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.     

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.     

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     

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.     

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.     

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。     

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.     

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.     

Merging complementary remote sensing datasets in the context of snow water equivalent reconstruction

Time series of snow covered area (SCA) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper (ETM+) were merged with a spatially explicit snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km²). A linear optimization scheme was used to derive SCA estimates that preserve the statistical moments of the higher spatial resolution (i.e. 30 m) ETM+ data and resolve the superior temporal signal (i.e. ∼ daily) of the MODIS data. It was found that merging the two SCA products led to an 8% decrease and an 18% increase in the basinwide SWE in 2001 and 2002, respectively, compared to the SWE estimated from ETM+ only. Relative to SWE simulations using only ETM+ data, the hybrid SCA estimates reduced the mean absolute SWE error by 17 and 84% in 2001 and 2002, respectively; errors were determined using intensive snow survey data and two separate methods of scaling snow survey field measurements of SWE to the 1-km model pixel resolution. SWE bias for both years was reduced by 49% and skewness was reduced from − 0.78 to 0.49. These results indicate that the hybrid SWE was closer to being an unbiased estimate of the measured SWE and errors were distributed more normally. The accuracy of the SCA estimates is likely dependent on the vegetation fraction.     

Microwave remote sensing of soil moisture: a comparison of results from different truck and aircraft platforms

A comparison of the soil moisture response of three 1–4 GHz radiometers from truck and aircraft platforms at a variety of lest sites indicates that microwave remote sensing of bare soil produces repeatable and quantifiable results regardless of geographic location and sensor system used. The combined microwave sensitivity of these data sets appears to be of the order of ? 3–4deg?K per 1 per cent change in volumetric soil moisture for L-band wavelengths ( ? 2–7 deg K per 1 per cent soil moisture if watersheds characterized by vegetation and surface roughness are included). With the addition of appropriate algorithms to handle the effect of roughness and vegetation, these results demonstrate the potential of microwave remote sensing for estimating soil moisture over large areas.     

遥感对地观测技术

着重分析了与计算机信息处理有关的遥感对地观测技术中的信息处理技术,并以GPS、RS、GIS一体化信息技术为例,阐述了空间集成型技术的概念、理论、技术构成、特点,展望了空间信息集成技术的发展前景。     

Signature extension in remote sensing

This paper considers the problem of signature extension in remote sensing. Signature extension is a process of increasing the spatial-temporal range over which a set of training statistics can be used to classify data without significant loss of recognition accuracy.Methods are developed for the selection of segments for obtaining the training data. Selection of the number of segments is treated as the problem of expansion of rectangular matrix with basis matrices. Computational algorithms based on mean minimum square estimation error are developed for the selection of best segments. Furthermore, a combinatorial algorithm for generating all possible r combinations of S in Sc_r steps with a single change at each step is presented.     

Hydrological consistency using multi-sensor remote sensing data for water and energy cycle studies

A multi-sensor/multi-platform approach to water and energy cycle prediction is demonstrated in an effort to understand the variability and feedback of land surface and atmospheric processes over large space and time scales. Remote sensing-based variables including soil moisture (from AMSR-E), surface heat fluxes (from MODIS) and precipitation rates (from TRMM) are combined with North American Regional Reanalysis derived atmospheric components to examine the degree of hydrological consistency throughout these diverse and independent hydrologic data sets. The study focuses on the influence of the North American Monsoon System (NAMS) over the southwestern United States, and is timed to coincide with the SMEX04 North American Monsoon Experiment (NAME). The study is focused over the Arizona portion of the NAME domain to assist in better characterizing the hydrometeorological processes occurring across Arizona during the summer monsoon period. Results demonstrate that this multi-sensor approach, in combination with available atmospheric observations, can be used to obtain a comprehensive and hydrometeorologically consistent characterization of the land surface water cycle, leading to an improved understanding of water and energy cycles within the NAME region and providing a novel framework for future remote observation and analysis of the coupled land surface-atmosphere system.     

Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia

Changes in species composition and diversity are the inevitable consequences of climate change, as well as land use and land cover change. Predicting species richness at regional spatial scales using remotely sensed biophysical variables has emerged as a viable mechanism for monitoring species distribution. In this study, we evaluate the utility of MODIS-based productivity (GPP and EVI) and surface water content (NDSVI and LSWI) in predicting species richness in the semi-arid region of Inner Mongolia, China. We found that these metrics correlated well with plant species richness and could be used in biome- and life form-specific models. The relationships were evaluated on the basis of county-level data recorded from the Flora of Inner Mongolia, stratified by administrative (i.e., counties), biome boundaries (desert, grassland, and forest), and grouped by life forms (trees, grasses, bulbs, annuals and shrubs). The predictor variables included: the annual, mean, maximum, seasonal midpoint (EVI_mid), standard deviation of MODIS-derived GPP, EVI, LSWI and NDSVI. The regional pattern of species richness correlated with GPP_SD (R² = 0.27), which was also the best predictor for bulbs, perennial herbs and shrubs (R² = 0.36, 0.29 and 0.40, respectively). The predictive power of models improved when counties with > 50% of cropland were excluded from the analysis, where the seasonal dynamics of productivity and species richness deviate patterns in natural systems. When stratified by biome, GPP_SD remained the best predictor of species richness in grasslands (R² = 0.30), whereas the most variability was explained by NDSVI_max in forests (R² = 0.26), and LSWI_avg in deserts (R² = 0.61). The results demonstrated that biophysical estimates of productivity and water content can be used to predict plant species richness at the regional and biome levels.