A simple correction method for the MODIS surface reflectance product over typical inland waters in China

The Moderate Resolution Imaging Spectroradiometer (MODIS) has the advantage of providing continuous, global, near-daily spatial measurements, and has greatly aided in understanding physical, optical, and biological processes in the global ocean biosphere. However, little research has been implemented for the remote-sensing monitoring of global inland waters. One important factor is that there is no operational atmospheric correction method designed for global inland waters. The MODIS surface reflectance product (MOD09) provides surface reflectance data for land at the global scale, but it does not offer accurate atmospheric correction over inland waters because of the constraints of its primary correction algorithm. The purpose of this article is to provide a simple and operational correction method for the MOD09 product to retrieve the water-leaving reflectance for large inland waters larger than 25 km². The correction method is based on an analysis of additive noises in MOD09 data over inland waters and on the adoption of two assumptions. Field-measured data collected in three typical inland waters in China were used to assess the performance of the correction method to ensure its applicability for waters in different conditions. The results show acceptable agreement with field data over the three inland waterbodies, with a mean relative error of 17.1% in visible bands. Our study demonstrates that the MOD09 correction method is moderately accurate when compared with the optimal method for specific waterbodies, but it has the potential for use in operational data-processing systems to derive water-leaving reflectance data from MOD09 data over inland waters in a variety of conditions and large regions.     

Mapping the concentrations of total suspended matter in Lake Taihu,China, using Landsat‐5 TM data

Remote sensing techniques can be used to estimate and map the concentrations of suspended matter in inland water, providing both spatial and temporal information. Although an empirical approach to remote sensing of inland waters has been carried out frequently, satellite imagery has not been incorporated into routine lake monitoring programmes due in part to the lack of a standard prediction equation with multi‐temporal capacity for suspended matter. Empirical and physical models must be developed for each lake and its corresponding turbidity composition if they are to be compared over time, or with other bodies of water.

This study aimed to develop and apply multi‐temporal models to estimate and map the concentrations of total suspended matter (TSM) in Lake Taihu, China. Two Landsat‐5 Thematic Mapper (TM) images and nearly contemporaneous in situ measurements of TSM were used. A modified Dark‐Object Subtraction (DOS) method was used, and appeared to be adequate for atmospheric correction. The relationships were examined between TSM concentrations and atmospherically corrected TM band and band ratios. Results of this study show that the ratio TM4/TM1 has a strong relationship with TSM concentrations for lake waters with relatively low concentrations of phytoplankton algae. However, TM3 provided a strong predictive relationship with TSM concentrations despite varied water quality conditions. Different prediction models were developed and compared using multiple regression analysis. The Akaike Information Criteria (AIC) approach was used to choose the best models. The validation of the multi‐temporal capability of the best models indicated that it is feasible to apply the linear regression model using TM3 to estimate TSM concentrations across time in Lake Taihu, even if no in situ data were available.     

Evaluation of four MERIS atmospheric correction algorithms in Lake Kasumigaura,Japan

Accurate atmospheric correction for turbid inland waters remains a significant challenge. Several atmospheric correction algorithms have been proposed to address this issue, but their performance is unclear in regard to Asian lakes, some of which have extremely high turbidity and different inherent optical properties from lakes in other continents. Here, four existing atmospheric correction algorithms were tested in Lake Kasumigaura, Japan (an extremely turbid inland lake), using in situ water-leaving reflectance and concurrently acquired medium resolution imaging spectrometer (MERIS) images. The four algorithms are (1) GWI (the standard Gordon and Wang algorithm with an iterative process and a bio-optical model) (2) MUMM (Management Unit of the North Sea Mathematical Models); (3) SCAPE-M (Self-Contained Atmospheric Parameters Estimation for MERIS Data) and (4) C2WP (Case-2 Water Processor). The results show that all four atmospheric correction algorithms have limitations in Lake Kasumigaura, even though SCAPE-M and MUMM gave acceptable accuracy for atmospheric correction in several cases (relative errors less than 30% for the 2006 and 2008 images). The poor performance occurred because the conditions in Lake Kasumigaura (i.e. the atmospheric state and/or turbidity) did not always meet the assumptions in each atmospheric correction algorithm (e.g. in 2010, the relative errors ranged from 42% to 83%). These results indicate that further improvements are necessary to address the issue of atmospheric correction for turbid inland waters such as Lake Kasumigaura, Japan.     

Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research

Atmospheric correction is an important preprocessing step required in many remote sensing applications. The authors are engaged in the project 'Human Dimensions of Amazonia: Forest Regeneration and Landscape Structure' in NASA/INPE's Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) programme. This project requires use of corrected Landsat TM data since research foci integrate ground-based data and TM to: (1) measure and model biomass; (2) classify multiple stages of secondary succession; (3) model land cover/land use changes; and (4) derive spectral signatures consistent across different study areas. The 30+ scenes of TM data are historic and lack detailed atmospheric data needed by physically-based atmospheric correction models such as 6S (Second Simulation of the Satellite Signal in the Solar Spectrum). Imagebased DOS models are based on image measurements and explored in this article for application to LBA study areas. Two methods using theoretical spectral radiance and image acquisition date respectively were used to convert TM DN values to at-satellite radiance. Three image-based models were employed using each method to convert at-satellite radiance to surface reflectance. Analyses of these six different image-based models were conducted. The Improved Imagebased DOS was the best technique for correcting atmospheric effects in this LBA research with results similar to those obtained from physically-based approaches.     

Temporal generalization of sub-pixel vegetation mapping with multiple machine learning and atmospheric correction algorithms

Temporal generalization allows a trained classification algorithm to be applied to multiple images across time to derive reliable classification map products. It is a challenging remote-sensing research topic since the results are dependent on the selection of atmospheric correction methods, classification algorithms, validation processes, and their varying combinations. This study examined the temporal generalization of sub-pixel vegetation mapping using multiple Landsat images (1990, 1996, 2004, and 2010). All Landsat images were processed with two atmospheric correction methods: simple dark object subtraction (DOS) and the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm. For the sub-pixel vegetation mapping of the 2004 Landsat image, we used high-resolution OrbView-3 images as a training/validation data set and compared three machine learning algorithms (neural networks, random forests, and classification and regression trees) for their classification performance. The trained classifiers were then applied to other Landsat images (1990, 1996, and 2010) to derive sub-pixel vegetation map products. For the 2004 Landsat image classification, cross-validation shows similar classification results for neural networks (root mean square error (RMSE) = 0.099) and random forests (RMSE = 0.100) algorithms, and both are better than classification and regression trees (RMSE = 0.123). Pseudo-invariant pixels between 2004 and 2010 were used as validation points to evaluate the temporal generalizability of classification algorithms. Simple DOS and LEDAPS atmospheric correction resulted in similar accuracy statistics. The neural-network-based classifier performed best in generating reliable sub-pixel vegetation map products across time.     

Atmospheric correction of ENVISAT/MERIS data over inland waters: Validation for European lakes

Traditional methods for aerosol retrieval and atmospheric correction of remote sensing data over water surfaces are based on the assumption of zero water reflectance in the near-infrared. Another type of approach which is becoming very popular in atmospheric correction over water is based on the simultaneous retrieval of atmospheric and water parameters through the inversion of coupled atmospheric and bio-optical water models. Both types of approaches may lead to substantial errors over optically-complex water bodies, such as case II waters, in which a wide range of temporal and spatial variations in the concentration of water constituents is expected. This causes the water reflectance in the near-infrared to be non-negligible, and that the water reflectance response under extreme values of the water constituents cannot be described by the assumed bio-optical models. As an alternative to these methods, the SCAPE-M atmospheric processor is proposed in this paper for the automatic atmospheric correction of ENVISAT/MERIS data over inland waters. A-priori assumptions on the water composition and its spectral response are avoided by SCAPE-M by calculating reflectance of close-to-land water pixels through spatial extension of atmospheric parameters derived over neighboring land pixels. This approach is supported by the results obtained from the validation of SCAPE-M over a number of European inland water validation sites which is presented in this work. MERIS-derived aerosol optical thickness, water reflectance and water pigments are compared to in-situ data acquired concurrently to MERIS images in 20 validation match-ups. SCAPE-M has also been compared to specific processors designed for the retrieval of lake water constituents from MERIS data. The performance of SCAPE-M to reproduce ground-based measurements under a range of water types and the ability of MERIS data to monitor chlorophyll-a and phycocyanin pigments using semiempirical algorithms after SCAPE-M processing are discussed. It has been found that SCAPE-M is able to provide high accurate water reflectance over turbid waters, outperforming models based on site-specific bio-optical models, although problems of SCAPE-M to cope with clear waters in some cases have also been identified.     

Comparison of MODIS and Landsat TM5 images for mapping tempo–spatial dynamics of Secchi disk depths in Poyang Lake National Nature Reserve,China

Landsat has successfully been applied to map Secchi disk depth of inland water bodies. Operational use for monitoring a dynamic variable like Secchi disk depth is however limited by the 16‐day overpass cycle of the Landsat system and cloud cover. Low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) image captured twice a day could potentially overcome these problems. However, its potential for mapping Secchi disk depth of inland water bodies has so far rarely been explored. This study compared two image sources, MODIS and Landsat Thematic Mapper (TM), for mapping the tempo–spatial dynamics of Secchi disk depth in Poyang Lake National Nature Reserve, China. Secchi disk depths recorded at weekly intervals from April to October in 2004 and 2005 were related to 5 Landsat TM and 22 MODIS images respectively. Two multiple regression models including the blue and red bands of Landsat TM and MODIS respectively explained 83% and 88% of the variance of the natural logarithm of Secchi disk depth. The standard errors of the predictions were 0.20 and 0.37 m for Landsat TM and MODIS‐based models. A high correlation (r = 0.94) between the predicted Secchi disk depth derived from the two models was observed. A discussion of advantages and disadvantages of both sensors leads to the conclusion that MODIS offers the possibility to monitor water transparency more regularly and cheaply in relatively big and frequently cloud covered lakes as is with Poyang Lake.     

渭河定量遥感水质反演中的大气校正作用研究

以渭河陕西段为研究对象,在获取了渭河陕西段实地监测数据和相应时间段的SPOT-5遥感影像数据基础上采用黑暗像元法(DOS)、大气辐射传输模型法等7种大气校正方法对SPOT-5遥感影像进行大气校正。结合校正后的遥感数据,使用多元线性回归、支持向量机(SVM)、及BP神经网络3种方法对渭河进行定量遥感水质反演。实验结果表明,通过遥感影像对渭河进行定量水质反演是可行的,大气校正在一定程度上提高了定量遥感水质的精度。对于SPOT-5遥感影像的大气校正,采取对遥感数据辐射定标后消除各波段最暗像元的方法可以达到较好的效果。     

Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data

Using the NASA maintained ocean optical and biological in situ data that were collected during 2002-2005, we have evaluated the performance of atmospheric correction algorithms for the ocean color products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. Specifically, algorithms using the MODIS shortwave infrared (SWIR) bands and an approach using the near-infrared (NIR) and SWIR combined method are evaluated, compared to the match-up results from the NASA standard algorithm (using the NIR bands). The in situ data for the match-up analyses were collected mostly from non-turbid ocean waters. It is critical to assess and understand the algorithm performance for deriving MODIS ocean color products, providing science and user communities with the important data quality information. Results show that, although the SWIR method for data processing has generally reduced the bias errors, the noise errors are increased due mainly to significantly lower sensor signal-noise ratio (SNR) values for the MODIS SWIR bands, as well as the increased uncertainties using the SWIR method for the atmospheric correction. This has further demonstrated that future ocean color satellite sensors will require significantly improved sensor SNR performance for the SWIR bands. The NIR-SWIR combined method, for which the non-turbid and turbid ocean waters are processed using the NIR and SWIR method, respectively, has been shown to produce improved ocean color products.     

Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment

Precise atmospheric correction is important for applications where small differences in surface reflectance (SR) are significant, such as biomass estimation, crop phenology, and retrieval of water quality parameters. It also enables direct comparison between different image dates and different sensors. As a precursor to monitoring different parameters of water quality around the coastline of Hong Kong using medium-resolution sensors Landsat TM/ETM, and HJ-1A/B, this study evaluated the performance of five atmospheric correction methods. The estimated SR of the first four reflective bands of Landsat 7 ETM+ and of the identical bands of the HJ-1A/B satellites was compared with in situ multispectral radiometer (MSR) SR measurements over sand, artificial turf, grass, and water surfaces for the five atmospheric correction methods – second simulation of the satellite signal in the solar spectrum (6S), fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH), atmospheric correction (ATCOR), dark object subtraction (DOS), and the empirical line method (ELM). Among the five methods, 6S was observed to be consistently more precise for SR estimation, with significantly less difference from the in-situ-measured SR, especially over lower reflective water surfaces. Of the two image-based methods, DOS performed well over the darker surfaces of water and artificial turf, although still inferior to 6S, while ELM worked well for grass sites as compared to the DOS and equalled the good performance of 6S over the high reflective sand surfaces.

The study also evaluated the new standard Landsat SR product Landsat ecosystem disturbance adaptive processing system (LEDAPS) using the in situ measured SR data for the three land surface types – sand, artificial turf, and grass. For the highly and moderately reflecting bright sand and artificial turf, LEDAPS performed poorly, while for the darker grass site it performed better, although still inferior to 6S and ELM methods. This is probably due to the variable aerosol types and atmospheric conditions of Hong Kong, as LEDAPS was mainly compiled with reference to larger continental landmass areas.     

利用多元遥感数据进行CBERS-02卫星大气校正研究

针对山地丘陵等复杂地表下地形对遥感数据影响较大的情况,提出了适合该区域的大气校正方法。该方法以MODIS数据为数据源,基于6S反演出陆地上空的气溶胶模式和光学厚度。使用同时相同区域的TM数据对CCD数据进行交叉定标,利用ATCOR3实现了对CBERS\|02星CCD数据进行大气校正。校正前后数据比较结果表明:该算法还原了下垫面原貌,尤其对山区丘陵等复杂地表具有很好效果。     

遥感影像地形与大气校正系统设计与实现

遥感影像地形和大气校正是提高定量化遥感数据处理精度的重要因素。目前的数据处理软件系统集成了一些地形和大气校正算法,但在应用中还存在不能获取重要的地形参数(如阴影因子、天空可视因子等),需提供精准DEM和校正方法基于朗伯体地表假设等问题。为应对遥感专业用户需求,设计并实现了遥感影像地形与大气校正软件系统,用以对影像进行地形辐射校正、获取DEM数据和相关地形参数、地形与大气校正等。介绍了系统的功能模块设计并展示了系统的原型版本,并应用系统中的地形和大气校正方法获取了HJ/CCD影像和Landsat/TM影像的反射率。校正结果表明:该系统中的BRDF模型能够有效消除地形影响。系统的实现可以为遥感科学研究和应用提供支撑。     

A comparison of algorithms for Near‐Real Time water vapour retrieval from MODIS

When studying the Earth's surface from space it is important that the component of the signal measured by the satellite‐based sensor due to the atmosphere is accurately estimated and removed. Such atmospheric correction requires good knowledge of atmospheric parameters including precipitable water (PW), ozone concentration and aerosol optical depth. To make full use of the capabilities of satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) these parameters should be accurately estimated in Near‐Real Time (NRT) with complete global coverage approximately every two days. NRT retrieval of the required ancillary information facilitates the atmospheric correction of such direct broadcast data from the MODIS instrument in the operational environment. In this paper three Near Infrared (NIR) algorithms for PW retrieval from MODIS are compared to determine which is most suitable for use in an operational MODIS‐based process for the atmospheric correction of spectral reflectance data. Two of the algorithms estimate PW in NRT and gave RMS errors of approximately 0.48 g cm^?2 (23%) and 0.59 g cm^?2 (28%), respectively, when compared against radiosonde data and modelled PW fields over Western Australia. The third algorithm was the NIR PW product from MODIS (MOD05) archived by the Distributive Active Archive Centre (DAAC). For the same locations the MOD05 NIR PW dataset gave an RMS error of approximately 0.95 g cm^?2 (44%). In each of the cases the best results were obtained after optimal cloudmasking of the NIR data. In this paper, the accuracy and suitability of the three algorithms for use in the operational atmospheric correction of MODIS data are evaluated and the importance of an accurate cloudmask for atmospheric correction in NRT is discussed.     

Haze removal based on advanced haze-optimized transformation (AHOT) for multispectral imagery

Ever-present spatially varying haze contamination in satellite scenes limits applications using visible and near-infrared bands of low-temporal-resolution multispectral satellite images. A relative atmospheric correction technique, the virtual cloud point (VCP) method, which is based on advanced haze-optimized transformation (AHOT), is developed for haze removal. It is an improved algorithm of the previous dark-object subtraction (DOS) based on haze-optimized transformation (HOT). In AHOT, extra steps are added to HOT to remove confusion caused by some land-cover types. The VCP method uses the upper bound of the histogram, as well as the lower bound, so that it enlarges the digital number (DN) variance reduced by haze, which is not considered in DOS. To evaluate this algorithm, hazy subsets of one Landsat Thematic Mapper (TM) and one QuickBird image are employed. Through before-and-after comparison using true-colour images and the normalized difference vegetation index (NDVI), it proves that the VCP method based on AHOT is apparently better than DOS based on HOT, when haze is distributed over urban areas where vegetation is sparse.     

不同大气校正方法对中小湖泊蓝藻遥感动态监测的影响

环境小卫星可实现中小湖泊蓝藻动态监测,但不同大气校正方法对于相同影像的处理结果有很大差异。研究利用多种大气校正方法对环境小卫星影像进行辐射校正处理,利用多个感兴趣区的全局和局部特征以及多个统计量对处理结果进行分析,比较其在蓝藻动态监测中的作用。基于多光谱植被指数计算蓝藻生物量的思路,分析了影像经不同大气校正算法处理后,其归一化植被指数的差异性来源及其对蓝藻生物量计算结果的影响。结果表明:蓝藻动态监测的定量描述会因大气校正算法不同而不一致,进而对几种大气校正算法在蓝藻生物量监测和定量分析中所产生的不确定性进行了比较分析,并对各算法的有效利用提出了建议。     

Use of satellite images of chlorophyll fluorescence to monitor the spring bloom in coastal waters

This article demonstrates a successful application of fluorescence line height (FLH) images from the Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagers and provides a strong argument for making more widespread use of FLH in monitoring surface phytoplankton in coastal waters. In the present example, MERIS and MODIS FLH images show the start of the spring bloom in coastal waters of the Strait of Georgia in British Columbia, Canada. The images clearly show a recurring pattern in five of the eight years from 2003 to 2010 covered by MERIS, which suggests seeding of the early spring bloom from narrow coastal inlets. Such seeding has been suggested before, but never observed. FLH images show the blooms more clearly than images of surface chlorophyll based on the ratios of water-leaving radiances in the blue and green spectral range (440–560 nm). FLH images used here have been derived with no atmospheric correction. Alternative products based on the blue/green ratio require atmospheric correction, which is difficult in coastal areas. Such products also tend to be more significantly confused with other constituents of coastal waters.     

A practical approach to retrieving the finer areas of algal bloom in inland lakes from coarse spatial resolution satellite data

Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) images have been used to monitor algal blooms in the open ocean, coastal waters, and inland lakes. However, it is difficult to obtain an accurate definition of algal bloom areas in inland lakes due to the spatial resolution of the generated images. This study developed a practical approach that uses a linear spectral mixing model with a moving window (LSMM), to obtain a finer algal bloom area. The approach analyses the differences in areas of algal bloom retrieved from MODIS images with 250 and 500 m spatial resolutions from 2012 to 2015 and synchronous VIIRS images with 750 m spatial resolution. Forty-two data sets with 126 satellite images were selected. The results showed that the average relative area difference (RAD) of algal bloom in the MODIS 500 m image was approximately 21.31% compared with the MODIS 250 m image and approximately 33.77% compared with the VIIRS image. A 5 × 5 window size was selected for the MODIS 500 m and VIIRS images. The results demonstrated that the approach can be successfully applied to MODIS 500 m and VIIRS images because the RAD significantly improved. The average RAD decreased to 9.39% in the MODIS 500 m image and to 12.84% in the VIIRS image. The relationship between the landscape of the algal bloom patch and the RAD showed that the performance of the LSMM method improved as the patch density (PD) increased from 0 to 2. When the perimeter-area ratio (PARA) is greater than 2 and the mean patch size (MPS) is less than approximately 5 km², the LSMM method significantly improved the RAD. An independent validation demonstrated that the LSMM method developed for MODIS and VIIRS images can be successfully applied to other coarser-resolution spatial imageries such as Geostationary Ocean Color Imager (GOCI) images. The LSMM method is more effective than the other methods for determining the fragmented landscape of algal blooms.     

Detection of turbid waters and absorbing aerosols for the MODIS ocean color data processing

Atmospheric correction for the ocean color products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) uses two near-infrared (NIR) bands centered at 748 and 869 nm for identifying aerosol type and correcting aerosol contributions at the MODIS visible wavelengths. The ocean is usually assumed to be black for open oceans at these two NIR bands with modifications for the productive waters and aerosols are assumed to be non- or weakly absorbing. For cases with strongly absorbing aerosols and cases with the significant NIR ocean contributions, the derived ocean color products will have significant errors, e.g., the derived MODIS normalized water-leaving radiances are biased low considerably. Both cases lead to a significant drop of the sensor-measured radiance at the short visible wavelengths, and they both have similar and indistinguishable radiance characteristics at the short visible wavelengths. To properly handle such cases, the strongly absorbing aerosols and turbid waters need to be identified. Therefore, an appropriate approach (different from the standard procedure) may be carried out. In this paper, we demonstrate methods to identify the turbid waters and strongly absorbing aerosols using combinations of MODIS-measured radiances at the short visible, NIR, and short wave infrared (SWIR) bands. The algorithms are based on the fact that for the turbid waters the ocean has significantly large contributions at the NIR bands, whereas at the SWIR bands the ocean is still black due to much stronger water absorption. With detection of the turbid waters, the strongly absorbing aerosols can then be identified using the MODIS measurements at the short visible and NIR bands. We provide results and discussions for test and evaluation of the algorithm performance with various examples in the coastal regions for the turbid waters and for various absorbing aerosols (e.g., volcano ash plumes, dust, smoke). The proposed algorithms are efficient in the data processing, and can be carried out prior to the atmospheric correction procedure.     

Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS

Eutrophication and cyanobacterial algal blooms present an increasing threat to the health of freshwater ecosystems and to humans who use these resources for drinking and recreation. Remote sensing is being used increasingly as a tool for monitoring these phenomena in inland and near-coastal waters. This study uses the Medium Resolution Imaging Spectrometer (MERIS) to view Zeekoevlei, a small hypertrophic freshwater lake situated on the Cape Flats in Cape Town, South Africa, dominated by Microcystis cyanobacteria. The lake's small size, highly turbid water, and covariant water constituents present a challenging case for both algorithm development and atmospheric correction. The objectives of the study are to assess the optical properties of the lake, to evaluate various atmospheric correction procedures, and to compare the performance of empirical and semi-analytical algorithms in hypertrophic water. In situ water quality parameter and radiometric measurements were made simultaneous to MERIS overpasses. Upwelling radiance measurements at depth 0.66 m were corrected for instrument self-shading and processed to water-leaving reflectance using downwelling irradiance measurements and estimates of the vertical attenuation coefficient for upward radiance, K_u, generated from a simple bio-optical model estimating the total absorption, a(λ), and backscattering coefficients, b_b(λ). The normalised water-leaving reflectance was used for assessing the accuracy of image-based Dark Object Subtraction and 6S Radiative Transfer Code atmospheric correction procedures applied to MERIS. Empirical algorithms for estimating chlorophyll a (Chl a), Total Suspended Solids (TSS), Secchi Disk depth (z_SD) and absorption by CDOM (a_CDOM) were derived from simultaneously collected in situ and MERIS measurements. The empirical algorithms gave high correlation coefficient values, although they have a limited ability to separate between signals from covariant water constituents. The MERIS Neural Network algorithms utilised in the standard Level 2 Case 2 waters product and Eutrophic Lakes processor were also used to derive water constituent concentrations. However, these failed to produce reasonable comparisons with in situ measurements owing to the failure of atmospheric correction and divergence between the optical properties and ranges used to train the algorithms and those of Zeekoevlei. Maps produced using the empirical algorithms effectively show the spatial and temporal variability of the water quality parameters during April 2008. On the basis of the results it is argued that MERIS is the current optimal sensor for frequent change detection applications in inland waters. This study also demonstrates the considerable potential value for simple TOA algorithms for hypertrophic systems. It is recommended that regional algorithm development be prioritized in southern Africa and that remote sensing be integrated into future operational water quality monitoring systems.     

TM遥感影像的地形辐射校正研究

从地面所接收到的太阳直接辐射、天空散射辐射和临近地形反射附加的辐射三个方面分析计算地面每个像元的太阳总辐射,并在此基础上建立地表真实反射率恢复模型,实现对地形的辐射校正。在算法实现上,采用交互式数据语言（Interactive Data Language,IDL）,结合6S大气校正模型和数字高程模型（DEM）进行编程实现。利用北京山区的TM遥感影像所做的实验表明该方法能有效地消除卫星影像中地形的影响,为影像的后续处理提供更真实的信息。