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Chlorophyll content can effectively reflect the health (growth condition) of vegetation. At present, chlorophyll retrieval is mostly based on pure species and traditional spectral indices but seldom considers the influence of canopy structural differences caused by interspecific competition of mixed vegetation on the accuracy of chlorophyll retrieval. Therefore, in this research, the native species Phragmites australis and invasive species Spartina alterniflora in the wetland of the Yangtze River Estuary were studied, and the mixed growth of the two species in the field was simulated accurately by setting different competition ratios. The differences in canopy spectral characteristics caused by different competition ratios and the effect of interspecific competition on the retrieval accuracy of canopy chlorophyll content (CCC) were analyzed. It is found that different competition ratios will lead to different spectral characteristics of the vegetation canopy, but these differences poorly reflect the spectral dimension. To solve this problem, the Hilbert-Huang transform (HHT) theory is introduced to decompose the spectrum and obtain frequency domain information, which is combined with spectral information to research the spectral response characteristics and retrieval of CCC under different interspecific competition ratios. Finally, the necessity of considering interspecific competition under mixed growth is analyzed by using the PROSAIL-D model from remote sensing. The results show that (1) the retrieval accuracy of CCC based on pure spectral indices is lower than that based on the mixed indices under different competition ratios, especially under the competition ratio of P:S = 1:1, and the R2 of the pure spectral index model is 0.714, but the R2 of the model increases to 0.803 after the HHT indices are added, with the accuracy of the retrieval model with the mixed index being the highest, with R2 and RMSE values of 0.874 and 17.378 µg.cm-2, respectively. (2) There are differences in the distribution of spectral index values under different competition ratios based on the remote sensing mechanism results of simulation data from the PROSAIL-D model, especially under mixed growth of Phragmites australis and Spartina alterniflora. (3) The HHT indices ESP1 and ESP2 are more sensitive to changes in the competition ratio under different competition conditions, showing a greater range of values. All the above results indicate that the frequency domain indices can reflect the change in canopy structure of mixed vegetation more accurately, and it is necessary to consider the level of interspecific competition in the retrieval of CCC from mixed vegetation.

Crop residue burning has been proved to have negative impacts on regional atmospheric environment. In this study, an evidence-based statistical modeling framework was established to quantify potential impacts of crop residue burning in the North China Plain (NCP) on summertime tropospheric ozone increase over East Asia during 2004–2016. To assess the intensity of crop residue burning, fire occurrence counts derived from the MODerate-resolution Imaging Spectroradiometer onboard the Terra and Aqua satellites were used as a proxy. Additionally, another six factors were employed as potent explanatory variables. Maximum covariance analysis was first applied to decouple spatiotemporal interactions between tropospheric ozone and each explanatory variable. Based on the decoupled modes, multivariate linear regression (MLR) and artificial neural network (ANN) were used to establish statistical relationships between tropospheric ozone and contributing factors, respectively. The results indicate that the ANN-based modeling scheme enables to approximate the observed tropospheric ozone variations better than MLR. Further investigations reveal that the summertime crop residue burning in the NCP is the predominant factor contributing to the observed additive tropospheric ozone increases over East Asia, yielding extra 8% tropospheric ozone elevation on average in June. Moreover, UV radiation and wind also played critical roles in modulating the observed tropospheric ozone variations therein. In general, the critical role of crop residue burning over the NCP in modulating summertime tropospheric ozone increase over East Asia have been well demonstrated based on the proposed evidenced-based modeling framework.

A four-step adaptive ozone trend estimation scheme is proposed by integrating multivariate linear regression (MLR) and ensemble empirical mode decomposition (EEMD) to analyze the long-term variability of total column ozone from a set of four observational and reanalysis total ozone data sets, including the rarely explored ERA-Interim total ozone reanalysis, from 1979 to 2009. Consistency among the four data sets was first assessed, indicating a mean relative difference of 1% and root-mean-square error around 2% on average, with respect to collocated ground-based total ozone observations. Nevertheless, large drifts with significant spatiotemporal inhomogeneity were diagnosed in ERA-Interim after 1995. To emphasize long-term trends, natural ozone variations associated with the solar cycle, quasi-biennial oscillation, volcanic aerosols, and El Nino–Southern Oscillation were modeled with MLR and then removed from each total ozone record, respectively, before performing EEMD analyses. The resulting rates of change estimated from the proposed scheme captured the long-term ozone variability well, with an inflection time of 2000 clearly detected. The positive rates of change after 2000 suggest that the ozone layer seems to be on a healing path, but the results are still inadequate to conclude an actual recovery of the ozone layer, and more observational evidence is needed. Further investigations suggest that biases embedded in total ozone records may significantly impact ozone trend estimations by resulting in large uncertainty or even negative rates of change after 2000.

People in Huaihe River Basin and Shanghai have been suffering from severe air pollution of nitrogen dioxide and sulfur dioxide due to the development of heavy industry. Traditional ambient monitoring station measurements can provide real-time accurate data, but it is limited due to the less number of monitoring sites. Satellite observation data from remote sensing can provide a wide range opollutants concentrations in long-time sequence. Top-down approaches based on satellite data can be effectively applied to estimate the ground concentrations of pollutants. In this paper, the tropospheric pollutants columns from the Ozone Monitoring Instrument(OMI) were used to analyse the seasonal variation of NO2 and SO2 in 2015. Moreover, the ground-level NO2 and SO2 concentrations of the Huaihe River Basin and Shanghai at this time were estimated by the data and meteorological data. The results show that: the concentrations of NO2 and SO2 are highest in winter, and high-value areas are mainly located in Shandong and Northern Henan. Estimating the ground-level NO2 and SO2 concentrations based on satellite observations is reliable with the validation R2 0.48 and 0.47 respectively. Finally, The spatial distribution of satellite-derived annual mean NO2 and SO2 has a similar characteristics to the satellite columns.

Paddy field is a major source of methane (CH4) emission. Methane emission in paddy fields accounts for 31.5% of agricultural methane emissions in China. Double-rice cropping system is a part of the major paddy systems in China for rice production, accounting for only 27% of the national rice planting area while CH4 emission accounting for 60% of the national CH4 emission. Given the importance of reducing CH4 emission from double rice to mitigate climate warming, it is necessary to investigate the impact of climate change on CH4 emission of double cropping paddy field in the future. In this study, the denitrification–decomposition (DNDC-a process-based biogeochemistry model) model is employed to simulate the CH4 emission from double-rice cropping system in southern China based on the historical meteorological data of the past 50 years (1966-2015) and the observational data of rice agricultural stations in the study area. Then we combined the outputs with Geographic Information System (GIS) technology to analyze the impact of climate change on CH4 emissions from the double rice paddy. The results indicate that change of the average temperature is associated with the change of CH4 emission across the growing period of double rice paddy. Methane has increased by 8.4% in the main producing provinces of double cropping rice in southern China. Zhejiang has increased by up to 20.8%. Anhui, Hubei, Hunan has increased by 10.6%, 10.2% and 11.4%. The relatively small increase in Fujian and Yunnan is only 5%. However, in the low latitudes of Guangxi, and Guangdong province, there was a slight reduction in CH4 emission.

Fine particles less than 2.5 microns in aerodynamic diameter (PM2.5) has found to threat human health and environment. The formation and diffusion of PM2.5 are closely related to the meteorological elements. Many scholars have studied the influence mechanism of meteorological elements to PM2.5. However, most of these researches mainly focus on some serious short-term atmospheric pollution, long-term research is rare. In addition, the impact of meteorological elements on PM2.5 has regional characteristics. This paper takes Shanghai as study area, applying PM2.5 concentrations from China environmental monitoring stations and reanalysis meteorological data from 2014 to 2016.. Through qualitative and quantitative analysis, this paper got the change characteristics of PM2.5 in Shanghai in recent three years, and the correlation between PM2.5 and relative humidity, temperature, wind and boundary layer height. Relative humidity is positively correlated with PM2.5, while U wind is negatively correlated with PM2.5. And there are seasonal differences in the correlation between PM2.5 and temperature, V wind and boundary layer height.

The objective of this study is to evaluate the accuracy of the daily nadir total column ozone products derived from the nadir mapper instrument on the Ozone Mapping and Profiler Suite (OMPS) flying onboard the Suomi National Polar-orbiting Partnership satellite (S-NPP) launched as a part of the Joint Polar Satellite System (JPSS) program between NOAA and NASA. Since NOAA is already operationally processing OMPS nadir total ozone products, evaluations were made in this study on the total column ozone research products generated by NASA’s science team, utilizing the latest version of their Backscatter Ultraviolet (BUV) retrieval algorithms, to provide insight into the performance of the operation system. Comparisons were made with globally distributed ground-based Brewer and Dobson spectrophotometer total column ozone measurements. Linear regressions show fair agreement between OMPS and ground-based total column ozone measurements with a root-mean-square error (RMSE) of approximately 3% (10 DU). The comparison results indicate that the OMPS total column ozone data are 0.59% higher than the Brewer measurements with a standard deviation of 2.82% while 1.09% higher than the Dobson measurements with a standard deviation of 3.27%. Additionally, the variability of relative differences between OMPS and ground total column ozone were analyzed as a function of latitude, time, viewing geometry, and total column ozone value. Results show a 2% bias over most latitudes and viewing conditions when total column ozone value varies between 220 DU and 450 DU.

This work aims to analyze the spatial and temporal variability of aerosol optical depth (AOD) from 2000 to 2012 in the Changjiang River Delta (CRD), China. US Terra satellite moderate resolution imaging spectroradiometer (MODIS) AOD and Angstrom exponent (α) data constitute a baseline, with the empirical orthogonal functions (EOFs) method used as a major data analysis method. The results show that the maximum value of AOD observed in June is 1.00±0.12, and the lowest value detected in December is 0.40±0.05. AOD in spring and summer is higher than in autumn and winter. On the other hand, the α-value is lowest in spring (0.86±0.10), which are affected by coarse particles. High α-value appears in summer (1.32±0.05), which indicate that aerosols are dominated by fine particles. The spatial distribution of AOD has a close relationship with terrain and population density. Generally, high AODs are distributed in the low-lying plains, and low AODs in the mountainous areas. The spatial and temporal patterns of seasonal AODs show that the first three EOF modes cumulatively account for 77% of the total variance. The first mode that explains 67% of the total variance shows the primary spatial distribution of aerosols, i.e., high AODs are distributed in the northern areas and low AODs in the southern areas. The second mode (7%) shows that the monsoon climate probably plays an important role in modifying the distribution of aerosols, especially in summer and winter. In the third mode (3%), this distribution of aerosols usually occurs in spring and winter when the prevailing northwestern or western winds could bring aerosol particles from the inland areas into the central regions of the CRD.

A Bayesian optimal estimation (OE) retrieval technique was used to retreive aerosol optical depth (AOD), aerosol single scattering albedo (SSA), and an asymmetry factor (g) at seven ultraviolet wavelengths, along with total column ozone (TOC), from the measurements of the UltraViolet Multifilter Rotating Shadowband Radiometer (UV-MFRSR) deployed at the Southern Great Plains (SGP) site during March through November in 2009. The OE technique specifies appropriate error covariance matrices and optimizes a forward model (Tropospheric ultraviolet radiative transfer model, TUV), and thus provides a supplemental method for use across the network of the Department of Agriculture UV-B Monitoring and Research Program (USDA UVMRP) for the retrieval of aerosol properties and TOC with reasonable accuracy in the UV spectral range under various atmospheric conditions. In order to assess the accuracy of the OE technique, we compared the AOD retreivals from this method with those from Beer’s Law and the AErosol RObotic Network (AERONET) AOD product. We also examine the OE retrieved TOC in comparison with the TOC from the U.S. Department of Agriculture UV-B Monitoring and Research Program (USDA UVMRP) and the Ozone Monitoring Instrument (OMI) satellite data. The scatterplots of the estimated AOD from the OE method agree well with those derived from Beer’s law and the collocated AERONETAOD product, showing high values of correlation coefficients, generally 0.98 and 0.99, and large slopes, ranging from 0.95 to 1.0, as well as small offsets, less than 0.02 especially at 368 nm. The comparison of TOC retrievals also indicates the promising accuracy of the OE method in that the standard deviations of the difference between the OE derived TOC and other TOC products are about 5 to 6 Dobson Units (DU). Validation of the OE retrievals on these selected dates suggested that the OE technique has its merits and can serve as a supplemental tool in further analyzing UVMRP data.

As one of the major greenhouse gases, atmospheric carbon dioxide CO2 concentrations have been monitored by both top-down satellite observations and air sampling systems on surface stations. The Atmospheric Infrared Sounder AIRS on board NASA’s Aqua low Earth orbit LEO satellite is a high-resolution infrared sounder that has been in operation for more than 10 years. The World Data Centre for Greenhouse Gases WDCGG archives and provides data on CO2 and other greenhouse gases measured mainly from surface stations. In this article, we focus on the correlation between the two different sources of CO2 data and the influencing factors. In general, we find that a linear positive correlation occurs at most stations. However, the variation in the correlation coefficient is large, especially for stations in the Northern Hemisphere. The station’s location, including its latitude, longitude, and altitude, is an important influencing factor because it determines how much its CO2 measurements are influenced by human activities. We also use root mean square difference RMSD and bias as evaluation indicators and find that they have similar trends like correlation coefficients.

As Ozone Monitoring Instrument (OMI) onboard the Aura satellite has provided global scale ozone measurements on a daily basis since 2004, the long-term stability and consistency of ozone retrievals is thus of critical importance, especially for the ozone recovery assessment. This study aims to evaluate the long-term stability of total ozone derived from the OMI Total Ozone Mapping Spectrometer (OMI-TOMS) algorithm, by comparing with collocated ground-based total ozone measurements recorded from 42Dobson spectrophotometers during the period 2004-2015. It is indicative that the OMI-TOMS total ozone is in good agreement with collocated ground-based measurements, with a R2 of 0.96 and root mean square error (RMSE) of 3.3%. Further investigations show that the OMI-TOMS total ozone is of quality, as no significant latitude dependence is observed. In the past 12 years, the OMI-TOMS total ozone is highly consistent with the ground-based Dobson total ozone, with a variation of mean relative difference less than 1%. In general, the OMI-TOMS total ozone performs well and can be used with confidence.

Phragmites australis is a native dominant specie in the Yangtze Estuary, which plays a key role in the structure and function of wetland ecosystem. One key question is how to estimate the Chlorophyll content quickly and effectively at large scales, which could be used to reflect the growth condition and calculate the vegetation productivity. The aim of this work was to estimate Chlorophyll content of P. australis based on the PROSPECT and DART (Discrete Anisotropic Radiative Transfer) model. A total of 6 widely used Vegetation indices (VIs) were chosen (i.e., Normalized Difference Vegetation Index (NDVI), Structure Insensitive Pigment Index (SIPI), Colouration Index (COI), Simple Ratio Index (SR), Cater Index (CAI), and Red-edge Position Linear Interpolation (REP_Li)) and calculated, and then the relationship between VIs and Cab were analyzed. Results showed that COI and SIPI were sensitive to the leaf chlorophyll content as the chlorophyll content changes at the leaf scale. Meanwhile, no obvious saturation phenomenon was observed for these two indices compared to other indices.

This study evaluates the accuracy of total ozone column derived from Ozone Monitoring Instruments (OMI) with two algorithms: OMI Total Ozone Mapping Spectrometer (OMI-TOMS) and OMI Differential Optical Absorption Spectroscopy (OMI-DOAS), compared to ground-based Brewer and Dobson spectrophotometers located at eight China stations from July 2009 to December 2013, including Xianghe, Kunming, Mt.Waliguan, Lhasa, Taipei, Chengkung, Cape D'Aguilar and Longfengshan. Results showed that the agreement between OMI ozone data and ground-based measurements is excellent. Total ozone columns from both OMI-TOMS and OMI-DOAS data are on average about 1.5% lower than ground-based data. For both OMI ozone data products the SZA dependence of the mean relative differences (RD) between satellite data and the ground-based data is relative obvious when the SZA is larger than 50°. Similar to the SZA, the satellite view zenith angle (VZA) dependence of the mean relative differences (RD) between satellite and ground is relatively markedly when the VZA is smaller than 10° in eight stations. Finally, the dependence of the mean relative differences (RD) (-4.28% to 0.818%) between OMI-DOAS data and ground-based data for the total ozone column is remarkable. While for OMI-TOMS data the dependence is not obvious (the RD value varies from -3.30% to -0.676%).

This paper uses PROSAIL model to simulate vegetation canopy reflectance under different chlorophyll contents and Leaf area index (LAI). The changes of NDVIs with different LAIs and chlorophyll contents are analyzed. A simulated spectral dataset was built firstly by using PROSIAL vegetation radiative transfer model with various vegetation chlorophyll concentrations and leaf area index. The responses of NDVIs to LAIs are quantitatively analyzed further based on the dataset. The results show that chlorophyll contents affect canopy reflectance mainly in visible band. Canopy reflectance decreases with an increasing chlorophyll content. Under the same LAI value, NDVI values increase with an increase chlorophyll contents. Under constant content of chlorophyll, NDVIs increases with an increasing LAI. When the value of LAI is less than5, the canopy reflectance is significantly affected by soil background. When value of LAI is higher than5, the earth surface is almost completely covered with vegetation. The increase in LAI has little effect on canopy reflectance and NDVIs consequently. NDVIs increases with the adding of chlorophyll content, when chlorophyll is higher than 40, the rangeability of NDVIs is becoming stable.

This paper provides a coherent pattern identification analysis of coastal land use and land cover (LULC) under the impact of seawater intrusion. This study analysis applied the 4-, 3-, and 2-band false color composite Landsat satellite data to characterize the LULC in the study area. The evapotranspiration (ET) and heat fluxes were estimated by using the SEBAL model with two-time phase thermal infrared band images and regional surface parameters. Our findings are as follows: 1) Due to its distance from the sea, the vegetation index gradually increases as the level of land use gradually increased. 2) The different influences of seawater intrusion in the study area resulted in significantly different influences of land surface parameters (LST, Gn, MSAVI, and Uindex) on ET. There are a variety of types of relational patterns between parameters (LST, Gn, MSAVI and Uindex) and ET (positive, negative, and no relationship). 3) Seawater intrusion significantly affected the spatial pattern of LUCC, which evidently affected the spatial distribution of ET. The spatial distribution pattern and change characteristics of ET were formed by double driving forces of seawater intrusion and LUCC under the background effects of regional climate. © Copyright 2013 SPIE.

Land surface heat and water fluxes are key components of water and energy cycles between land and atmosphere. Information about these surface fluxes can guide agricultural production and environmental preservation, and manage different ecosystems to mitigate climate change. The main objective of this work is to estimate the surface heat fluxes and evapotranspiration. For this purpose, the Community Land Model Version 3 was used, atmospheric forcing data and flux observation were extracted from AmeriFlux standardized Level 2 database, then surface heat fluxes under two different underlying surfaces were modeled. The results showed that the model works well regarding the simulation of daily surface fluxes and diurnal surface fluxes although these values were underestimated relative to the values observed from eddycovariance. After validation, evapotranspiration was chosen as the indicator for specific comparison. CLM3.0 showed a better performance in simulating the moisture and evapotranspiration.

Aerosol optical depth (AOD) is critically important for a better understanding of how Earth’s climate is radiatively forced. To compensate for the conventional satellite observations, several types of ground-based radiometers are operated by AOD measurement programs. This study compares the Bratts Lake climate station’s long-term AOD measurements from 1999 to 2012 which are derived from two ground-based programs with high accuracy: the United States Department of Agriculture (USDA) UV-B Monitoring and Research Program (UVMRP) and the AERONET (AErosol RObotic NETwork) program. The comparison shows that, in the 14-year period, the AOD values have an excellent agreement at six wavelengths (368, 415, 500, 610, 665, and 860 nm) with varying slopes (ranging from 0.95763 to 1.04089), intercepts (ranging from 0.0219 to 0.03945), correlation coefficients (R) (ranging from 0.82005 to 0.96155), and root mean square errors (RMSE) (ranging from 0.02639 to 0.03663). The correlations of both monthly and hourly averaged AOD measurements are highly consistent for each band. Specifically, the shorter (with larger AOD values) the wavelength is, the better the correlation is. Also, the results show that the peaks of relative errors generally occur in summer each year, and at noon each day. Our analyses suggest that AOD products derived from UVMRP are accurate and can serve as an alternative ground-based validation source for satellite AOD measurements.

This study compares the aerosol optical depth (AOD) at 0.55 um derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the National Aeronautics and Space Administration (NASA) Terra satellite with the Level 2.0 AOD (Quality Assured) from the Aerosol Robotic Network (AERONET) at four different locations over East China, including Hefei, Shouxian, Taihu, and Hangzhou-ZFU. The evaluation results indicate that most MODIS data from all sites fall into the expected error ranges (± 0.05 ± 0.15), with over a 66% probability that the NASA design requirements have been met. The Taihu station is an exception, accounting for only 41% of expected errors due to its lake area location and tendency to underestimate surface reflectance, thereby increasing the AOD values. Overall, the MODIS data show a good consistency and thus, are applicable for this analysis over the study area. The MODIS/Terra derived AOD at 0.55 um from 2000 to 2012 are used to analyze the spatio-temporal variation of AOD in East China. Results indicate that AODs are significantly affected by the topographic distribution. The AODs are relativity low over mountainous areas and high over plains and basins. Human activities also have a certain impact on the distribution of AOD. In addition, AODs exhibit clear seasonal variations; generally high in spring and summer, but low in autumn and winter. Combined with Angstrom exponent, aerosol particles are mainly coarse in spring and winter, but fine during summer and autumn.

This study mainly focuses on the validation of total column (TC) ozone data derived from the Ozone Mapping and Profiler Suite (OMPS) on board the NASA's Suomi National Polar-orbiting Partnership satellite (NPP). OMPS is an advanced suite of three hyperspectral instruments that maps global ozone on a daily basis and extends the more than 30 yr total ozone and ozone profile records. The algorithm used to derive OMPS TC ozone is adapted from the heritage of Total Ozone Mapping Spectrometer (TOMS) Version 7 algorithm but with a number of enhancements. Validation is primarily performed through comparisons with an ensemble of 74 global distributed Brewer and Dobson spectrophotometers measurements. Linear regression performs fair agreement between OMPS TC ozone and ground-based TC ozone measurements with root mean square error (RMSE) around of 3% (10 DU). Comparison shows that OMPS TC ozone estimates 0.21% higher than Brewer measurements average, with station-to-station standard deviation of 3.14%. As comparing with Dobson measurements, OMPS TC ozone averages 0.86% higher than the station average with standard deviation of 3.05%. The relative differences between OMPS and ground TC ozone were analysed varying with latitude and time as well as viewing geometry respectively. Comparisons show relative differences within 2% over most of latitude and viewing conditions. Only comparing with Brewer measurements did it show an OMPS TC ozone dependent error, large negative bias was observed as OMPS TC ozone below 220 DU and positive bias shown above 460 DU.

Carbon dioxide (CO2) is one of major green house gases affecting global climate. Biomass burning caused by fire is an important emission source of CO2 in the atmosphere. CO2 concentration retrieved from Atmospheric Infrared Sounder (AIRS) and fire pixel counts (FPC) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2003 to 2010 over China were obtained and analyzed. The characteristics of correlation between CO and FPC were analyzed in time series. To investigate the spatial characteristics of correlation between CO2 and fire, energy fires emitted based on the Global Fire Emissions Database v3 (GFED3) was used. CO2 concentration was steadily increased in both daytime and nighttime. The seasonal distribution of CO2 concentration and FPC had the similar pattern as the highest value appeared in Spring and lowest value in Autumn. What’s more, the changes of the aggregated CO2 concentration had a good agreement with the changes of the total FPC. However, the concentration of CO2 emitted from fires was low except Heilongjiang province. And the tempo-spatial characteristic of CO2 and FPC were similar with each other. It was different with characteristic of correlation between CO2 and FPC in whole country.

Water vapor represents a small but environmentally significant constituent of the atmosphere. This study retrieved columnar water vapor (CWV) with the 939.3 nm band of a Multi-filter Rotating Shadowband Radiometer (MFRSR) using the modified Langley technique from September 23, 2004 to June 20, 2005 at the XiangHe site.To improve the credibility, the MFRSR results were compared with those obtained from the AERONET (AErosol RObotic NETwork) CIMEL sun-photometer measurements, co-located at the XiangHe site, and the Moderate Resolution Imaging Spectroradiometer (MODIS) Near-Infrared Total Precipitable Water Product (MOD05), respectively. These comparisons show a good agreement in terms of correlation coefficients, slopes, and offsets, revealing that the accuracy of CWV estimation using the MFRSR instrument is reliable and suitable for extended studies in northern China.

Plants cover more than 70% of our earth's land surface and play an important role in the exchange of materials and energy between land and atmosphere. The biochemical substances existing in leaves have been proven to be critical factors in influencing, or even controlling, this exchange by means of various physiological processes, including photosynthesis, evaportranspiration, litter decomposition, etc. Compared to traditional wet chemistry methods, estimation of their contents through leaf and canopy reflectance spectra has become a rapid, efficient, and promising method over the past few decades, which had been physically based on the selective absorption features of a particular substance. This paper discusses the responses of plant biochemical substances to the reflectance spectra at both leaf and canopy scales. We use a leaf optical model PROSPECT and a leaf-canopy coupled optical model PROSAIL to generate large amounts of leaf and canopy spectra. Both of these models contain three biochemical parameters: cab-chlorophyll concentration, Cw-equivalent leaf water thickness, and Cm-dry matter concentration. Local and global sensitivity analysis (SA) methods are used on the simulated spectra to differentiate their contributions to the outcome spectra at both scales so as to investigate their scale effects. Results show that the maximum sensitivities of chlorophyll and water are much higher than that of dry matter at leaf scale, and they tend to decrease at canopy scale because canopy shape, soil, and incident/reflected geometric factors have a large influence on canopy spectra. However, the sensitivity of dry matter exceeds the maximum sensitivity of mesophyll structure parameters at canopy scale and becomes the top contributor at the beginning of near infrared.

Leaf biochemical concentrations influence both leaf and canopy spectra at their absorption bands. Leaf scale model PROSPECT, canopy scale model SAIL and their coupled model PROSAIL are used to generate simulated reflectance an transmittance spectra to study the influence of biochemical concentrations. Three biochemical input variables of PROSPECT model, concentration of chlorophyll, leaf water and dry matters, are studied in this paper. Figures show that their influences on both leaf reflectance spectra and canopy reflectance spectra are similar. The influence of chlorophyll is located at visible region, leaf water at near infrared region especially centered at 1400 nm and 1900 nm, and dry matters at near infrared region especially at some reflectance peaks. Reflectance at canopy scale is generally less than that at leaf scale. Other non-biochemical factors also influence the spectra at both scales and some of them may cover biochemical signals in canopy reflectance spectra to some extent.