Satellite remote sensing of waste water reservoirs

SPOT multi-spectral images of nearly 100 water reservoirs were analysed. The image analysis was supported by ground measurements which included water sampling and detailed laboratory analysis of the water constituents and concentration. The analysed reservoirs covered a wide range, from open drinking water reservoirs to polluted hypertrophic ones. Classification of reservoirs by their water quality is achieved by the study of the spectral distribution of the reflected solar radiation, taking into account the atmospheric effects. The techniques included chromatic coordinates analysis and principal component analysis. An optical model of the volume reflectance of the water body was constructed, based on the radiative transfer equation which includes the scattering properties of the main water constituents. Numerical simulations, based on the model, support the experimental findings regarding the reservoir c1assilication according to water quality and composition.     

Satellite remote sensing of primary production

Leaf structure and function are shown to result in distinctive variations in the absorption and reflection of solar radiation from plant canopies. The leaf properties that determine the radiation-interception characteristics of plant canopies are directly linked to photosynthesis, stomatal resistance and evapotran-spiration and can be inferred from measurements of reflected solar energy. The effects of off-nadir viewing and atmospheric constituents, coupled with the need to measure changing surface conditions, emphasize the need for multitemporal measurements of reflected radiation if primary production is to be estimated     

Satellite remote sensing of groundwater: Quantitative modelling and uncertainty reduction using 6S atmospheric simulations

Remote sensing has been successfully used in the exploration of natural resources such as groundwater. Satellite data with different spatial, spectral and temporal characteristics have been evaluated for their potential use in groundwater detection in arid and semi-arid regions. However, distortions and noises caused by the presence of the atmosphere in the radiometric wave transmission become serious impediments for quantitative analysis and measurement work. In the present study, oasis and desert ecotone (ODE), a nonlinear ecological transitional belt, in Qira, Xinjiang Uyghur Autonomous Region of China was selected for this research. The ODE boundary was defined on the basis of widely collected information from the study area, including environmental, sociological and economic data. A model of groundwater level distribution using remote sensing (GLDRS), which empirically relates satellite sensor spectral radiance with groundwater level, is developed via in situ measurement and field examination of soil moisture and groundwater. Next, the second simulation of the satellite signal in the solar spectrum (6S), a code enabling simulations of radiative transfer process on the Sun-target-sensor path, is used to reduce uncertainties in the calculation of groundwater level. Then, groundwater level is evaluated using 6S atmospheric corrected and uncorrected Landsat-7 Enhanced Thematic Mapper (ETM)+ images respectively along with isochronous meteorological information. Greater correspondence between field examined and satellite monitoring data is obtained from 6S atmospheric corrected image (correlation coefficient is 0.94) than from the uncorrected image (correlation coefficient is 0.83).     

Multi-parameter regularization method for atmospheric remote sensing

The iteratively regularized Gauss-Newton algorithm with simple bounds on the variables is extended to the multi-parameter case. The global regularization matrix is computed by using the L-curve method for a sequence of one-parameter regularization problems. Numerical results concerning the joint retrieval of O₃ and NO₂ profiles using the scattered light from the limb of the atmosphere are presented.     

Iteratively regularized Gauss-Newton method for atmospheric remote sensing

In this paper we present an inversion algorithm for nonlinear ill-posed problems arising in atmospheric remote sensing. The proposed method is the iteratively regularized Gauss-Newton method. The dependence of the performance and behaviour of the algorithm on the choice of the regularization matrices and sequences of regularization parameters is studied by means of simulations. A method for improving the accuracy of the solution when the identity matrix is used as regularization matrix is also discussed. Results are presented for atmospheric temperature retrievals from a far infrared spectrum observed by an airborne uplooking heterodyne instrument.     

Satellite and surface-based remote sensing of Saharan dust aerosols

The spatial and temporal characteristics of dust aerosols and their properties are assessed from satellite and ground-based sensors. The spatial distribution of total column aerosol optical depth at 550 nm (AOD) from the Moderate Resolution Imaging SpectroRadiometer (MODIS) coupled with top of atmosphere Clouds and the Earth's Radiant Energy System (CERES) shortwave fluxes are examined from the Terra satellite over the Atlantic Ocean. These data are then compared with AOD from two Aerosol Robotic Network (AERONET) ground-based sun photometer measurement sites for nearly six years (2000-2005). These two sites include Capo Verde (CV) (16°N, 24°W) near the Saharan dust source region and La Paguera (LP) (18°N, 67°W) that is downwind of the dust source regions. The AOD is two to three times higher during spring and summer months over CV when compared to LP and the surrounding regions. For a unit AOD value, the instantaneous TOA shortwave direct radiative effect (DRE) defined as the change in shortwave flux between clear and aerosol skies for CV and LP are − 53 and − 68 Wm^− 2 respectively. DRE for LP is likely more negative due to fall out of larger particles during transport from CV to LP. However, separating the CERES-derived DRE by MODIS aerosol effective radii was difficult. Satellite and ground-based dust aerosol data sets continue to be useful to understand dust processes related to the surface and the atmosphere.     

Definitions of atmospheric radiance and transmittances in remote sensing

Some confusion sometimes arises about atmospheric radiance and transmittances in the remote sensing literature. We propose an appropriate terminology for use in problems of remote sensing of ground-reflected solar radiation through a turbid atmosphere.     

Effect of atmospheric conditions on remote sensing of vegetation parameters

The effect of atmospheric conditions on remote sensing of vegetation parameters is examined by studying variations of the ratio of the simulated radiance in the 0.76–0.90 μm [TM4 band of the multispectral scanner system (thematic mapper) of the Landsat-D satellite] to that in 0.52–0.60 μm (TM2 band), as a function of the leaf water content of blue grama grass plots. Information on the spectral flux reflectance, and on the leaf water content for 35 different blue grama grass plots is taken from Tucker and Maxwell (1976). This ratio is computed, after taking into account all orders of scattering, for several observational parameters, and for three different atmospheric models with common absorbing gases and with nil, moderate, and very large amount of aerosols (haze particles). The Lambert's law of reflection is assumed for the vegetation canopies. The slope and the ratio-axis intercept of the least-square-fit straight line through the ratio versus leaf water content data points are found to exhibit a very significant dependence on the atmospheric aerosol content, solar zenith angle, position of the observer, and on the direction of observation when the observer is not located at the ground level.     

水体遥感影像处理的研究

研究了遥感图片的水体类型解译识别问题。水体图形的准确提取是遥感图片解译识别的关键问题。综合应用敏感因子组合法、种子点增长法和数学形态学的方法提取水体,在对提取的水体图像分析基础上,进一步确定了特征向量和采用模糊识别理论进行识别,并对识别结果进行了分析,实验结果显示提取的水体获得较满意的识别效果。     

Thermal remote sensing of near-surface water vapor

In this study, four approaches to estimate atmospheric water vapor from Advanced Very High Resolution Radiometer (AVHRR) observations were tested with data from the Boreal Ecosystem–Atmosphere Study (BOREAS) and the Oklahoma Mesonetwork. The approaches studied were (i) the split-window difference of the thermal channels (Channel 4: 10.3–11.3 μm and Channel 5: 11.5–12.5 μm) by Dalu [Int. J. Remote Sens. 7 (1986) 1089.], (ii) the ratio of variances by Jedlovec [J. Appl. Meteorol. 29 (1990) 863.], (iii) the regression slope by Goward et al. [Ecol. Appl. 4 (1994) 322.], and (iv) a look-up table derived from radiative transfer model output. Although these techniques were primarily developed to estimate total column precipitable water, we used them to estimate near-surface water vapor, within a few meters of the surface. Near-surface water vapor is needed for hydrologic and biospheric modeling. Analysis showed the total column precipitable water to be highly correlated (r²=.79) with near-surface absolute humidity for clear-sky conditions at the BOREAS and the Oklahoma study sites. Correlation of all the retrieval techniques with ground observations was very low. For the split-window approach, water vapor can only be estimated on a per pixel basis and is ambiguous for anything but a single site. The regression slope and variance ratio techniques showed very little correlation with ground observations with r²=.02 when compared with data from BOREAS, and .17 for the variance ratio and .24 for the regression slope when compared with Mesonet data. The spatial variability of water vapor across the landscape hampers the use of these contextual approaches. The highest correlation was for the look-up table approach, with r²=.36 when compared with data from the BOREAS site. The look-up table was applied using AVHRR Channels 4 and 5 brightness temperatures, surface temperature, and near-surface air temperature. Surface temperature and air temperature were both estimated from the satellite readings. Combining the satellite data with air temperature measured at meteorological ground stations improved the correlation to .50. The relatively low r² values were at least partly due to spatial and temporal mismatches between surface and satellite measurements. Simulation of Moderate Resolution Imaging Spectrometer (MODIS) thermal Channels 29 (8.4–8.7 μm), 31 (10.78–11.28 μm), and 32 (11.77–12.27 μm) brightness temperatures showed that Channels 31 and 32 provide similar information as AVHRR Channels 4 and 5. The additional thermal information provided by Channel 29 shows promise for future water vapor detection efforts.     

Microwave remote sensing of plant water stress

The sensitivity of microwave (MW) emission to physical conditions of vegetation has been assessed by means of ground-based microwave and infrared radiometers. Measurements on corn and wheat have shown an inverse correlation between the normalized brightness temperature (T_N) from the Ka band (36 GHz) and the atmospheric water vapor pressure (VP) at the top of vegetation. From this observation, we show that a crop water stress index can be calculated by means of down-looking MW sensors, provided air temperature is known. A polarization index (PI) dependent only on microwave measurements was shown to be related to crop water stress.     

In place merging of satellite based atmospheric water vapour measurements

Atmospheric water vapour plays a key role in the climatology of the Earth. It has traditionally been measured using radiosondes for reasons of instrumental simplicity but these offer limited opportunities for spatial and continuous measurements of dynamic water vapour changes over large areas of the Earth's atmosphere. Efforts have recently turned to using satellite remote sensing instruments with different spectral and spatial capabilities to derive measurements of total water vapour content in atmospheric columns or simply precipitable water. The merging of remote sensing data with different spectral and spatial capabilities can result in large biases when independent measurements are not nested correctly to produce the final product. Consequently, such merging of data must take into account the intrinsic time dynamics of measured parameters. In this paper, the impact of atmospheric water vapour dynamics on the merging of satellite-based retrieval of precipitable water estimates is investigated by comparing independent measurements obtained at different spatial resolutions from the High Resolution Infrared Sounder (HIRS) and the Advanced Very High Resolution Radiometer (AVHRR). Correlations are used to infer optimal merging parameters depending on the observational conditions. The authors conclude that the merging technique reproduces HIRS-based retrievals in cloud-free and partly cloudy locations from AVHRR soundings.     

Variation of atmospheric optical depth for remote sensing radiance calculations

An analysis of the radiative transfer processes in the atmosphere suggests that the ratio between the diffuse radiance flux and the directly transmitted flux may be a useful parameter for investigating the variation of atmospheric optical depth. The radiative parameter is derived for an observer at the top of the atmosphere in the position of a satellite. One of the important variables required in the computations of the radiative parameter is the aerosol phase function which can be satisfactorily fitted by the two-term Henyey-Greenstein function. When the surface reflectance equals zero, representing the atmospheric radiance alone without any influence from an oceanic surface, the study shows that the variation of the optical depth is associated with the changing of aerosol size distribution. The optical depth increases with the presence of large aerosol particles. The results indicate that inhomogeneities in aerosol optical properties, such as the variation of the scattering phase function due to the changes in aerosol size distribution, can affect the magnitude of the optical depth.     

The Tethered Satellite System as a new remote sensing platform

Abstract

The Tethered Satellite System is a joint US-Italian programme for the study and design of a sub-satellite deployed from the Shuttle by means of a long tether. Two preliminary demonstration flights (the first upwards, the second downwards to 100 km) have been proposed to conduct several scientific and engineering experiments. Furthermore an improved multipurposes tethered platform is under study, to be deployed by future space stations at different low altitudes, where free-flying satellites have a short lifetime. Starting from the existing characteristics, the authors analyse the improvements necessary to achieve the performance required for remote sensing operational missions. Several authors have shown that, in station keeping, a tether control allows the longitudinal and in-plane/out-of-plane oscillations to be damped. Therefore the sub-satellite position can be accurately controlled and measured, also using active tracking systems during the short observation time interval. As far as attitude is concerned, a deeper study is needed to identify the attitude measurement and control systems of the future multipurpose platform. In order to obtain a design value representing the most stringent condition, the along-track stereoscopic coverage using linear arrays is considered. After an analysis of the attitude stability rate required by STEREOSAT and MAPSAT, and of the geometric errors involved in this kind of observation, it is shown that a value of 10^?4deg/s or better will allow the multipurpose tethered platform to be used for different remote sensing missions.     

Satellite remote sensing and wave studies into the 1990s

Abstract

One of the fundamental problems in studying ocean waves has been the difficulty of obtaining spatial wave data. Conventional instrumentation, such as waverider buoys or wave staffs, give data in the form of a time series of surface elevation at a fixed point on the sea surface. This enables us to study the temporal statistics of waves at that point, but does not allow us to answer the basic question of over what area of the sea surface are the results obtained valid. The advent of both airborne and satellite-borne instruments for measuring waves and their ability to give spatial data, has opened up the possibility of studying the spatial statistics of waves. Indeed, using satellite data it is feasible to do this on a global scale, as demonstrated by the experience gained from the short-lived Seasat mission. In this paper we consider which scientific questions about sea surface waves might be addressed using existing and proposed satellite remote sensing techniques. We also discuss the application of remotely-sensed wave data to practical problems, many of which require temporal statistical information. As there are limitations as to the type of wave data that may be obtained by present remote sensing techniques, we will discuss the implications of these for waves studies and try to assess what developments are necessary to overcome them. Finally, the authors' opinion is that if the data are to be fully exploited, it necessary to address the issue of facilities for the storage, retrieval and analysis of the data. The quantity of wave data that will be obtained from forthcoming and proposed satellite missions will far exceed that obtained by conventional techniques, and the question of data handling becomes crucial. To this end we briefly discuss the issues involved in algorithm development, calibration and validation, data banking, retrieval and analysis of the data.     

Error analysis and minimum bound method for atmospheric remote sensing

In this study, we present an error analysis for Tikhonov regularization in a semi-stochastic setting. The analysis is carried out in such a way that it can be applied to any kind of inverse problem in atmospheric remote sensing. A method for selecting the optimal regularization parameter relying on the minimization of an estimator of the bound of the error between the first iterate and the exact solution is also discussed. Numerical simulations are performed for NO₂ retrieval from SCIAMACHY limb scatter measurements.     

Satellite remote sensing of tropical forest canopies and their seasonal dynamics

Seasonal patterns of tropical evergreen forest green-up in Amazonia, corresponding to drought and the dry season, have recently been detected by the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) products of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. These observations provide additional evidence for solar radiation as the primary limiting factor regulating wet-tropical ecosystem processes. However, in situ structural mechanisms for forest canopy green-up are unclear and frequently inconsistent with observations. Here, we investigate the signal of seasonal green-up at several intensively measured sites, applying a rigorous series of filters to minimize error from atmospheric contamination that is common in tropical regions. We find that, while satellite-observed forest seasonality is sensitive to data-quality filtering, statistical noise reduction and spatial averaging, the signal is robust at sites where field observations are available, and in particular for the EVI. For the sites where field data are unavailable, it appears that additional filters to those commonly used to remove cloud effects and aerosols also reduce the seasonal magnitude of the LAI. These findings imply that seasonal tropical evergreen forest green-up remains sensitive to the methodology used in removing seasonal contamination from atmospheric conditions and that further field measurements and comparisons to remote sensing are required to reduce this uncertainty.     

Retrieval of atmospheric water vapour using a ground-based single-channel microwave radiometer

Estimates of the amount of atmospheric water vapour derived from algorithms for a ground-based single-channel (21.0 GHz) microwave radiometer have been investigated. Ten datasets covering 44 days were used to derive the methods and two other sets (in total 32 days) were used to assess the quality of these. It is shown how the rms estimation error can be reduced by recognizing the rapid variations in sky brightness temperatures during periods when cloud liquid is present. Data was either discarded, guided by the variability, or an adaptive Kalman filter was applied with different parameter values for different degrees of variability. The resulting estimates were compared to the estimates obtained from a dual-channel algorithm (21.0 and 31.4 GHz), which were used as reference. The amount of water vapour was represented as the ‘wet delay’, the excess radio path length due to the atmospheric water vapour. Applying the Kalman filter to the single-frequency estimates reduced the wet delay rms error from 20 mm to 9 and 14 mm for the two datasets. Further reduction of the rms error was achieved by the removal of data in periods with high variability; discarding about 40% of the data led to rms errors of 5 and 7 mm for the two datasets.     

Satellite passive microwave remote sensing for monitoring global land surface phenology

Vegetation phenology characterizes seasonal life-cycle events that influence the carbon cycle and land-atmosphere water and energy exchange. We analyzed global phenology cycles over a six year record (2003-2008) using satellite passive microwave remote sensing based Vegetation Optical Depth (VOD) retrievals derived from daily time series brightness temperature (T_b) measurements from the Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and other ancillary data inputs. The VOD parameter derives vegetation canopy attenuation at a given microwave frequency (18.7 GHz) and varies with canopy height, density, structure and water content. An error sensitivity analysis indicates that the retrieval algorithm can resolve the VOD seasonal cycle over a majority of global vegetated land areas. The VOD results corresponded favorably (p < 0.01) with vegetation indices (VIs) and leaf area index (LAI) information from satellite optical-infrared (MODIS) remote sensing, and phenology cycles determined from a simple bioclimatic growing season index (GSI) for over 82% of the global domain. Lower biomass land cover classes (e.g. savannas) show the highest correlations (R = 0.66), with reduced correspondence at higher biomass levels (0.03 < R < 0.51) and higher correlations for homogeneous land cover areas (0.41 < R < 0.83). The VOD results display a unique end-of-season signal relative to VI and LAI series, and may reflect microwave sensitivity to the timing of vegetation biomass depletion (e.g. leaf abscission) and associated changes in canopy water content (e.g. dormancy preparation). The VOD parameter is independent of and synergistic with optical-infrared remote sensing based vegetation metrics, and contributes to a more comprehensive view of land surface phenology.     

An approach for correcting inhomogeneous atmospheric effects in remote sensing images

Two contextual-based approaches for correcting first order atmospheric effects due to atmospheric inhomogeneity and adjacency effect are described. The first method is a modification of the restoration method for the adjacency effect suggested by Richter. The second method is an adaptation of Stenberg's rolling ball algorithm using the mathematical morphology transformation. Evaluation of the proposed method was carried out by noting classification accuracy on the basis that an increase in classification accuracy reflects in improved image quality. Results show that the average accuracy of classification of a scene from Malaysia, with 13 ground truth classes was as follows: (a) uncorrected--86%, (b) adjacency correction--86%, and (c) rolling ball correction--89%. It is clear from the results that the rolling ball method of correction does yield an increase in accuracy although not as substantial as in this case, and there is no reason to doubt that this method could be used to correct a wide variety of images that are affected by inhomogeneity of the atmosphere.