The development of local solar irradiance for outdoor computer graphics rendering

Atmospheric effects are approximated by solving the light transfer equation, LTE, of a given viewing path. The resulting accumulated spectral energy (its visible band) arriving at the observer’s eyes, defines the colour of the object currently on the line of sight. Due to the convenience of using a single rendering equation to solve the LTE for daylight sky and distant objects (aerial perspective), recent methods had opt for a similar kind of approach. Alas, the burden that the real-time calculation brings to the foil had forced these methods to make simplifications that were not in line with the actual world observation. Consequently, the results of these methods are laden with visual-errors. The two most common simplifications made were: i) assuming the atmosphere as a full-scattering medium only and ii) assuming a single density atmosphere profile. This research explored the possibility of replacing the real-time calculation involved in solving the LTE with an analytical-based approach. Hence, the two simplifications made by the previous real-time methods can be avoided. The model was implemented on top of a flight simulator prototype system since the requirements of such system match the objectives of this study. Results were verified against the actual images of the daylight skies. Comparison was also made with the previous methods’ results to showcase the proposed model strengths and advantages over its peers

Calibration of AVHRR Data Generated by the Instrument on-board TIROS-N Using Ocean and Cloud Views

Remote sensing images of the Earth have been regarded by many as an important source of data for environmental studies. In order to produce good quality data, all known errors should be removed or eliminated before any of the data are used, especially for time-dependent applications. Over the years a number of post-launch calibration methods and procedures have been suggested and used, particularly for the later spacecraft in the series; each of these has some advantages and disadvantages over the others. This paper reviews a post-launch calibration method which has been established using ocean and cloud views as the main calibration targets; it then applies this method to data from the earlier spacecraft in the series which are no longer in operation

Estimating of regional evapotranspiration for arid areas using LANDSAT thematic mapper images data : a case study for grape plantation

In west southern mountains of Yemen grape crop has been considered as an important cash crop. Thus, water management for grape plantation in arid areas has become an important aspect to ensure a food produce. Except alfalfa, the water used by grape trees is greater than that of most crops. Conventional Point measurement of water needed by one Grape plantation cannot provide accurate estimate for all the orchards in a county. In fact, over a vast area, the point measurements technique is costly and unpractical. In this paper, a new approach is suggested to estimate detailed water requirement by grape plantation at a county scale. The proposed technique used LANDSAT-TM data and a modified SEBAL (Surface Energy Balance Algorithm for Land) to estimate evapotranspiration over grape plantation in wadi asser- Sana’a basin central Yemen mountains. The modified SEBAL model estimates evapotranspiration (ET) using the energy balance equations, for which the surface temperature and reflectance data from TM image data and metrological data from local weather station. The model calculates net radiation, soil and sensible heat flux, and evapotranspiration. Comparing the calculated results with those observed in point measurements in the field of Grape and alfalfa from the period 1995 to 1998 proves that the modified SEBAL also provides an accurate information. The average relative error between estimated and observed ET is 11.6%, and the average absolute error is 0.43 mm/day. This proposed technique has the potential to provide guidelines for various users, including government agencies on how to evaluate current water-usage schemes

Evapotranspiration estimation using a normalized difference vegetation index transformation of two satellites data in arid mountain areas

Evapotranspiration (ET) was estimated using a normalized difference vegetation index (NDVI) of satellite data on central Yemen Mountains. A procedure was developed which equated the index to crop coefficients. Evapotranspiration estimates for fields for three dates of Landsat Thematic Mapper data were highly correlated with ground estimates. Service area estimates using landsat Thematic Mapper (TM) and NOAA Advanced Very High Resolution Radiometer (AVHRR) data agreed well with estimates based on National Water Resources Authority (NWRA) gauging station data. Comparisons of ET results with traditional ET models show good agreement. Sensitivity analyses show that the model is accurate even without atmospheric correction

Decision support system for estimating actual crop evapotranspiration using remote sensing, GIS and hydrological models

Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate estimation is essential for hydrological studies. In the past, various estimation methods have been developed for different climatologically data, and the accuracy of these methods varies with climatic conditions. Therefore, Remote Sensing and GIS techniques with Hydrological Models are used to develop a friendly decision support system (DSS) for estimating of the Actual Crop ET. For given data availability and climatic conditions, the developed model estimates ET. The ET estimation methods are based on combination theory, radiation, temperature, and Remote Sensing methods; the model selects the best ET estimation method based on ASCE rankings. In order to evaluate the DSS, various tests were conducted with different data availability conditions for three climatological studies at the stations CAMA, NWRA, and Al-Irra. The decisions made by the model exactly matched the ASCE rankings. For the two climatic stations NWRA, and CAMA, ET values were estimated by all applicable methods using this models was developed for ERDAS Imagine and Arc-GIS software and were compared with the Penman-Monteith ET estimates, which were taken as the standard. Based on the weighted average standard error of the estimate, the modified SEBAL , and Biophysical model methods ranked first, respectively, for areas near the CAMA and NWRA stations. The SEBALID ranked first for Al-Irra station. The DSS model is developed as user tool for estimating ET under different data availability and climatic conditions

Pengenalan kepada mekanisma cerapan penderia remote sensing

Buku ini mengandungi beberapa bab iaitu pancaran suria, angkatap suria (solar constant), pembetulan kepada pancaran suria (irradiance), pancaran tenaga keatas dari sistem atmosfera bumi (upwelling), albedo, tenaga pancaran (radiance) yang dicerap oleh penderia diatas satelit, sumbangan dari sistem atmosfera bumi kepada radiance yang dicerap oleh penderia, hitungan pembetulan atmosfera secara ringkas, radiance laluan rayligh, radiance laluan aerosols, pancaran suria kebawah (global irradiance, penyeragaman bagi pancaran yang melalui atmosfera (normalization), penutup

The approaches for oasis desert vegetation information abstraction based on medium - Resolution Lansat TM image: A case study in desert wadi Hadramut Yemen

This paper present two issues namely; firest is oasis desert brightness inversion correction, and secondly, the classifying method of oasis desert vegetation through remote sensing image data., Oasis desert brightness inversion is known reduce the classification accuracy in medium-resolution images. In this study, the radiation correction and the brightness inversion adjustment models was analysis. The model's parameters were obtained from the image pixel values. The result of brightness inversion correction shows that the model can correct oasis desert brightness inversion. After brightness inversion correction, the vegetation's pixel value in brightness inversion area is similar with the pixel value of vegetation in other area. Brightness inversion correction increases classification accuracy. In the second part of this study, three methods are studied to derive oasis desert vegetations information, including vegetation index method, back propagation neural network method, and texture method. Three methods' classification accuracies are calculated and appraised. And a conclusion is drawn, which is the texture classification method is a good classification method. The accuracy of texture classification method can reach up to 82.31%

Estimation of evapotranspiration using fused remote sensing image data and energy balance model for improving water management in arid area

Remote sensing has proved to be very useful in the investigation of vegetation and hydrological monitoring, especially when studying vast areas. In this paper, the complement between two optical remote sensing data (Landsat TM and NOAA-AVHRR) and a Digital Elevation Model (DEM) is used to estimate hydrological parameters based on derived surface reflectance. These parameters which are used in the Modified Soil Energy Balance Algorithm for Land (M-SEBAL) model have been used to estimate net radiation, soil heat flux, sensible heat flux and evapotranspiration (ET) for Sana'a Basin in Yemen. The area is known for arid and semi-arid weather conditions with undulating topography. Image data from AVHRR on-board NOAA satellites with a large areal coverage, good temporal and spectral resolution are found to be very useful in generating some parameters required for the above process. However, the data have poor spatial resolution. On the other hand, image data from the Thematic Mapper on-board the Landsat satellite, with a high spatial and spectral resolution should be able to provide values for the parameters involved, but the area coverage is significantly reduced. This study has been carried out, using a data fusion technique in order to exploit the respective advantages of these two disparate sources of image data. A general framework is then proposed to generate ET maps for and and semi-arid regions. This is achieved by means of multi-temporal, multiresolution remote sensing data. Taking into account topographic effects, an attempt has also been made to incorporate DEM information for estimating the net radiation of the areas involved. An application for computing a daily ET map over Sana'a Basin, Yemen is presented. As a result, a daily ET map generated from meteorological observations was compared with estimated ET data simulated from remote sensing data. In conclusion, data from both remote sensing sources give reasonable values with the result from the TM being better than those obtained from the AVHRR. This is attributed to the differences in spatial resolution, in which TM data is higher than AVHRR. The fusion of the two shows improves spatial detail whilst maintaining the spectral signature close to the original

Downscaling albedo from moderate-resolution imaging spectroradiometer (MODIS) to advanced space-borne thermal emission and reflection radiometer (ASTER) over an agricultural area utilizing aster visible-near infrared spectral bands

Due to the limitation in spatial and spectral resolution, a few numbers of satellite data are applicable in field scale surface Albedo modeling. ASTER was an alternative for surface energy balance modeling, but since April 2008, shortwave detector has stopped recording due to the high-abnormal-temperature problem. Beside, temporal resolution of ASTER is insufficient for field-scale monitoring of surface parameters. Thus, this study was aimed first; to examine the capability of ASTER VNIR bands in estimation of surface Albedo and second, to downscale Albedo from MODIS to ASTER using Albedo resulted from ASTER VNIR bands. Combination of these two stages is expected to be a solution for field scale monitoring of surface Albedo from MODIS and ASTER data acquired after April 2008. Results confirmed that bands 1 and 3 which is available after April 2008 on ASTER data can be modeled for estimation of surface Albedo with less than 0.024% loss of information where land cover consist of soil and vegetation. From four downscaling methods, namely FSIM, PBIM, wavelet transfer and high pass filter (HPF) examined in this study, we also found that the most precise subpixel estimate were obtained by FSIM downscaling method (R2 = 0.96, RMSE = 0.01); although, the outputs of three other methods were significant