Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

The research evaluated the information content of spectral reflectance (laboratory and airborne data) for the estimation of needle chlorophyll (C_AB) and nitrogen (C_N) concentration in Norway spruce (Picea abies L. Karst.) needles. To identify reliable predictive models different types of spectral transformations were systematically compared regarding the accuracy of prediction. The results of the cross-validated analysis showed that C_AB can be well estimated from laboratory and canopy reflectance data. The best predictive model to estimate C_AB was achieved from laboratory spectra using continuum-removal transformed data (R²cv = 0.83 and a relative RMSEcv of 8.1%, n = 78) and from hyperspectral HyMap data using band-depth normalised spectra (R²cv = 0.90, relative RMSEcv = 2.8%, n = 13). Concerning the nitrogen concentration, we observed somewhat weaker relations, with however still acceptable accuracies (at canopy level: R²cv = 0.57, relative RMSEcv = 4.6%). The wavebands selected in the regression models to estimate C_AB were typically located in the red edge region and near the green reflectance peak. For C_N, additional wavebands related to a known protein absorption feature at 2350 nm were selected. The portion of selected wavebands attributable to known absorption features strongly depends on the type of spectral transformation applied. A method called “water removal” (WR) produced for canopy spectra the largest percentage of wavebands directly or indirectly related to known absorption features. The derived chlorophyll and nitrogen maps may support the detection and the monitoring of environmental stressors and are also important inputs to many bio-geochemical process models.     

我国东部某地油气微渗漏的遥感研究

我国东部某地一些油气田区的遥感图像(本文为国土卫星彩红外片)上,常见有一些灰白色、浅褐色云雾状色调异常,经过地植物、土壤的地面光谱测试和土壤气体化探检查,发现色调；异常范围内、外植物和土壤光谱存在一定差异,异常内还发现有烃类气体异常,这种色调异常极有可能与油气微渗漏有关。对这种遥感图像上的色调异常进行的计算机增强处理研究表明,正规化与频谱变量、时间变量标准化交换组合处理效果较好,处理后的图像上色调异常显示十分明显。据永安地区国土卫星彩红外片正规化、标准化组合处理图像上显示的色调异常与已知油气田十分吻合,说明该处理方法是有效的。     

Potential of Detecting the Sulfur Dioxide Stress on Landscape Plants in Spectral Reflectance Data

Sulfur dioxide (SO₂) exhibits a powerful implication on the air condition and responsible for increasing the acidity of rainfall which plays negative effects on plant growth. It is a big problem to quantitatively access the stress degrees of sulfur dioxide on landscape plants. This study aims to find a non-destructive way to detect the degrees of SO₂ stress by using the spectral reflectance data. Five different landscape plants were selected and a simulated SO₂ stress environment by using fumigation box was built in this experiment. Landscape plants were grown on at this simulated SO₂ environment, and the leaf reflectance, chlorophyll and sulfur concentration were measured at 0, 2, 4, 6, 8, 10 and 12 h respectively. The spectral, chlorophyll response of five different plants were examined and the red edge position (REP) shift obtained from the reflectance were used to evaluate the SO₂ stress degrees at this paper. The results showed leaf chlorophyll content generally decreased and leaf sulfur content generally increased of all of these five landscape plants as though the chlorophyll and sulfur content disturbing during the whole stress time. However, compared with the sulfur content changed in leaves, chlorophyll content did not significantly changed when suffering from SO₂. The shift of REP performed well to indicate the severity of SO₂ fumigation stress and different species showed the different REP shift. The determined coefficient R² of REP shift and the relative changed sulfur content in leaves can up to 0.85. And the results also indicated that the different species maintained different resistance to SO₂.     

江汉油田烃类微渗漏蚀变信息及提取机理

本文围绕烃类微渗漏造成地表土壤蚀变的褪红、粘土矿化和碳酸盐化等3项标志,在江汉油田进行了相关的地球化学信息和光谱信息研究。在研究土壤成分特征的基础上,提出了反映3项蚀变标志的成分因子;在研究土壤光谱特征的基础上,提出了与蚀变成分因子相关的TM波段比值因子;在成分因子及TM波段比值因子的统计分析基础上,探索从室内土壤光谱数据中提取和烃类微渗漏蚀变相关的信息,并为图像特征信息提取奠定了基础。     

Spectral indicators of forage quality in West Africa’s tropical savannas

Forage is among the essential ecosystem services provided by tropical savannas. Expected changes in climate and land use may cause a strong decline in herbaceous forage provision and thus make it advisable to monitor its dynamics. Spectroscopy offers promising tools for fast and non-destructive estimations of forage variables, yet suffers from unfavourable measurement conditions during the tropical growing period such as frequent cloud cover and high humidity. This study aims to test whether spatio-temporal information on the quality (metabolisable energy content, ME) and quantity (green biomass, BM) of West African forage resources can be correlated to in situ measured reflectance data. We could establish robust and independent models via partial least squares regression, when spectra were preprocessed using second derivative transformation (ME: max. adjusted R² in validation (_adjR²_VAL) = 0.83, min. normalised root mean square error (nRMSE) = 7.3%; BM: max. _adjR²_VAL = 0.75, min. nRMSE = 9.4%). Reflectance data with a reduced spectral range (350–1075 nm) still rendered satisfactory accuracy.Our results confirm that a strong correlation between forage characteristics and reflectance of tropical savanna vegetation can be found. For the first time in field spectroscopy studies, forage quality is modelled as ME content based on 24-h in vitro gas production in the Hohenheim gas test system and crude protein concentration of BM. Established spectral models could help to monitor forage provision in space and time, which is of great importance for an adaptive livestock management.     

A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake

Soft-classification-based methods for estimating chlorophyll-a concentration (C_chla) by satellite remote sensing have shown great potential in turbid coastal and inland waters. However, one of the most important water color sensors, the MEdium Resolution Imaging Spectrometer (MERIS), has not been applied to the study of turbid or eutrophic lakes. In this study, we developed a new soft-classification-based C_chla estimation method using MERIS data for the highly turbid and eutrophic Taihu Lake. We first developed a decision tree to classify Taihu Lake into three optical water types (OWTs) using MERIS reflectance data, which were quasi-synchronous (±3 h) with in situ measured C_chla data from 91 sample stations. Secondly, we used MERIS reflectance and in situ measured C_chla data in each OWT to calibrate the optimal C_chla estimation model for each OWT. We then developed a soft-classification-based C_chla estimation method, which blends the C_chla estimation results in each OWT by a weighted average, where the weight for each MERIS spectra in each OWT is the reciprocal value of the spectral angle distance between the MERIS spectra and the centroid spectra of the OWT. Finally, the soft-classification based C_chla estimation algorithm was validated and compared with no-classification and hard-classification-based methods by the leave-one-out cross-validation (LOOCV) method. The soft-classification-based method exhibited the best performance, with a correlation coefficient (R²), average relative error (ARE), and root-mean-square error (RMSE) of 0.81, 33.8%, and 7.0 μg/L, respectively. Furthermore, the soft-classification-based method displayed smooth values at the edges of OWT boundaries, which resolved the main problem with the hard-classification-based method. The seasonal and annual variations of C_chla were computed in Taihu Lake from 2003 to 2011, and agreed with the results of previous studies, further indicating the stability of the algorithm. We therefore propose that the soft-classification-based method can be effectively used in Taihu Lake, and that it has the potential for use in other optically-similar turbid and eutrophic lakes, and using spectrally-similar satellite sensors.     

烃类微渗漏的光谱遥感信息

烃类微渗漏的间接证据,包括一系列地球物理、地球化学、岩石矿物和地貌异常以及以后认识到的在土壤化学与植物生理方面引起的变异。所有这些现象均与烃类微渗漏密切相关。根据这种地表“蚀变体”的波谱特征,可利用遥感技术在特定波段内将其信息直接提取出来。     

Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants

Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350–2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, R_v² = 0.54; root mean square error in validation, RMSE_v = 12.99 mg kg⁻¹; and residual prediction deviation in validation, RPD_v = 1.35) was slightly better than using the soil spectra (R_v² = 0.42, RMSE_v = 13.35 mg kg⁻¹, and RPD_v = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (R_v² = 0.63, RMSE_v = 11.94 mg kg⁻¹, RPD_v = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and 2290 nm, leaf spectral bands near 700, 890 and 900 nm in PLSR models were important wavelengths for soil arsenic prediction. Moreover, soil arsenic showed significantly positive correlations with soil organic matter (r = 0.62, p < 0.01) and leaf arsenic (r = 0.77, p < 0.01), and a significantly negative correlation with leaf chlorophyll (r = −0.67, p < 0.01). The results showed that the prediction of arsenic contents using soil and leaf spectra may be based on their relationships with soil organic matter and leaf chlorophyll contents, respectively. Although RPD of 1.47 was below the recommended RPD of >2 for soil analysis, arsenic prediction in agricultural soils can be improved by combining the leaf and soil spectra.     

Hyperspectral reflectance of leaves and flowers of an outbreak species discriminates season and successional stage of vegetation

Spectral reflectance can be used to assess large-scale performances of plants in the field based on plant nutrient balance as well as composition of defence compounds. However, plant chemical composition is known to vary with season – due to its phenology – and it may even depend on the succession stage of its habitat. Here we investigate (i) how spectral reflectance could be used to discriminate successional and phenological stages of Jacobaea vulgaris in both leaf and flower organs and (ii) if chemical content estimation by reflectance is flower or leaf dependent.We used J. vulgaris, which is a natural outbreak plant species on abandoned arable fields in north-western Europe and studied this species in a chronosequence representing successional development during time since abandonment. The chemical content and reflectance between 400 and 2500 nm wavelengths of flowers and leaves were measured throughout the season in fields of different successional ages. The data were analyzed with multivariate statistics for temporal discrimination and estimation of chemical contents in both leaf and flower organs.Two main effects were revealed by spectral reflectance measurements: (i) both flower and leaf spectra show successional and seasonal changes, but the pattern is complex and organ specific (ii) flower head pyrrolizidine alkaloids, which are involved in plant defence against herbivores, can be detected through hyperspectral reflectance.We conclude that spectral reflectance of both leaves and flowers can provide information on plant performance during season and successional stages. As a result, remote sensing studies of plant performance in complex field situations will benefit from considering hyperspectral reflectance of different plant organs. This approach may enable more detailed studies on the link between spectral information and plant defence dynamics both aboveground and belowground.     

冀东地区油气微渗漏地植物的遥感基础研究

本文通过已知含油区及背景区地植物及地表土壤多种微量金属元素和吸附烃的综合分析,并利用遥感资料与物探资料作复合评价,讨论了冀东油气微渗漏地区的遥感植物地球化学标志。     

Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements

Assessment of vegetation water content is critical for monitoring vegetation condition, detecting plant water stress, assessing the risk of forest fires and evaluating water status for irrigation. The main objective of this study was to investigate the performance of various mono- and multi-variate statistical methods for estimating vegetation water content (VWC) from hyper-spectral data. Hyper-spectral data is influenced by multi-collinearity because of a large number of (independent) spectral bands being modeled by a small number of (dependent) biophysical variables. Therefore, some full spectrum methods that are known to be suitable for analyzing multi-collinear data set were chosen. Canopy spectral reflectance was obtained with a GER 3700 spectro-radiometer (400–2400 nm) in a laboratory setting and VWC was measured by calculating wet/dry weight difference per unit of ground area (g/m²) of each plant canopy (n = 95). Three multivariate statistical methods were applied to estimate VWC: (1) partial least square regression, (2) artificial neural network and (3) principal component regression. They were selected to minimize the problem related to multi-collinearity. For comparison, uni-variate techniques including narrow band ratio water index (RWI), normalized difference water index (NDWI), second soil adjusted vegetation index (SAVI2) and transferred soil adjusted vegetation index (TSAVI) were applied. For each type of vegetation index, all two-band combinations were evaluated to determine the best band combination. Validation of the methods was based on the cross validation procedure and using three statistical indicators: R², RMSE and relative RMSE. The cross-validated results identified PLSR as the regression model providing the most accurate estimates of VWC among the various methods. The result revealed that this model is highly recommended for use with multi-collinear datasets ($R_{\text{CV}}^2=0.94$, RRMSE_CV = 0.23). Principal component regression exhibited the lowest accuracy among the multivariate models ($R_{\text{CV}}^2=0.78$, RRMSE_CV = 0.41).     

基于ALI数据的柴达木油气区含铁矿物制图

烃类微渗漏造成的油气藏上方红层褪色是遥感间接找油气的重要标志之一，因此，在油气遥感勘探中，含铁矿物的分布制图和铁异常信息的提取至关重要。作为新一代的多光谱图像，ALI（Advanced Land Imager）和ETM+图像相比，光谱分辨率有了很大提高，它在0.4~1.3 μm波长范围内有7个波段，可以有效地反映出不同含铁矿物在此波长范围内独特的光谱特征，可以用于含铁矿物制图和铁异常信息提取。本文选取有天然气分布的柴达木盆地东部三湖地区为研究区，对ALI图像运用光谱角度制图方法进行含铁矿物分布制图。     

Retrieval of leaf water content spanning the visible to thermal infrared spectra

The objective of this study was to investigate the entire spectra (from visible to the thermal infrared; 0.390–14.0 μm) to retrieve leaf water content in a consistent manner. Narrow-band spectral indices (calculated from all possible two band combinations) and a partial least square regression (PLSR) were used to assess the strength of each spectral region. The coefficient of determination (R²) and root mean square error (RMSE) were used to report the prediction accuracy of spectral indices and PLSR models. In the visible-near infrared and shortwave infrared (VNIR–SWIR), the most accurate spectral index yielded R² of 0.89 and RMSE of 7.60%, whereas in the mid infrared (MIR) the highest R² was 0.93 and RMSE of 5.97%. Leaf water content was poorly predicted using two-band indices developed from the thermal infrared (R² = 0.33). The most accurate PLSR model resulted from MIR reflectance spectra (R² = 0.96, RMSE = 4.74% and RMSE cross validation RMSE_CV = 6.17%) followed by VNIR–SWIR reflectance spectra (R² = 0.91, RMSE = 6.90% and RMSE_CV = 7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R² = 0.67, RMSE = 13.27% and RMSE_CV = 16.39%). This study demonstrated that the mid infrared (MIR) and shortwave infrared (SWIR) domains were the most sensitive spectral region for the retrieval of leaf water content.     

<Emphasis Type="Italic">C</Emphasis>/<Emphasis Type="Italic">N</Emphasis><Subscript>0</Subscript> estimators for high-sensitivity snapshot GNSS receivers

We address the problem of estimating the carrier-to-noise ratio (C/N₀) in weak signal conditions. There are several environments, such as forested areas, indoor buildings and urban canyons, where high-sensitivity global navigation satellite system (HS-GNSS) receivers are expected to work under these reception conditions. The acquisition of weak signals from the satellites requires the use of post-detection integration (PDI) techniques to accumulate enough energy to detect them. However, due to the attenuation suffered by these signals, estimating their C/N₀ becomes a challenge. Measurements of C/N₀ are important in many applications of HS-GNSS receivers such as the determination of a detection threshold or the mitigation of near-far problems. For this reason, different techniques have been proposed in the literature to estimate the C/N₀, but they only work properly in the high C/N₀ region where the coherent integration is enough to acquire the satellites. We derive four C/N₀ estimators that are specially designed for HS-GNSS snapshot receivers and only use the output of a PDI technique to perform the estimation. We consider four PDI techniques, namely non-coherent PDI, non-quadratic non-coherent PDI, differential PDI and truncated generalized PDI and we obtain the corresponding C/N₀ estimator for each of them. Our performance analysis shows a significant advantage of the proposed estimators with respect to other C/N₀ estimators available in the literature in terms of estimation accuracy and computational resources.     

RTM-based dynamic absorption integrals for the retrieval of biochemical vegetation traits

Information about pigment and water contents provides comprehensive insights for evaluating photosynthetic potential and activity of agricultural crops. In this study, we present the concept of using spectral integral ratios (SIR) to retrieve three biochemical traits, namely chlorophyll a and b (C_ab), carotenoids (C_cx), and water (C_w) content, simultaneously from hyperspectral measurements in the wavelength range 460−1100 nm. The SIR concept is based on automatic separation of respective absorption features through local peak and intercept analysis between log-transformed reflectance and convex hulls. The algorithm was tested on two synthetically established databases using a physiologically constrained look-up-table (LUT) generated by (i) the leaf optical properties model PROSPECT and (ii) the canopy radiative transfer model (RTM) PROSAIL. LUT constraints were realized based on natural C_cx-C_ab relations and green peak locations identified in the leaf optical database ANGERS. Linear regression between obtained SIRs and model parameters resulted in coefficients of determination (R²) of 0.66 (i and ii) for C_cx, R² = 0.85 (i) and 0.53 (ii) for C_ab, and R² = 0.97 (i) and 0.67 (ii) for C_w, respectively. Using the model established from the PROSPECT LUT, leaf level validation was carried out based on ANGERS data with reasonable results both in terms of goodness of fit and root mean square error (RMSE) (C_cx: R² = 0.86, RMSE = 2.1 μg cm⁻²; C_ab: R² = 0.67, RMSE = 12.5 μg cm^-2; C_w: R² = 0.89, RMSE = 0.007 cm). The algorithm was applied to airborne spectrometric HyMap data acquired on 12th July 2003 in Barrax, Spain and to AVIRIS-NG data recorded on 2nd July 2018 southwest of Munich, Germany. Mapping of the SIR results as multiband images (3-segment SIR) allows for intuitive visualization of dominant absorptions with respect to the three considered biochemical variables. Barrax in situ validation using linear regression models derived from PROSAIL LUT showed satisfactory results regarding C_ab (R² = 0.84; RMSE = 9.06 μg cm^-2) and canopy water content (CWC, R² = 0.70; RMSE = 0.05 cm). Retrieved C_cx values were reasonable according to C_ab-C_cx-dependence plausibility analysis. Hence, the presented SIR algorithm allows for computationally efficient and RTM supported robust retrievals of the two most important vegetation pigments as well as of water content and is ready to be applied on satellite imaging spectroscopy data available in the near future. The algorithm is publicly available as an interface supported tool within the 'Agricultural Applications' of the EnMAP-Box 3 hyperspectral remote sensing software suite.     

柑橘植株冠层氮素和光合色素含量近地遥感估测

柑橘植株营养状况的遥感监测是实现果树轻简高效管理和优质丰产的重要手段,但迄今有关基于低空遥感信息的果树营养诊断研究鲜见报道。本文采用具有490 nm、550 nm、570 nm、671 nm、680 nm、700 nm、720 nm、800 nm、840 nm、900 nm、950 nm等11个波段光谱的八旋翼飞行器(UAV)载多光谱遥感系统,获取距地面100 m高度的哈姆林甜橙植株春季冠层近地遥感信息,对比分析基于多元散射校正(MSC)和标准正态变量(SNV)两种预处理光谱和原始光谱(OS)的偏最小二乘(PLS)、多元线性回归(MLR)、主成分回归(PCR)及最小二乘支持向量机(LS-SVM)等4种模型对冠层叶片氮素、叶绿素a、叶绿素b和类胡萝卜素含量预测精度的影响。结果显示,距地面100 m高度的多光谱信息,通过SNV光谱预处理和MLR建模对冠层叶片氮素、叶绿素a和叶绿素b含量的预测效果均较好,预测集相关系数(Rp)值分别达0.8036、0.8065和0.8107,预测均方根误差(RMSEP)值分别为0.1363、0.0427和0.0243;而在SNV光谱预处理基础上的LS-SVM建模对冠层类胡萝卜素含量预测效果更优,Rp值达到了0.8535,RMSEP值为0.0117。表明利用机载多光谱图像信息可实现对柑橘植株冠层全氮及叶绿素a、叶绿素b和类胡萝卜素含量的较好估算,为大规模柑橘园植株冠层营养状况的精准和高效监测提供了一条新途径。     

A novel combined spectral index for estimating the ratio of carotenoid to chlorophyll content to monitor crop physiological and phenological status

Accurate estimation of the ratio of carotenoid (Car) to chlorophyll (Chl) content is crucial to provide valuable insight into diagnoses of plant physiological and phenological status in crop fields. Studies for assessing the ratio of Car to Chl content have been extensively conducted with semi-empirical approaches using spectral indices. However, spectral indices established in previous studies generally relied on site- or species-specific measured data and these indices typically lacked sufficient estimation accuracy for the ratio of Car to Chl content to be used across various species and under different physiological conditions. In this study, we propose a novel combined carotenoid/chlorophyll ratio index (CCRI) in the form of the carotenoid index (CARI) divided by the red-edge chlorophyll index (CI_red-edge): The value of the index is illustrated using synthetic data simulated from the leaf radiative transfer model PROSPECT-5 and with extensive measured datasets at both the leaf and canopy level from the ANGERS dataset and winter wheat and maize field experiments. Results show that CCRI was the index with the highest correlation with the ratio of Car to Chl content in PROSPECT-5 simulations (R² = 0.99, RRMSE = 8.65%) compared to other spectral indices. Calibration and validation results using the ANGERS and winter wheat leaf level data showed that CCRI achieved accurate estimation of the ratio of Car to Chl content (R² = 0.52, RRMSE = 14.10%). CCRI also showed a good performance (R² = 0.54, RRMSE = 17.08%) for estimation of the ratio of Car to Chl content in both calibration and validation with the winter wheat and maize canopy spectra measured in field experiments. Further investigation of the effect of the correlation between leaf Chl and Car content on the performance of CCRI indicated that variation of the correlation affected the retrieval accuracy of CCRI, and CCRI might not be very sensitive to changes of the ratio of Car to Chl content with low values (<0.10).     

Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT + SAIL

This study describes the retrieval of state variables (LAI, canopy chlorophyll, water and dry matter contents) for summer barley from airborne HyMap data by means of a canopy reflectance model (PROSPECT + SAIL). Three different inversion techniques were applied to explore the impact of the employed method on estimation accuracies: numerical optimization (downhill simplex method), a look-up table (LUT) and an artificial neural network (ANN) approach. By numerical optimization (Num Opt), reliable estimates were obtained for LAI and canopy chlorophyll contents (LAI × C_ab) with r² of 0.85 and 0.94 and RDP values of 1.81 and 2.65, respectively. Accuracies dropped for canopy water (LAI × C_w) and dry matter contents (LAI × C_m). Nevertheless, the range of leaf water contents (C_w) was very narrow in the studied plant material. Prediction accuracies generally decreased in the order Num Opt > LUT > ANN. This decrease in accuracy mainly resulted from an increase in offset in the obtained values, as the retrievals from the different approaches were highly correlated. The same decreasing order in accuracy was found for the difference between the measured spectra and those reconstructed from the retrieved variable values. The parallel application of the different inversion techniques to one collective data set was helpful to identify modelling uncertainties, as shortcomings of the retrieval algorithms themselves could be separated from uncertainties in model structure and parameterisation schemes.     

Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis

Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (ND_LMA).Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R²: 0.51–0.82, p < 0.0001). The best robustness (R² = 0.74, RMSE = 18.97 g/m² and Bias = 0.12 g/m²) was obtained using a combination of two low-scale features (1639 nm, scale 4) and (2133 nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the ND_LMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368 nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340 nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or spaceborne imaging spectroscopy data for mapping canopy foliar dry biomass.     

油气勘探中的化探遥感综合评价技术

油气化探和油气遥感虽然是两种不同的油气勘查方法,但二者在油气勘查中具有相辅相成的关系。地表化探异常与遥感影像异常的空间分布具有一致性,综合评价化探异常与遥感异常的油气指示意义,可提高油气勘探效率。