter wheat (C3 plant functional type) on 18 April, 2008 and summer maize (C4 plant functional type) on 5 July, 2008 by synchronously measuring daily canopy radiance spectra and leaf or canopy LUE. The solar-induced chlorophyll fluorescence signals at 760 nm and 688 nm were separated from the reflected radiance spectra based on Fraunhofer lines in two oxygen absorption bands. The results showed that LUE was inversely related to the relative chlorophyll fluorescence. The leaf-level LUE models for winter wheat were built based on relative ChlF at bands of 688 nm (R2=0.78) and 760 nm (R2=0.64), whereas correlation coefficients of the canopy-level LUE models for summer maize on relative ChlF at the same bands were 0.63 and 0.77, respectively.

Analysis of the changes of vegetation coverage of western Beijing mountainous areas using remote sensing and GIS

Mentougou District acts as a crucial component in the ecological buffer in western Beijing mountainous areas, Beijing, China. Using two Landsat MSS/TM images acquired on July 14, 1979 and July 23, 2005, the vegetation coverage of Mentougou District was calculated based on normalized difference vegetation index and spectral mixture analysis (NDVI-SMA) model. Its temporal and spatial changes were analyzed according to digital elevation model (DEM) image, social and economic data. The results showed that the vegetation coverage decreased from 76.4% in 1979 to 72.7% in 2005. Vegetation degradation was probably the result of human disturbance, such as outspreading of resident areas, and coal and stone mining activities, while vegetation restoration might be contributed by the combined effects of both natural processes and ecological construction effort. Vegetation changes were closely related to topographical characteristics. Plants at high altitude were more stable and less degraded than the plants at low altitude, while the plants on steep slope or northwest aspect were more vulnerable to degradation. During the period of 26 years, landscape appeared to become more fragmental, and ecological quality of the land seemed deteriorated sharply in that highly-covered vegetation area has been decreased by 24%.

Characteristics and drivers of global NDVI‐based FPAR from 1982 to 2006

Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is a state parameter in most ecosystem productivity models and is also the key terrestrial product. In this study, Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) was used to estimate FPAR from 1982 to 2006, using an intermediate model. Our research focused on the analysis of long-term global FPAR interannual trend patterns and driving forces involving climate and land cover changes. Results showed that interannual trend and spatial distribution patterns of global FPAR were independent of the changes in AVHRR instruments, and differed by season dynamics and vegetation types. Compared with other seasons, the period during JJA (June-August) exhibited more areas with decreasing FPAR and greater reduction range. For FPAR interannual trend, a wholly different correlation pattern was observed between temperature and precipitation, especially for arid and semi-arid regions. A significant influence of extreme droughts such as those associated with El Nino/Southern Oscillation (ENSO) on FPAR variability was found. The result also revealed the increasing and decreasing interannual trend of FPAR corresponding to the afforestation in the Three North Shelterbelts Program in China and deforestation in tropical forests in Southeast Asia. Driving factor analysis indicated that the climate and land cover changes had an interactive effect on the FPAR annual anomalous variation.

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.