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1.
The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance Rrs(λ) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APDm) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr−1, whereas the Rrs(λ) uncertainty at 412 nm is the highest (APDm 47–62%, RMS 0.0027–0.0041 sr−1). The uncertainties of the aerosol optical thickness (AOT) τa, diffuse attenuation coefficient for downward irradiance at 490 nm Kd(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of Kd(490), SPM, and Chl-a may decrease significantly to the level of 20–30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task.  相似文献   

2.
Based on in situ water sampling and field spectral measurements in Dianshan Lake, a semi-analytical three-band algorithm was used to estimate Chlorophylla (Chla) content in case II waters. The three bands selected to estimate Chla for high concentrations included 653, 691 and 748 nm. An equation, based on the difference in reciprocal reflectance between 653 and 691 nm, multiplied by reflectance at 748 nm as [Rrs−1(653) − Rrs−1 (691)] Rrs(748), explained 85.57% of variance in Chla concentration with a root mean square error (RMSE) of <6.56 mg/m3. In order to test the utility of this model with satellite data, HJ-1A Hyperspectral Imager (HSI) data were analyzed using comparable wavelengths selected from the in situ data [B67−1(656) − B80−1(716)] B87(753). This model accounted for 84.3% of Chla variation, estimating Chla concentrations with an RMSE of <4.23 mg/m3. The results illustrate that, based on the determined wavelengths, the spectrum-based model can achieve a high estimation accuracy and can be applied to hyperspectral satellite imagery especially for higher Chla concentration waters.  相似文献   

3.
In-situ chlorophyll concentration data and remote sensing reflectance (Rrs) measurements collected in six different ship campaigns in the Arabian Sea were used to evaluate the accuracy, precision, and suitability of different ocean color chlorophyll algorithms for the Arabian Sea. The bio-optical data sets represent the typical range of biooptical conditions expected in this region and are composed of 47 stations encompassing chlorophyll concentration, between 0.072 and 5.90 mg m-3, with 43 observations in case I water and 4 observations in case II water. Six empirical chlorophyll algorithms [i.e. Aiken-C, POLDER-C, OCTS-C, Morel-3, Ocean Chlorophyll-2 (OC2) and Ocean Chlorophyll-4 (OC4)] were selected for analysis on the Arabian Sea data set. Numerous statistical and graphical criterions were used to evaluate the performance of these algorithms. Among these six chlorophyll algorithms two chlorophyll algorithms (i.e. OC2 and OC4) performed well in the case I waters of the Arabian Sea. The OC2 algorithm, a modified cubic polynomial function which uses ratio of Rrs490 nm and Rrs555 nm (where, Rrs is remote sensing reflectance), performed well with r2=0.85; rms =0.15. The OC4 algorithm, a four-band (443, 490, 510, 555 nm), maximum band ratio formulation was found best on the basis of statistical analysis results with r2=0.85 and rms=0.14. Both OC2 and OC4 algorithms failed to estimate chlorophyll inTrichodesmium dominated waters. The OC2 algorithm was preferred over OC4 algorithm for routine processing of the OCM data to generate chlorophyll-a images, as it uses a band ratio of 490/555 nm and atmospheric correction is more accurate in 490 nm compared to 443 nm band, which is used by OC4 algorithm.  相似文献   

4.
ABSTRACT

Optical satellite data is an efficient and complementary method to hydrographic surveys for deriving bathymetry in shallow coastal waters. Empirical approaches (in particular, the models of Stumpf and Lyzenga) provide a practical methodology to derive bathymetric information from remote sensing. Recent studies, however, have focused on enhancing the performance of such empirical approaches by extending them via spatial information. In this study, the relationship between multibeam depth and Sentinel-2 image bands was analyzed in an optically complex environment using the spatial predictor of kriging with an external drift (KED), where its external drift component was estimated: a) by a ratio of log-transformed bands based on Stumpf’s model (KED_S) and b) by a log-linear transform based on Lyzenga’s model (KED_L). Through the calibration of KED models, the study objectives were: 1) to better understand the empirical relationship between Sentinel-2 multispectral satellite reflectance and depth, 2) to test the robustness of KED to derive bathymetry in a multitemporal series of Sentinel-2 images and multibeam data, and 3) to compare the performance of KED against the existing non-spatial models described by Stumpf et al. and Lyzenga. Results showed that KED could improve prediction accuracy with a decrease in RMSE of 89% and 88%, and an increase in R2 of 27% and 14%, over the Stumpf and Lyzenga models, respectively. The decrease in RMSE provides a worthwhile improvement in accuracy, where results showed effective prediction of depth up to 6 m. However, the presence of higher concentrations of suspended materials, especially river plumes, can reduce this threshold to 4 m. As would be expected, prediction accuracy could be improved through the removal of outliers, which were mainly located in the channel of the river, areas influenced by the river plume, abrupt topography, but also very shallow areas close to the shoreline. These areas have been identified as conflictive zones where satellite-derived bathymetry can be compromised.  相似文献   

5.
The present study is aimed to determine the bio-optical characteristics of oceanic waters during South west monsoon in Bay of Bengal using hyperspectral radiometer. The variability of diffuse attenuation coefficient, Kd(λ), with chlorophyll a showed a good relation at shorter wavelengths, indicating the effect of phytoplankton on Kd(λ). The determination coefficient, R2 at 412, 443, 490 and 555 nm were greater than 0.931. A good linear relation between Kd(490) and Kd(λ) was observed at shorter wavelengths. These relationships of Kd(λ) provides a platform to study the underwater light field during Southwest monsoon in Bay of Bengal.  相似文献   

6.
杭州湾HJ CCD影像悬浮泥沙遥感定量反演   总被引：6，自引：0，他引：6  
利用环境小卫星CCD(HJ CCD)影像对杭州湾悬浮泥沙浓度(SSC)进行了反演研究。通过对杭州湾水体遥感反射率(Rrs)与SSC进行相关性分析发现,在690nm和830nm左右出现显著的反射峰,分别位于HJ CCD影像的第3和第4波段范围内;大于700nm波长处的Rrs与SSC相关性较好。基于实测Rrs和SSC之间的相关关系,利用第4和第3波段比值作为遥感因子建立SSC反演模型,模型决定系数达到0.90。借鉴近红外-短波红外(NIR-SWIR)结合的大气校正方法反演出的准同步MODIS气溶胶数据,实现了HJ CCD影像的大气校正,第3、第4波段的大气校正结果相对误差分别为5.54%和6.97%。结果显示,HJ CCD影像反演的SSC相对误差为7.12%;杭州湾悬浮泥沙浓度要显著高于长江口,且内部差异明显。研究表明,通过适当的大气校正方法和反演算法,HJ CCD影像可用于杭州湾悬浮泥沙浓度的估计。  相似文献   

7.
The relative abundance and distribution of trees in savannas has important implications for ecosystem function. High spatial resolution satellite sensors, including QuickBird and IKONOS, have been successfully used to map tree cover patterns in savannas. SPOT 5, with a 2.5 m panchromatic band and 10 m multispectral bands, represents a relatively coarse resolution sensor within this context, but has the advantage of being relatively inexpensive and more widely available. This study evaluates the performance of NDVI threshold and object based image analysis techniques for mapping tree canopies from QuickBird and SPOT 5 imagery in two savanna systems in southern Africa. High thematic mapping accuracies were obtained with the QuickBird imagery, independent of mapping technique. Geometric properties of the mapping indicated that the NDVI threshold produced smaller patch sizes, but that overall patch size distributions were similar. Tree canopy mapping using SPOT 5 imagery and an NDVI threshold approach performed poorly, however acceptable thematic accuracies were obtained from the object based image analysis. Although patch sizes were generally larger than those mapped from the QuickBird image data, patch size distributions mapped with object based image analysis of SPOT 5 have a similar form to the QuickBird mapping. This indicates that SPOT 5 imagery is suitable for regional studies of tree canopy cover patterns.  相似文献   

8.
Soil salinization is a worldwide environmental problem with severe economic and social consequences. In this paper, estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR) predictive model was carried out using QuickBird data and soil reflectance spectra. At first, a relationship between the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectral coverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBird data in estimating soil salinity by analyzing the correlations between the measured reflectance spectra and reflectance spectra derived from QuickBird data and analyzing the contributions of each band of QuickBird data to soil salinity estimation Finally, a PLSR predictive model of soil salinity was developed using reflectance spectra from QuickBird data and eight spectral indices derived from QuickBird data. The results indicated that the sensitive bands covered several bands of each optical sensor and these sensors can be used for soil salinity estimation. The result of estimation model showed that an accurate prediction of soil salinity can be made based on the PLSR method (R2 = 0.992, RMSE = 0.195). The PLSR model's performance was better than that of the stepwise multiple regression (SMR) method. The results also indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinity indices (SI1, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) as independent model variables can help to increase the accuracy of soil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soil moisture on prediction accuracy. The method developed in this paper can be applied in other arid and semi-arid areas, such as western China.  相似文献   

9.
The gross primary production (GPP) at individual CO2 eddy covariance flux tower sites (GPPTower) in Dali (DL), Wenjiang (WJ) and Linzhi (LZ) around the southeastern Tibetan Plateau were determined by the net ecosystem exchange of CO2 (NEE) and ecosystem respiration (Re). The satellite remote sensing-VPM model estimates of GPP values (GPPMODIS) used the satellite-derived 8-day surface reflectance product (MOD09A1), including satellite-derived enhanced vegetation index (EVI) and land surface water index (LSWI). In this paper, we assembled a subset of flux tower data at these three sites to calibrate and test satellite-VPM model estimated GPPMODIS, and introduced the satellite data and site-level environmental factors to develop four new assimilation models. The new assimilation models’ estimates of GPP values were compared with GPPMODIS and GPPTower, and the final optimum model among the four assimilation models was determined and used to calibrate GPPMODIS. The results showed that GPPMODIS had similar temporal variations to the GPPTower, but GPPMODlS were commonly higher in absolute magnitude than GPPTower with relative error (RE) about 58.85%. While, the assimilation models’ estimates of GPP values (GPPMODEL) were much more closer to GPPTower with RE approximately 6.98%, indicating that the capacity of the simulation in the new assimilation model was greatly improved, the R2 and root mean square error (RMSE) of the new assimilation model were 0.57–4.90% higher and 0.74–2.47 g C m−2 s−1 lower than those of the GPPMODIS, respectively. The assimilation model was used to predicted GPP dynamics around the Tibetan Plateau and showed a reliable result compared with other researches. This study demonstrated the potential of the new assimilation model for estimating GPP around the Tibetan Plateau and the performances of site-level biophysical parameters in related to satellite-VPM model GPP.  相似文献   

10.
In geopotential space, the fundamental geodetic parameter W 0 defines the Gauss–Listing geoid which can be used to best represent the Earth’s mean sea level (MSL) and hence specifies a conventional zero height level to unify vertical datums employed by mapping agencies throughout the world. Further, W 0 cannot be considered invariant as the parameter varies temporally as a direct response to sea level change and mass redistributions. This study determines W 0 and its rate, dW 0/dt, by utilizing altimetric MSL models and an independent mean dynamic topography (MDT) model to define points on the geoid. W 0 and dW 0/dt are estimated by two approaches: (i) by means of a global gravity field model (GGM) and (ii) within normal gravity field space as the geopotential value of the best fitting reference ellipsoid. The study shows that uncertainty in W 0 is mainly influenced by MDT while the choice of methodology, GGM and MSL data coverage are not significant within reason. Our estimate W 0 =?62636854.2 ± 0.2 m2?s?2 at epoch 2005.0 differs by 1.8?m2s?2 from the International Astronomical Union reference value. This study shows that, at a sub-decadal time scale, the time variation dW 0/dt stems mainly from sea level change with negligible effect from gravity field variations. dW 0/dt =?(?2.70 ± 0.03)?×?10?2?m2?s?2?year?1, corresponding to a MSL rise of 2.9?mm?year?1, is evaluated from sea level change based on 16?years of TOPEX and Jason-1 data.  相似文献   

11.
Estimation of forest structural parameters by field-based data collection methods is both expensive and time consuming. Satellite remote sensing is a low-cost alternative in modeling and mapping structural parameters in large forest areas. The current study investigates the potential of using WordView-2 multispectral satellite imagery for predicting forest structural parameters in a dryland plantation forest in Israel. The relationships between image texture features and the several structural parameters such as Number of Trees (NT), Basal Area (BA), Stem Volume (SV), Clark-Evans Index (CEI), Diameter Differentiation Index (DDI), Contagion Index (CI), Gini Coefficient (GC), and Standard Deviation of Diameters at Breast Heights (SDDBH) were examined using correlation analyses. These variables were obtained from 30 m × 30 m square-shaped plots. The Standard Deviation of Gray Levels (SDGL) as a first order texture feature and the second order texture variables based on Gray Level Co-occurrence Matrix (GLCM) were calculated for the pixels that corresponds to field plots. The results of the correlation analysis indicate that the forest structural parameters are significantly correlated with the image texture features. The highest correlation coefficients were calculated for the relationships between the SDDBH and the contrast of red band (r = 0.75, p < 0.01), the BA and the entropy of blue band (r = 0.73, p < 0.01), and the GC and the contrast of blue band (r = 0.71, p < 0.01). Each forest structural parameter was modeled as a function of texture measures derived from the satellite image using stepwise multi linear regression analyses. The determination coefficient (R2) and root mean square error (RMSE) values of the best fitting models, respectively, are 0.38 and 109.56 ha−1 for the NT; 0.54 and 1.79 m2 ha−1 for the BA; 0.42 and 27.18 m3 ha−1 for the SV; 0.23 and 0.16 for the CEI; 0.32 and 0.05 for the DDI; 0.25 and 0.06 for the CI; 0.50 and 0.05 for the GC; and 0.67 and 0.70 for the SDDBH. The leave-one-out cross-validation technique was applied for validation of the best-fitted models (R2 > 0.50). In conclusion, cross-validated statistics confirmed that the structural parameters including the BA, SDDBH, and GC can be predicted and mapped with a reasonable accuracy using the texture features extracted from the spectral bands of WorldView-2 image.  相似文献   

12.
Soil erosion rates in alpine regions are related to high spatial variability complicating assessment of risk and damages. A crucial parameter triggering soil erosion that can be derived from satellite imagery is fractional vegetation cover (FVC). The objective of this study is to assess the applicability of normalized differenced vegetation index (NDVI), linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) in estimating abundance of vegetation cover in alpine terrain. To account for the small scale heterogeneity of the alpine landscape we used high resolved multispectral QuickBird imagery (pixel resolution = 2.4 m) of a site in the Urseren Valley, Central Swiss Alps (67 km2). A supervised land-cover classification was applied (total accuracy 93.3%) prior to the analysis in order to stratify the image. The regression between ground truth FVC assessment and NDVI as well as MTMF-derived vegetation abundance was significant (r2 = 0.64, r2 = 0.71, respectively). Best results were achieved for LSU (r2 = 0.85). For both spectral unmixing approaches failed to estimate bare soil abundance (r2 = 0.39 for LSU, r2 = 0.28 for MTMF) due to the high spectral variability of bare soil at the study site and the low spectral resolution of the QuickBird imagery. The LSU-derived FVC map successfully identified erosion features (e.g. landslides) and areas prone to soil erosion. FVC represents an important but often neglected parameter for soil erosion risk assessment in alpine grasslands.  相似文献   

13.
QuickBird satellite imagery acquired in June 2003 and September 2004 was evaluated for detecting the noxious weed spiny aster [Leucosyris spinosa (Benth.) Greene] on a south Texas, USA rangeland area. A subset of each of the satellite images representing a diversity of cover types was extracted and used as a study site. The satellite imagery had a spatial resolution of 2.8 m and contained 11-bit data. Unsupervised and supervised classification techniques were used to classify false colour composite (green, red, and near-infrared bands) images of the study site. Imagery acquired in June was superior to that obtained in September for distinguishing spiny aster infestations. This was attributed to differences in spiny aster phenology between the two dates. An unsupervised classification of the June image showed that spiny aster had producer's and user's accuracies of 90% and 93.1%, respectively, whereas a supervised classification of the June image had producer's and user's accuracies of 90% and 81.8%, respectively. These results indicate that high resolution satellite imagery coupled with image analysis techniques can be used successfully for detecting spiny aster infestations on rangelands.  相似文献   

14.
Saltcedar (Tamarix ramosissima), an invasive shrub species, has successfully invaded large extents of several riparian zones in the western United States and northern Mexico. Mapping the distribution and abundance of saltcedar over these large areas through a multi-seasonal, cost-effective monitoring approach using satellite remote sensing is very essential. Ground truth surveys were conducted at 79 locations where the spectral reflectance measurements of vegetation, type of plant species, plant heights, soil samples and GPS co-ordinates were recorded. All the sampling was designed to coincide with the satellite overpass period. The Landsat TM colour-composite spectral ratio image (normalized difference vegetative index (NDVI), R 1,5 and R 1,7 as green, blue and red) can clearly identify and map the areas infested with saltcedar. The Landsat image analysis shows that these spectral ratios can be applied to multiple satellite overpasses for monitoring the seasonal progression of the saltcedar growth over time.  相似文献   

15.
Land surface temperature (LST) is an important aspect in global to regional change studies, for control of climate change and balancing of high temperature. Urbanization is one of the influencing factors increasing land surface and atmospheric temperature, by the emission of greenhouse gases (e.g. CO2, NO and methane). In the present study, LST was derived from Landsat-8 of multitemporal data sets to analyse the spatial structure of the urban thermal environment in relation to the urban surface characteristics and land use–land cover (LULC). LST is influenced by the greenhouse gases i.e. CO2 plays an important role in increasing the earth’s surface temperature. In order to provide the evidence of influence of CO2 on LST, the relationship between LST, air temperature and CO2 was analysed. Landsat-8 satellite has two thermal bands, 10 and 11. These bands were used to accurately to calculate the temperature over the study area. Results showed that the strength of correlation between ground monitoring data and satellite data was high. Based on correlation values of each month April (R2 = 0.994), May (R2 = 0.297) and June (R2 = 0.934), observed results show that band 10 was significantly correlating with air temperature. Relationship between LST and CO2 levels were obtained from linear regression analysis. band 11 was correlating significantly with CO2 values in each of the months April (R2 = 0.217), May (R2 = 0.914) and June, (R2 = 0.934), because band 11 is closer to the 15-micron band of CO2. From the results, it was observed that band 10 can be used for calculating air temperature and band 11 can be used for estimation of greenhouse gases.  相似文献   

16.
针对高分辨率卫星影像,提出一种特征分量构建与面向对象结合的阴影提取方法。分析遥感阴影光谱特性,构建彩色不变特征C3、亮度特征I、主成分第一特征量PC1以及蓝色波段和近红外波段归一化比率特征RATIOb_nir,增强阴影信息。采用线性变换将几个特征分量Digital Number(DN)值归一化到相同范围,对这几个分量进行综合分析。以I和PC1分量为输入对影像进行多尺度分割,建立包括波段均值、标准差、最大差异等特征的规则集,实现面向对象的阴影信息提取。选取20幅QuickBird影像为例进行阴影提取实验,平均总体精度为97%,平均用户精度为96%,平均Kappa系数为0.94。实验结果表明,相对传统基于像素信息提取方法,本文方法提取阴影斑块完整,无破碎图斑;相对基于原始光谱的面向对象方法,本文方法提取精度更高。  相似文献   

17.
With the emergence of very high spatial and spectral resolution data set, the resolution gap that existed between remote-sensing data set and aerial photographs has decreased. The decrease in resolution gap has allowed accurate discrimination of different tree species. In this study, discrimination of indigenous tree species (n?=?5) was carried out using ground based hyperspectral data resampled to QuickBird bands and the actual QuickBird imagery for the area around Palapye, Botswana. The purpose of the study was to compare the accuracies of resampled hyperspectral data (resampled to QuickBird sensors) with the actual image (QuickBird image) in discriminating between the indigenous tree species. We performed Random Forest (RF) using canopy reflectance taking from ground-based hyperspectral sensor and the reflectance delineated regions of the tree species. The overall accuracies for classifying the five tree species was 79.86 and 88.78% for both the resampled and actual image, respectively. We observed that resampled data set can be upscale to actual image with the same or even greater level of accuracy. We therefore conclude that high spectral and spatial resolution data set has substantial potential for tree species discrimination in savannah environments.  相似文献   

18.
Developing models for estimating aboveground biomass (AGB) in naturally growing forests is critical for climate change modelling. AGB models developed using satellite imagery varies with study area, depending on the complexity of vegetation and landscape structure, which affects the upwelling radiance. We assessed the potential of SPOT-6 imagery in predicting AGB of trees planted at different time periods, using image texture combinations. Image texture variables were computed from the SPOT6 pan-sharpened image data, which is characterised by a 1.5 m spatial resolution. In addition, we incorporated the minimal variance technique to select the optimum window sizes that best captures AGB variation in our study area. The results showed that image texture was able to detect AGB for both mature and young trees, however, models detecting mature trees were more superior, with accuracies of R2 = 0.70 and 0.25 for 2009–2011 and 2011–2013 plantation phases, respectively. In addition, our results showed that the three band texture ratios yielded the highest accuracy (R2 = 0.88 and RMSE = 54.54 kg m−2) compared to two texture (R2 = 0.85 and RMSE = 60.65 kg m−2) and single texture band combinations (R2 = 0.64 and RMSE = 94.13 kg m−2). A frequency analysis was also run to determine which bands appeared more frequently in the selected texture band models. The frequency analysis revealed that both the red and green bands appeared more frequently on the selected texture band variables, indicating that they were more sensitive to the variation of AGB in our study area. The results showed high variation in AGB within the Buffelsdraai reforestation site, especially due to varying tree plantation phases as well as topography. In essence, the study demonstrated the possibility of image texture combinations computed from the SPOT-6 image in estimating AGB.  相似文献   

19.
Low and moderate spatial resolution satellite sensors (such as TOMS, AVHRR, SeaWiFS) have already shown their capability in tracking aerosols at a global scale. Sensors with moderate to high spatial resolution (such as MODIS and MERIS) seem also to be appropriate for aerosol retrieval at a regional scale. We investigated in this study the potential of MERIS-ENVISAT data to resolve the horizontal spatial distribution of aerosols over urban areas, such as the Athens metropolitan area, by using the differential textural analysis (DTA) code. The code was applied to a set of geo-corrected images to retrieve and map aerosol optical thickness (AOT) values relative to a reference image assumed to be clean of pollution with a homogeneous atmosphere. The comparison of satellite retrieved AOT against PM10 data measured at ground level showed a high positive correlation particularly for the AOT values calculated using the 5th MERIS’ spectral band (R2=0.83). These first results suggest that the application of the DTA code on cloud free areas of MERIS images can be used to provide AOT related to air quality in this urban region. The accuracy of retrieved AOT mainly depends on the overall quality, the pollution cleanness and the atmospheric homogeneity of the reference image.  相似文献   

20.
The possibility of quantifying iron content in the topsoil of the slopes of the El Hacho Mountain complex in Southern Spain using imaging spectroscopy is investigated. Laboratory, field and airborne spectrometer (ROSIS) data are acquired, in combination with soil samples, which are analysed for dithionite extractable iron (Fed) content. Analysis of the properties of two iron related absorption features present in laboratory spectra demonstrates good relations, especially between the standard deviation (S.D.) of the values in an absorption feature and the Fed content (R2 = 0.67) as well as the ratio based Redness Index (R2 = 0.51). Such derived relations are less strong for the ROSIS data (R2 for S.D. = 0.26 and R2 for Redness Index = 0.22). The spatial distribution of iron in vegetated areas shows a strong sensitivity of these relations with the presence of vegetation. A combination of both methods shows that the overestimation of the Fed content with the one method is (partly) compensated by the underestimation with the other method.  相似文献   

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