Microwave remote sensing of agricultural crops in canada

The objective of this paper is to review current knowledge concerning synthetic aperture radar applications to crops in Canada. Following a brief overview of studies during SURSAT (1978-1980) and RADARSAT (1981 and later) projects, several issues are considered. These include crop-classification accuracies achieved with SAR or SAR and VIR data at various sites, the importance of crop and soil parameters affecting SAR images, the procedures for digital SAR image analysis and the relationship between airborne SAR data and future satelliteSAR data. Finally, recent developments of new ground and airborne microwave instruments to be used in agricultural studies are described.     

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.     

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.     

Satellite remote sensing of atmospheric water vapour

Abstract

The Advanced Very High Resolution Radiometer (AVHRR-2) has two channels in the 10-13/mi window region. These channels are used for remote sensing of the sea surface temperature corrected for atmospheric absorption. The brightness temperature difference between the channels can be directly related to the atmospheric absorption due to water vapour. The problem of water-vapour retrieval from satellite data is examined in detail. The best evaluation of the water-vapour content is obtained from the spectrometric data of the Infrared Interferometer Spectrometer (IRIS), with an error of about + 3kg/m². It is, however, feasible to obtain the water-vapour content from AVHRR data with an algorithm derived from radiative transfer model simulations. The retrieved water vapour has an error of ±5kg/m² when compared with ship data. It is possible to use the remotely sensed water vapour for the inference of the boundary-layer structure. The information is, however, limited for water vapour contained near the surface.     

Acoustic remote sensing of internal waves in shallow water

Abstract

Acoustic continuous-wave measurements are presented, which were carried out during June 1978 in the North Sea at a water depth of 30 m. The source was operated at a frequency of 295 Hz and the signal was recorded by two hydrophones at a distance of 1100 m south and east, respectively, from the source. The observed fluctuations of the acoustic amplitude and phase are analysed for periods from 10 min up to 2 hours and are compared with simultaneously measured currents. A theory is developed with the objective of relating the acoustic fluctuations to motions of the sea. For the considered periods and the density stratification during the experiment it is assumed that the observed currents are due to internal waves. The theoretical results agree well with the measurements.     

Mapping the effects of water stress on Sphagnum: Preliminary observations using airborne remote sensing

Remote sensing of near-surface hydrological conditions within northern peatlands has the potential to provide important large-scale hydrological information regarding ecological and carbon-balance processes occurring within such systems. This article details how field knowledge of the spectral properties of Sphagnum spp., airborne remote sensing data and a range of image analysis approaches, may be combined to provide a suitable proxy for near-surface wetness. Co-incident field and airborne remote sensing data were acquired in May and September 2002 over an important UK raised bog (Cors Fochno). A combination of laboratory-tested NIR and SWIR water-based and biophysical spectral reflectance indices were applied to field and airborne reflectance spectra of Sphagnum pulchrum to elucidate changes in near-surface moisture conditions. Field results showed significant correlations between water-based indices (moisture stress index (MSI) and floating water band indices (fWBI₉₈₀ and fWBI₁₂₀₀₎)) and measures of both near-surface volumetric moisture content (VMC) and water-table position. Spectral indices formulated from the NIR (fWBI₉₈₀ and fWBI₁₂₀₀) proved to be the most useful for indicating near-surface wetness across the widest range of moisture conditions because of their ability to penetrate deeper into the Sphagnum canopy. Correlations between a biophysical index based upon chlorophyll content and both hydrological measures were not significant, possibly due to relatively high levels of surface wetness at the field site in both May and September. S. pulchrum lawns were successfully located and mapped from airborne imagery using the mixed tuned match filtering (MTMF) algorithm. Importantly, MSI derived from airborne data was significantly correlated with both field moisture and the water-table position. Relationships between measures of near-surface wetness and the MSI for naturally heterogeneous canopies were, however, found to be weaker for airborne imagery than for associated field data. This is likely to be a result of the formulation of the MSI itself and the possible preferential detection of “wetter” pixels within the imagery. This effectively reduced the ability of MSI to detect subtle changes in near-surface wetness under high moisture conditions, but would not impede the use of the index under drier conditions. Results from the field data suggest that indices formulated from the NIR may be more suitable for detailed estimations of near-surface and surface wetness at the landscape-scale although reliable hyperspectral data are required to test fully the performance of such indices. The relative merits of using such an approach to determine near-surface hydrological conditions across entire peatland complexes are also discussed.     

Shallow water bathymetry using integrated airborne multi-spectral remote sensing

An integrated low cost airborne multi-spectral remote sensing system is described and evaluated for remote sensing for shallow water bathymetry. The system consists of: two 35mm motor driven reconnaissance cameras using colour and colour infrared film. Three optically filtered (including removable internal IR cut-off filters), electronically shuttered CCD progressive scan cameras (Sony XC-7500) integrated into an airborne direct digital recording system using a PC processor, a 32-bit RGB analogue to digital conversion card and Zip disk storage. Two CCD based imaging spectrometers providing approximately 10nm bandwidth spectral data across the CCD spectrum (400nm to 1000nm). These CCD cameras were used with a variable interference filter fixed in front of the sensor surface. This provided a 'rainbow' image of the ground varying across the image from 400nm to 700nm (visible) and 700nm to 1000nm. Field studies were undertaken to evaluate the performance of digital multi-spectral (DMV) imagery, supplementary reconnaissance photography (SRP) and VIFIS imaging spectrometry for mapping shallow water bathymetry. The results indicate good performance in shallow water and suggest that with further refinement the system could be used to give a quick comprehensive estimate of shallow water depths     

基于RIA的遥感信息系统的压力测试实现

遥感信息系统相对于传统的软件系统有自己的特点,随着遥感信息系统的广泛应用,它的性能也越来越引起人们的广泛关注。Silverlight是极为热门的一种RIA开发技术,一个使用Silverlight技术的遥感信息系统通常是由大量终端用户同时使用的,传统的手工测试已经不能快速有效地解决问题。提出了一种借助自动化测试工具LoadRunner11.00进行压力测试的方法,对某个使用Silverlight技术的遥感信息系统进行自动化压力测试,并通过分析测试结果数据来调整和优化软件系统的性能。     

Barriers to adopting satellite remote sensing for water quality management

Sustainable practices require a long-term commitment to creating solutions to environmental, social, and economic issues. The most direct way to ensure that management practices achieve sustainability is to monitor the environment. Remote sensing technology has the potential to accelerate the engagement of communities and managers in the implementation and performance of best management practices. Over the last few decades, satellite technology has allowed measurements on a global scale over long time periods, and is now proving useful in coastal waters, estuaries, lakes, and reservoirs, which are relevant to water quality managers. Comprehensive water quality climate data records have the potential to provide rapid water quality assessments, thus providing new and enhanced decision analysis methodologies and improved temporal/spatial diagnostics. To best realize the full application potential of these emerging technologies an open and effective dialogue is needed between scientists, policy makers, environmental managers, and stakeholders at the federal, state, and local levels. Results from an internal US Environmental Protection Agency qualitative survey were used to determine perceptions regarding the use of satellite remote sensing for monitoring water quality. The goal of the survey was to begin understanding why management decisions do not typically rely on satellite-derived water quality products.     

Reconciling the global terrestrial water budget using satellite remote sensing

Recent retrievals of multiple satellite products for each component of the terrestrial water cycle provide an opportunity to estimate the water budget globally. In this study, we estimate the water budget from satellite remote sensing over ten global river basins for 2003-2006. We use several satellite and non-satellite precipitation (P) and evapo-transpiration (ET) products in this study. The satellite precipitation products are the GPCP, TRMM, CMORPH and PERSIANN. For ET, we use four products generated from three retrieval models (Penman-Monteith (PM), Priestley-Taylor (PT) and the Surface Energy Balance System (SEBS)) with data inputs from the Earth Observing System (EOS) or the International Satellite Cloud Climatology Project (ISCCP) products. GPCP precipitation and PM (ISCCP) ET have less bias and errors over most of the river basins. To estimate the total water budget from satellite data for each basin, we generate merged products for P and ET by combining the four P and four ET products using weighted values based on their errors with respect to non-satellite merged product. The water storage change component is taken from GRACE satellite data, which are used directly with a single pre-specified error value. In the absence of satellite retrievals of river discharge, we use in-situ gauge measurements. Closure of the water budget over the river basins from the combined satellite and in-situ discharge products is not achievable with errors of the order of 5-25% of mean annual precipitation. A constrained ensemble Kalman filter is used to close the water budget and provide a constrained best-estimate of the water budget. The non-closure error from each water budget component is estimated and it is found that the merged satellite precipitation product carries most of the non-closure error.     

Application of eigenvector analysis to remote sensing of coastal water quality

Abstract

The use of algorithms incorporating radiance information from one or a number of wavelengths is a standard technique for detecting the concentration and distribution of water quality parameters in coastal and open ocean waters. It has become clear, however, that in a turbid dynamic coastal environment there is no one algorithm applicable for all times, seasons or area because the composition of the suspended material variescontinually. Consequently site specific algorithms have been proposed. Results of an eigenvector analysis of radiance spectra and sea-truth data collected as part of airborne remote sensing campaigns in 1984 and 1985 are presented. The eigenvectors of radiance data are shown to be dependent on the type and relative concentrations of material in suspension. The technique is shown to have great potential for the identification of the composition of material in suspension without recourse to sea-truth data. This information could be used as a criterion for selection of an appropriate algorithm.     

Regularized estimation of bathymetry and water quality using hyperspectral remote sensing

Coastal water mapping from remote-sensing hyperspectral data suffers from poor retrieval performance when the targeted parameters have little effect on subsurface reflectance, especially due to the ill-posed nature of the inversion problem. For example, depth cannot accurately be retrieved for deep water, where the bottom influence is negligible. Similarly, for very shallow water it is difficult to estimate the water quality because the subsurface reflectance is affected more by the bottom than by optically active water components.

Most methods based on radiative transfer model inversion do not consider the distribution of targeted parameters within the inversion process, thereby implicitly assuming that any parameter value in the estimation range has the same probability. In order to improve the estimation accuracy for the above limiting cases, we propose to regularize the objective functions of two estimation methods (maximum likelihood or ML, and hyperspectral optimization process exemplar, or HOPE) by introducing local prior knowledge on the parameters of interest. To do so, loss functions are introduced into ML and HOPE objective functions in order to reduce the range of parameter estimation. These loss functions can be characterized either by using prior or expert knowledge, or by inferring this knowledge from the data (thus avoiding the use of additional information).

This approach was tested both on simulated and real hyperspectral remote-sensing data. We show that the regularized objective functions are more peaked than their non-regularized counterparts when the parameter of interest has little effect on subsurface reflectance. As a result, the estimation accuracy of regularized methods is higher for these depth ranges. In particular, when evaluated on real data, these methods were able to estimate depths up to 20 m, while corresponding non-regularized methods were accurate only up to 13 m on average for the same data.

This approach thus provides a solution to deal with such difficult estimation conditions. Furthermore, because no specific framework is needed, it can be extended to any estimation method that is based on iterative optimization.     

Advances in plant nutrition diagnosis based on remote sensing and computer application

Neural Computing and Applications - Hyperspectral remote sensing, visible light remote sensing and canopy color analysis have been widely concerned for rapid diagnosis of crop growth and nutrition....     

Influence of fire severity on plant regeneration by means of remote sensing imagery

In this paper we analyse the interactions between fire severity (plant damage) and plant regeneration after fire by means of remote sensing imagery and a field fire severity map. A severity map was constructed over a large fire (2692 ha) occurring in July 1994 in the Barcelona province (north-east of Spain). Seven severity classes were assigned to the apparent plant damage as a function of burning intensity. Several Landsat TM and MSS images from dates immediately before and after the fire were employed to monitor plant regeneration processes as well as to evaluate the relationship with fire severity observed in situ . Plant regeneration was monitored using NDVI measurements (average class values standardized with neighbour unburned control plots). Pre-fire NDVI measurements were extracted for every plant cover category (7), field fire severity class (7), and spatial cross-tabulation of both layers (33) and compared to post-fire values. NDVI decline due to fire was positively correlated with field fire severity class. Results show different patterns of recovery for each dominant species, severity class and combination of both factors. For all cases a significant negative correlation was found between damage and regeneration ability. This work leads to a better understanding of the influence of severity, a major fire regime parameter on plant regeneration, and may aid to manage restoration on areas burned under different fire severity levels.     

Applying support vector regression to water quality modelling by remote sensing data

This article applies a nonlinear machine learning method, support vector regression (SVR), to construct empirical models retrieving water quality variables using remote sensing images. Based on in situ measurements and high-resolution multispectral SPOT-5 (Satellite Pour l'Observation de la Terre) data, a fittest nonlinear function between input and output was obtained from this method, and SVR model parameters were selected automatically using a genetic algorithm (GA). The relationship between water quality variables – permanganate index (COD_Mn), ammonia-nitrogen (NH₃–N) and chemical oxygen demand (COD) – and spectral components of SPOT-5 data for the Weihe River in China was constructed by the proposed method. Spatial distribution maps for the three water quality variables were also developed. The results show that SVR can implement any nonlinear mapping, and produce better predictions than the traditional statistical multiple regression method, especially when samples are limited. With further testing, SVR can also be extended to hyperspectral remote sensing applications in the management of land and water resources.     

土壤水分变化量遥感反演算法

为获得作物生长发育期内任意时间段的土壤水分变化量空间分布,基于叶面积指数和生物量的土壤水分变化量遥感反演模型,利用ArcGIS Engine 9.3平台结合GDAL图像读写库设计和实现了相关算法,形成了从遥感影像数据到土壤水分变化量产品的处理流程。经研究区域的Landsat TM数据测试,反演算法运行稳定且计算结果符合实际,可为农业生产提供决策依据,为补充性灌溉提供指导。     

Global remote sensing of Trichodesmium

The maximum chlorophyll index (MCI) from the Medium Resolution Imaging Spectrometer (MERIS) satellite imager gives a robust indicator of the presence of a variety of floating slicks, near-surface vegetation, and intense surface plankton blooms. The index responds to the presence of surface slicks of Trichodesmium due to the ‘red edge’ increase in radiance with increasing wavelength near 700 nm. Global composite images of this index can be used to map the distribution of surface slicks of Trichodesmium, showing seasonal and long-term variations. The MCI also responds to the ‘red edge’ in the spectral signature of Sargassum. The two species share some areas of common occurrence and it is important to distinguish between them. We have developed spectral techniques of distinguishing between these two, and for discriminating a variety of other confusing targets that occur in different areas. We feel that MERIS MCI can be a useful tool in monitoring global Trichodesmium spatial distribution, and its short- and long-term variation.     

Microwave remote sensing of physically buried objects in the Negev Desert: implications for environmental research

We report remote detections of physically buried specularly reflecting objects using microwave radar at two sites: Ashalim and Tseelim in the northern region of the Negev Desert, Israel. These detections provide confirmation that microwave subsurface remote sensing is a genuine phenomenon. At Ashalim, a scatterometer operating in the P-band (441 MHz, 68 cm) was mounted on a cherry picker truck at a height of 8 m and used to detect two triangular aluminum mesh reflectors (forming a 1-m square area reflector) buried down to a depth of 8 cm in dry sand. At Tseelim, the same scatterometer was mounted on an airplane flying at an altitude of 70 m and used to detect 1-m square aluminum reflectors (each one submerged at a different location along the airplane flight path) buried down to a depth of 20 cm. The experimental results compare favorably with a theoretical model that incorporates radar absorption effects arising in the sandy subsurface layer and radar interference effects arising from phase differences between reflections from the surface and buried reflector. The theoretical modeling also predicts the detection of a subsurface reflector down to a depth of about 4.4 m. This experiment and the associated modeling approach is the first of a series of planned experiments, which we outline for the detection and the theoretical evaluation of buried reflectors using remote microwave and VHF radar. We identify potential subject areas for environmental research.     

Microwave sensing from satellites

Microwave data from the ESMR sensor of the NIMBUS-6 satellite is used to obtain (i) rainfall rates during a tropical cyclone over the northern part of Western Australia, (ii) wind speed over the surface of the waters of the coastal regions of the southern part of Western Australia, and (iii) the microwave emissivity over the land surface of the southern part of Western Australia.