Evaluation of hyperspectral indices for retrieval of canopy equivalent water thickness and gravimetric water content

There are two main parameters describing the amount of water in vegetation: the gravimetric water content (GWC) and the equivalent water thickness (EWT). In this study, we investigated the applicability of hyperspectral water-sensitive indices from canopy spectra for estimating canopy EWT (CEWT) and GWC. First, the spectral reflectance’s response to different levels of canopy water content was analysed and a noticeable increase in the slope of the near-infrared (NIR) shoulder of the canopy spectrum was observed. Next, the correlation between the CEWT and various hyperspectral water-sensitive indices was investigated. It was found that all of the indices could retrieve the CEWT of winter wheat well, with the coefficients of determination (R²) all being higher than 0.80. Finally, the retrieval performance of these indices for canopy GWC was evaluated and no significant correlation was observed between canopy GWC and the water-sensitive indices except for the spectral ratio index in the NIR shoulder region (NSRI). These results showed that the traditional water-sensitive vegetation indices are more sensitive to CEWT than to GWC, especially when the LAI is not highly correlated with the GWC, and that the NSRI is a potential vegetation index for use in the retrieval of GWC.     

Water content estimation in vegetation with MODIS reflectance data and model inversion methods

Statistical and radiative-transfer physically based studies have previously demonstrated the relationship between leaf water content and leaf-level reflectance in the near-infrared spectral region. The successful scaling up of such methods to the canopy level requires modeling the effect of canopy structure and viewing geometry on reflectance bands and optical indices used for estimation of water content, such as normalized difference water index (NDWI), simple ratio water index (SRWI) and plant water index (PWI). This study conducts a radiative transfer simulation, linking leaf and canopy models, to study the effects of leaf structure, dry matter content, leaf area index (LAI), and the viewing geometry, on the estimation of leaf equivalent water thickness from canopy-level reflectance. The applicability of radiative transfer model inversion methods to MODIS is studied, investigating its spectral capability for water content estimation. A modeling study is conducted, simulating leaf and canopy MODIS-equivalent synthetic spectra with random input variables to test different inversion assumptions. A field sampling campaign to assess the investigated simulation methods was undertaken for analysis of leaf water content from leaf samples in 10 study sites of chaparral vegetation in California, USA, between March and September 2000. MODIS reflectance data were processed from the same period for equivalent water thickness estimation by model inversion linking the PROSPECT leaf model and SAILH canopy reflectance model. MODIS reflectance data, viewing geometry values, and LAI were used as inputs in the model inversion for estimation of leaf equivalent water thickness, dry matter, and leaf structure. Results showed good correlation between the time series of MODIS-estimated equivalent water thickness and ground measured leaf fuel moisture (LFM) content (r²=0.7), demonstrating that these inversion methods could potentially be used for global monitoring of leaf water content in vegetation.     

Detection of leaf and canopy EWT by calculating REWT from reflectance spectra

The spectral characteristics of and the interaction between leaves and light were analysed based on the optical absorption coefficients of foliar water and biochemical components. The equations for calculating the radiative-equivalent water thickness (REWT) of leaves and canopy were presented based on the difference in reflectance at 945 and 975 nm. Because of the direct reflection on leaf surface and the leaf internal scattering, the REWT derived from the Beer–Lambert principle was different from the leaf or canopy equivalent water thickness (EWT). Two independent datasets at canopy or leaf scales were designed to calibrate and validate the relationships between EWT and REWT. The results show that (1) the leaf or canopy REWT can be calculated from the reflectance difference between 945 and 975 nm; (2) the leaf REWT was 3.3 times larger than the EWT with a significant determination coefficient (R ²) of 0.80 for our dataset and 0.86 for the Leaf Optical Properties Experiment (LOPEX'93) dataset; (3) the canopy REWT was 1.4 times larger than the EWT with a significant R ² of 0.56 for the winter wheat canopy spectral dataset in 2002, and 0.61 for the 2004 dataset. Therefore, the leaf or canopy EWT can be detected by calculating REWT from the difference in reflectance at 945 and 975 nm. Furthermore, because the relationship between REWT and EWT reflected the interaction of light with leaves or canopy, the multiple scattering optical pathlength in the near-infrared (NIR) bands can also be calculated by the ratio of REWT to EWT.     

A MODIS-based perpendicular moisture index to retrieve leaf moisture content of forest canopies

Moisture dictates vegetation susceptibility to fire ignition and propagation. Various spectral indices have been proposed for the estimation of equivalent water thickness (EWT), which is defined as the mass of liquid water per unit of leaf surface. However, fire models use live fuel moisture content (LFMC) as a measure of vegetation moisture. LFMC is defined as the ratio of the mass of the liquid water in a leaf over the mass of dry matter, and traditional spectral indices are not as effective as with EWT in capturing LFMC variability. The aim of this research was to explore the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra and Aqua satellites in retrieving LFMC from top of the canopy reflectance, and to develop a new spectral index sensitive to this parameter. All the analyses were based on synthetic canopy spectra constructed by coupling the PROSPECT (leaf optical properties model) and SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer models. Simulated top of the canopy spectra were then convolved to MODIS ‘land’ channels 1–7 spectral response functions. All band pairs were evaluated to determine the subspace of MODIS measurements where the separability of points based on their value of LFMC was the highest. This led to the identification of isolines of LFMC in the plane defined by MODIS reflectance measurements in channels 2 and 5; the isolines are straight and parallel, and ordered from lower to higher values of LFMC. This observation allowed the construction of a novel spectral index that is directly related to LFMC – the perpendicular moisture index (PMI). This index measures the distance of a point in the plane spanned by reflectance measurements in MODIS channels 2 and 5 from a reference line, that of completely dry vegetation. Validation against simulated data showed that PMI exhibits a linear relationship with LFMC. When the vegetation cover is dense, the LFMC explains most of the variability in the PMI (R² = 0.70 when LAI > 2; R² = 0.87 when LAI > 4). When the LAI is lower, the contribution of soil background to the measured reflectance increases, and the index underestimates LFMC. The PMI was also validated against the LOPEX93 (Leaf Optical Properties Experiment 1993) data set of leaf optical and biophysical measurements, scaled to canopy reflectance with SAIL, showing acceptable results (R² = 0.56 when LAI > 2; R² = 0.63 when LAI > 4).     

Towards the Remote Sensing of Matorral Vegetation Physiology: Relationships between Spectral Reflectance, Pigment, and Biophysical Characteristics of Semiarid Bushland Canopies

This study investigates the relationships between the spectral reflectance characteristics and the concentrations of photosynthetic pigments and biophysical attributes of a structurally complex, spatially heterogeneous vegetation canopy with varying background properties. A field experiment was performed in the Guadalentin basin, Spain using matorral vegetation canopies dominated by Rosmarinus officinalis, Cistus albidus, and Anthyllis cytosoides. A spectroradiometer was used to record the reflectance of a series of sites at which measurements were made of the concentrations per unit ground area and per unit leaf mass of chlorophyll a and b and the carotenoids, together with leaf area index and percent canopy cover. A range of spectral characteristics was examined which have been found previously to be related to pigment concentrations and biophysical properties of vegetation. For matorral vegetation many of these spectral characteristics were unrelated or only weakly related to canopy properties. However, it was found that pigment concentrations per unit ground area were related to ratios of reflectance in narrow spectral bands within the near-infrared region, ratios of bands within the red region, and characteristics of the amplitude of first derivative spectra in the red edge region. Pigment concentrations per unit leaf mass were correlated with ratios of bands around the nearinfrared “shoulder” and the amplitude of the first derivative in certain visible wavelengths. LAI and percent cover were related to ratios of reflectance in narrow bands on the near-infrared plateau and red edge features of canopy reflectance spectra, as well as with the amplitude of the first derivative in the red edge and visible regions respectively.     

MODIS spectral signals at a flux tower site: Relationships with high-resolution data, and CO2 flux and light use efficiency measurements

In this study, spectral indices were calculated from single date HyMap (3 m; 126 bands), Hyperion (30 m; 242 bands), ASTER (15/30 m; 9 bands), and a time series of MODIS nadir BRDF-adjusted reflectance (NBAR; 1 km, 7 bands) for a study area surrounding the Tumbarumba flux tower site in eastern Australia. The study involved: a) the calculation of a range of physiologically-based vegetation indices from ASTER, HyMap, Hyperion and MOD43B NBAR imagery over the flux tower site; b) comparison across scales between HyMap, Hyperion and MODIS for the normalized difference water index (NDWI) and the Red-Green ratio; c) analysis of relationships between tower-based flux and light use efficiency (LUE) measurements and seasonal and climatic constraints on growth; and d) examination of relationships between fluxes, LUE and time series of NDVI, NDWI and Red-Green ratio. Strong seasonal patterns of variation were observed in NDWI and Red/Green ratio from MODIS NBAR. Correlations between fine (3 and 30 m) and coarse (1 km) scale indices for a small region around the flux tower site were moderately good for Red/Green ratio, but poor for NDWI. Hymap NDWI values for the understorey canopy were much lower than values for the tree canopy. MODIS NDWI was negatively correlated with CO₂ fluxes during warm and cool seasons. The correlation indicated that surface reflectance, affected by a spectrally bright grassland understorey canopy, was decoupled from growth of trees with access to deep soil moisture. The application of physiologically-based indices at earth observation scale requires careful attention to applicability of band configurations, contribution of vegetation components to reflectance signals, mechanistic relationships between biochemical processes and spectral indexes, and incorporation of ancillary information into any analysis.     

A dataset for the validation of reflectance models

Three mature stands at the forest test site Järvselja, Estonia were extensively measured for using as a validation dataset for heterogeneous canopy reflectance models. In order to enable the reconstruction of the 3-D architecture of these 100 × 100 m² test plots, individual tree positions and crown dimensions were inventoried. In addition, leaf, needle, stem bark and branch bark visible and near-infrared (VNIR) reflectance spectra, and VNIR reflectance spectra of ground vegetation were measured. This in situ dataset is supported by atmospherically and radiometrically corrected Mode 3 CHRIS reflectance spectra for three view directions, and top of canopy VNIR nadir spectra from airborne measurements. Details of measurements, instruments in use, data processing, and access to data are described in a technical report which is available on-line.     

Soil adjusted vegetation water content retrievals in grasslands

Soil contamination of canopy reflectance over grasslands can cause errors in empirical vegetation water content (VWC) retrievals using the NDII (Normalized Difference Infrared Index, [ρ_0.86?ρ_1.64]/[ρ_0.86+ρ_1.64]). Minimization of soil contamination by NDII relies on the existence of a quasi straight soil line and quasi straight VWC isolines (lines of equal VWC) in the 1.64–0.86 µm reflectance space. Further the VWC isolines are expected to meet at the origin of the 1.64–0.86 µm reflectance space. Considering soil moisture as the primary determinant of soil reflectance variation at a given location, this study investigates the effect of soil moisture on the nature of soil lines and VWC isolines under grassland conditions. Reflectance simulations from coupled soil‐leaf‐canopy reflectance models under grassland conditions show that soil lines and VWC isolines are expected to be curved and may not converge at the origin. This behaviour is attributed to disproportionate soil moisture related absorption processes operating at 1.64 µm and 0.86 µm. A new technique that accounts for these inconsistencies in NDII assumptions is proposed for VWC retrievals. The technique consists of using separate regression relationships between VWC and a Soil Adjusted NDII (SANDII) based on the volumetric soil moisture category of the background. SANDII, based on the idea borrowed from the Soil Adjusted Vegetation Index (SAVI) is an origin shifted transformation of NDII. The optimum origin that reduces VWC retrieval errors is shown to be soil moisture category specific. The proposed technique requires categorical soil moisture information in order to decide which regression relationship to apply for VWC retrievals. Climatology, meteorological models or microwave observations are expected to be reliable resources for such categorical soil moisture information. Evaluations of the proposed technique using simulated reflectances showed that absolute errors in VWC retrievals were reduced by an average 20% as compared to the traditional NDII regression method. Such improvements are expected to be significant for fire‐risk applications. Finally supporting evidence for the need of an origin translated NDII is provided using data collected over pastures during the Soil Moisture Experiment 2003 (SMEX03) field campaign.     

Study of vegetation spectral anomaly behaviour in a porphyry copper mine area based on hyperspectral indices

ABSTRACT

Hyperspectral remote sensing is economical and fast, and it can reveal detailed spectral information of plants. Hence, hyperspectral data are used in this study to analyse the spectral anomaly behaviours of vegetation in porphyry copper mine areas. This analytical method is used to compare the leaf spectra and relative differences among the vegetation indices; then, the correlation coefficients were computed between the soil copper content and vegetation index of Quercus spinosa leaves at both the leaf scale and the canopy scale in the Chundu mine area with different geological backgrounds. Lastly, this study adopts hyperspectral data for the level slicing of vegetation anomalies in the Chundu mine area. The results showed that leaf spectra in the orebody and background area differed greatly, especially in the infrared band (750 nm – 1300 nm); moreover, some indices like the normalized water index (NWI) and normalized difference water index (NDWI) of Quercus spinosa and Lamellosa leaves are sensitive to changes in the geological background. Compared with the canopy, the leaf hyperspectral indices of Quercus spinosa in Chundu can better reflect soil cuprum (Cu) anomaly. In addition, the NWI and NDWI of Quercus spinosa are significantly correlated with the soil Cu content at both the canopy scale and the leaf scale. Consequently, the results of the vegetation anomaly level slicing can adequately reflect the plant anomalies from ore bodies and nearby areas, thereby providing a new ore-finding method for areas with a high degree of vegetation coverage.     

Evaluating the performance of PC-ANN for the estimation of rice nitrogen concentration from canopy hyperspectral reflectance

In this study, a wide range of leaf nitrogen concentration levels was established in field-grown rice with the application of three fertilizer levels. Hyperspectral reflectance data of the rice canopy through rice whole growth stages were acquired over the 350 nm to 2500 nm range. Comparisons of prediction power of two statistical methods (linear regression technique (LR) and artificial neural network (ANN)), for rice N estimation (nitrogen concentration, mg nitrogen g^?1 leaf dry weight) were performed using two different input variables (nitrogen sensitive hyperspectral reflectance and principal component scores). The results indicted very good agreement between the observed and the predicted N with all model methods, which was especially true for the PC-ANN model (artificial neural network based on principal component scores), with an RMSE?=?0.347 and REP?=?13.14%. Compared to the LR algorithm, the ANN increased accuracy by lowering the RMSE by 17.6% and 25.8% for models based on spectral reflectance and PCs, respectively.     

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76-0.90 μm wavelength) and band 5 (1.55-1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R² of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation.     

Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery

Microwave-based remote sensing algorithms for mapping soil moisture are sensitive to water contained in surface vegetation at moderate levels of canopy cover. Correction schemes require spatially distributed estimates of vegetation water content at scales comparable to that of the microwave sensor footprint (10¹ to 10⁴ m). This study compares the relative utility of high-resolution (1.5 m) aircraft and coarser-resolution (30 m) Landsat imagery in upscaling an extensive set of ground-based measurements of canopy biophysical properties collected during the Soil Moisture Experiment of 2002 (SMEX02) within the Walnut Creek Watershed. The upscaling was accomplished using expolinear relationships developed between spectral vegetation indices and measurements of leaf area index, canopy height, and vegetation water content. Of the various indices examined, a Normalized Difference Water Index (NDWI), derived from near- and shortwave-infrared reflectances, was found to be least susceptible to saturation at high levels of leaf area index. With the aircraft data set, which did not include a short-wave infrared water absorption band, the Optimized Soil Adjusted Vegetation Index (OSAVI) yielded best correlations with observations and highest saturation levels. At the observation scale (10 m), LAI was retrieved from both NDWI and OSAVI imagery with an accuracy of 0.6, vegetation water content at 0.7 kg m⁻², and canopy height to within 0.2 m. Both indices were used to estimate field-scale mean canopy properties and variability for each of the intensive soil-moisture-sampling sites within the watershed study area. Results regarding scale invariance over the SMEX02 study area in transformations from band reflectance and vegetation indices to canopy biophysical properties are also presented.     

Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery

Hyperspectral remote sensing has great potential for accurate retrieval of forest biochemical parameters. In this paper, a hyperspectral remote sensing algorithm is developed to retrieve total leaf chlorophyll content for both open spruce and closed forests, and tested for open forest canopies. Ten black spruce (Picea mariana (Mill.)) stands near Sudbury, Ontario, Canada, were selected as study sites, where extensive field and laboratory measurements were carried out to collect forest structural parameters, needle and forest background optical properties, and needle biophysical parameters and biochemical contents chlorophyll a and b. Airborne hyperspectral remote sensing imagery was acquired, within one week of ground measurements, by the Compact Airborne Spectrographic Imager (CASI) in a hyperspectral mode, with 72 bands and half bandwidth 4.25-4.36 nm in the visible and near-infrared region and a 2 m spatial resolution. The geometrical-optical model 4-Scale and the modified leaf optical model PROSPECT were combined to estimate leaf chlorophyll content from the CASI imagery. Forest canopy reflectance was first estimated with the measured leaf reflectance and transmittance spectra, forest background reflectance, CASI acquisition parameters, and a set of stand parameters as inputs to 4-Scale. The estimated canopy reflectance agrees well with the CASI measured reflectance in the chlorophyll absorption sensitive regions, with discrepancies of 0.06%-1.07% and 0.36%-1.63%, respectively, in the average reflectances of the red and red-edge region. A look-up-table approach was developed to provide the probabilities of viewing the sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up tables, the 4-Scale model was inverted to estimate leaf reflectance spectra from hyperspectral remote sensing imagery. Good agreements were obtained between the inverted and measured leaf reflectance spectra across the visible and near-infrared region, with R² = 0.89 to R² = 0.97 and discrepancies of 0.02%-3.63% and 0.24%-7.88% in the average red and red-edge reflectances, respectively. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified PROSPECT inversion model, with R² = 0.47, RMSE = 4.34 μg/cm², and jackknifed RMSE of 5.69 μg/cm² for needle chlorophyll content ranging from 24.9 μg/cm² to 37.6 μg/cm². The estimates were also assessed at leaf and canopy scales using chlorophyll spectral indices TCARI/OSAVI and MTCI. An empirical relationship of simple ratio derived from the CASI imagery to the ground-measured leaf area index was developed (R² = 0.88) to map leaf area index. Canopy chlorophyll content per unit ground surface area was then estimated, based on the spatial distributions of leaf chlorophyll content per unit leaf area and the leaf area index.     

LANDSAT Thematic Mapper bands for characterizing fescue grass vegetation

The relationships between reflectance in the LANDSAT Thematic Mapper (TM) bands and grass canopy characteristics were studied. Data were collected from a total of 107 tall fescue (Festuca arundinacia) plots (0·2 m²) during the 1983 growing season. Canopy height, percentage cover, total wet biomass, total dry biomass, plant water and leaf area index were correlated with spectral data obtained from a Barnes Modular Multiband Radiometer. The near-infrared wavelength region corresponding to TM band 4 appeared to be the best estimator of total wet biomass (r = 0·80) and canopy height (r =0·82). Percentage canopy cover had the highest correlation coefficients with TM band 7 (r = ?0·95) and TM bands 1, 2 and 3 (r≥0·93). All canopy variables showed a curvilinear relationship with the spectral bands, except canopy cover, which showed a near linear relationship, for the biomass range in this study. Linear transformations were obtained using natural logarithms of the grass canopy variables and the spectral bands. Band ratios were more significant than individual bands when correlated with the canopy variables. The relationship between the normalized difference transformation and total wet biomass was linear for low biomass situations. The normalized difference values were constant for high biomass levels. Redundancy was found in several of the canopy variables and several of the spectral variables. Principal component transformations were effective in reducing the seven spectral bands to two principal components, while maintaining nearly all of the variance of the seven bands.     

NDWI与NDVI指数在区域干旱监测中的比较分析——以2003年江西夏季干旱为例

植被水分指数NDWI是基于短波红外（SWIR）与近红外（NIR）的归一化比值指数。与NDVI相比，它能有效地提取植被冠层的水分含量；在植被冠层受水分胁迫时，NDWI指数能及时地响应，这对于旱情监测具有重要意义。结合2003年夏季MODIS影像数据和地面气象数据，以江西省内一片农田和一片林地为试验区域，分析比较了NDWI与NDVI距平值在短期旱情监测中的有效性。监测结果表明， NDWI对植被冠层水分信息比NDVI更为敏感；在短期干旱监测中，NDWI指数能准确地反映旱情的时空变化。     

The Pasterze Glacier,Austria

The remote sensing of foliar biochemical concentration assumes that leaf biochemical absorption features will be manifest in canopy reflectance. This is a reasonable assumption providing the effect of a given change in foliar biochemical concentration has a similar effect on both leaf and canopy reflectance. A comparison between canopy and leaf reflectance was made to determine if canopy effects (composite of leaf area index, biomass, structure, multiple scattering and shadow) could alter the leaf biochemical information in canopy reflectance spectra. Differences in leaf biochemical concentrations and leaf biomass were induced by the application of fertilisers to large plots of slash pine (Pinus elliottii var elliottii) in Florida, U.S.A. The reflectance of plot canopies was measured using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The reflectance of samples of leaves drawn from each plot were measured using a laboratory spectrometer. The differences between airborne and laboratory reflectance ratios (fertilised/control spectra) were used to isolate the effects of the canopy in AVIRIS reflectance spectra. From this study it was concluded that the canopy influenced leaf reflectance substantially at wavelengths beyond the water absorption feature at 1400nrn and leaf biochemical information was transmitted virtually unchanged from the leaf to the canopy in near-infrared wavelengths.     

Spectral estimation of absorbed photosynthetically active radiation in corn canopies

Most models of crop growth and yield require an estimate of canopy leaf area index or absorption of radiation; however, direct measurement of LAI or light absorption can be tedious and time-consuming. The object of this study was to develop relationships between photosynthetically active radiation (PAR) absorbed by corn (Zea mays L.) canopies and the spectral reflectance of the canopies. Absorption of PAR was measured near solar noon in corn canopies planted in north-south rows at densities of 50,000 and 100,000 plants ha.^?1 Reflectance factor data were acquired with a radiometer with spectral bands similar to the Landsat MSS. Three spectral vegetation indices (ratio of near infrared to red reflectance, normalized difference, and greenness) were associated with more than 95% of the variability in absorbed PAR from planting to silking. The relationships developed between absorbed PAR and the three indices were tested with reflectance factor data acquired from corn canopies planted in 1979–1982 that excluded those canopies from which the equations were developed. Treatments included in these data were two hybrids, four planting densities (25, 50, 75, and 100 thousand plantsha^?1), three soil types (Typic Argiaquoll, Udollic Ochraqualf, and Aeric Ochraqualf), and several planting dates. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50% of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR, estimated indirectly from the multispectral reflectance of the canopies, were associated with up to 73% of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index.     

全球典型植被叶片光谱特征及其对LAI反演的影响分析

在全球范围长时间序列LAI遥感产品反演算法中,植被冠层反射率模型仅使用少量叶片光谱特征代表全球植被全年的典型植被光谱特征,叶片光谱的不确定性导致LAI遥感产品存在一定的误差。目前全球已经构建了多个典型植被叶片波谱数据集,这些数据集包含多个植被物种、不同空间地域及多时相叶片光谱数据,为定量分析叶片光谱特征提供了数据支持。主要利用LOPEX’93、ANGERS’03、中国典型地物波谱数据库和野外实测的叶片光谱数据,以黄边参数、红边参数和叶片光谱指数作为分析指标,探讨不同植被物种、不同气候区和不同物候期的叶片光谱特征差异,及其对植被冠层反射率、LAI反演的影响,为发展考虑现实叶片光谱差异的LAI反演算法提供研究基础。结果表明:植被叶片光谱存在多样性,叶片光谱特征差异主要影响MODIS传感器近红外波段和绿波段反射率值,其中,绿波段反射率值对叶片光谱变化最为敏感;在LAI反演算法中,如果只考虑植被类型而不考虑物种叶片光谱差异,可能会给LAI反演带来大于3的误差。     

Global sensitivity analysis of PROSAIL model parameters when simulating Moso bamboo forest canopy reflectance

PROSAIL is a combination of the leaf optical properties spectra (PROSPECT) model and the scattering by arbitrarily inclined leaves (SAIL) canopy bidirectional reflectance model. When modelling forest canopy reflectance using the PROSAIL radiative transfer model, the sensitivities of parameters can affect the modelling accuracy. Traditionally, sensitivities have been assessed using local sensitivity analysis (LSA); however, drawbacks to this approach include a lack of consideration for coupled effects between different parameters. In this study, parameter sensitivities in the PROSAIL model were calculated using two global sensitivity analysis (GSA) methods (the Extended Fourier Amplitude Sensitivity Test (EFAST) method and the Morris method), field measurements, and Landsat 5 Thematic Mapper (TM) data for a Moso bamboo forest. The results of GSA were compared with those of LSA in order to identify the key parameters impacting the Moso bamboo forest canopy reflectance, and to provide a reference for model optimization and vegetation canopy inversion improvement. The results showed that: (1) the sensitivities of six major input parameters of the PROSAIL model were generally consistent with the sorting orders of the two GSA methods, but were not in accordance with those from the LSA method, especially in the mid-infrared band; (2) coupled effects among parameters acting on reflectance simulation in visible light bands were greater than those in infrared bands; (3) the simulated canopy reflectance was evaluated using Landsat 5 TM data, and the results simulated based on LSA analysis showed higher error than those based on GSA analysis, because the LSA method ignored the influence of some parameters on canopy reflectance, e.g. leaf mesophyll structure (N), average leaf angle (ALA), leaf water content (C_w), and leaf dry matter content (C_m). However, GSA was able to fully consider the coupled effects among parameters, and thus identified the sensitive parameters impacting on reflectance more accurately.