Estimating wheat partitioning coefficient using remote sensing and its coupling with a crop growth model
Yining TangYuanyuan PanYuejiao ZhaoXin LiJiaoyang HeCaili GuoHengbiao ZhengXia YaoTao ChengYan ZhuWeixing CaoYongchao Tian
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Vicarious techniques are used to provide supplemental radiometric calibration data for sensors with onboard calibration systems, and are increasingly important for sensors without onboard calibration systems. The Radiometric Calibration Test Site (RadCaTS) is located at Railroad Valley, Nevada. It is a facility that was developed with the goal of increasing the amount of ground-based radiometric calibration data that are collected annually while maintaining the current level of radiometric accuracy produced by traditional manned field campaigns. RadCaTS is based on the reflectance-based approach, and currently consists of a Cimel sun photometer to measure the atmosphere, a weather station to monitor meteorological conditions, and ground-viewing radiometers (GVRs) that are used the determine the surface reflectance throughout the 1 × 1-km area. The data from these instruments are used in MODTRAN5 to determine the at-sensor spectral radiance at the time of overpass. This work describes the RadCaTS concept, the instruments used to obtain the data, and the processing method used to determine the surface reflectance and top-of-atmosphere spectral radiance. A discussion on the design and calibration of three new eight-channel GVRs is introduced, and the surface reflectance retrievals are compared to in situ measurements. Radiometric calibration results determined using RadCaTS are compared to Landsat 7 ETM+, MODIS, and MISR.
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Given that many operational satellite sensors are not calibrated, while a handful of research sensors are, cross-calibration between the two types of sensor is a cost-effective means of calibration. A new method of sensor cross-calibration is demonstrated here using the Chinese Multi-channel Visible Infrared Scanning radiometer (MVIRS) and the US Moderate Resolution Imaging Spectrometer (MODIS). MVIRS has six channels, equivalent to the current National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and four additional ones for remote sensing of ocean colour and moisture. The MVIRS on-board China's polar-orbiting meteorological satellite (FY-1D) was launched on 15 May 2002 with an earlier overpass time than Terra. The sensor has no on-board calibration assembly. This study attempts to calibrate MVIRS against the well-calibrated MODIS, by taking a series of measures to account for their differences. Clear-sky measurements made from the two sensors in July-October 2002 were first collocated. Using the 6S radiative transfer model, MODIS reflectances measured at the top-of-the atmosphere were converted into surface reflectances. They were corrected to the viewing geometry of the MVIRS using the bidirectional reflectance distribution function (BRDF) measured on the ground. The spectral response functions of the two sensors were employed to account for spectral discrepancies. After these corrections, very close linear correlations were found between radiances estimated from the MODIS and the digital readings from the MVIRS, from which the calibration gains were derived. The gains differ considerably from the pre-launch values and are subject to degradation over time. The calibration accuracy is estimated to be less than 5%, which is compatible to that obtained by the more expensive vicarious calibration approach.
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Abstract. Abstract. The design and calibration of a rugged, low-cost, portable multiband radiometer is described. The instrument measures simultaneously in four bands, the spectral coverage of each being determined by interchangeable absorption filters. Simple band-pass type radiometers are seen to be complementary to conventional spectroradiometers for ground data collection in remote sensing. They have the inherent advantages of portability and speed of operation which make them particularly suitable for fieldwork in areas of complex terrain. An example of the practical use of the instrument to measure the spectral reflectance of partially vegetated surfaces in an area of complex terrain in southern Italy is presented. The increased spectral and spatial resolution of second generation sensing systems will make remote sensing of complex areas more feasible and extensive ground-based spectral measurements in such areas are a necessary step to understanding and utilizing such remotely sensed data.
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Multispectral remote sensing of the Earth using Landsat sensors was ushered on July 23, 1972, with the launch of Landsat-1. Following that success, four more Landsat satellites were launched, and each of these carried the Multispectral Scanner System (MSS). These five sensors provided the only consistent multispectral space-based imagery of the Earth's surface from 1972 to 1982. This work focuses on developing both a consistent and absolute radiometric calibration of this sensor system. Cross-calibration of the MSS was performed through the use of pseudoinvariant calibration sites (PICSs). Since these sites have been shown to be stable for long periods of time, changes in MSS observations of these sites were attributed to changes in the sensors themselves. In addition, simultaneous data collections were available for some MSS sensor pairs, and these were also used for cross-calibration. Results indicated substantial differences existed between instruments, up to 16%, and these were reduced to 5% or less across all MSS sensors and bands. Lastly, this paper takes the calibration through the final step and places the MSS sensors on an absolute radiometric scale. The methodology used to achieve this was based on simultaneous data collections by the Landsat-5 MSS and Thematic Mapper (TM) instruments. Through analysis of image data from a PICS location and through compensating for the spectral differences between the two instruments, the Landsat-5 MSS sensor was placed on an absolute radiometric scale based on the Landsat-5 TM sensor. Uncertainties associated with this calibration are considered to be less than 5%.
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Recently, in the case of observing the global environment, satellite remote sensing technology has been important. It's because satellite remote sensing is valuable for assessing relatively large areas. But now, small scale remote sensing techniques are needed which can be applicable to the detail investigation of plant tree areas which afforest land after the large scale construction of roads, dams and airports. In this study, we tried to develop and propose a lower altitude sensing technique which can be used in ground remote sensing by using a CCD camera. As a result of this investigation the following can be concluded: We recognized the transference characteristics of filters which were used in comparative tests about the four ground remote sensing devices. We also found that the near-IR camera could be used for an imaging spectral radiometer in the extraction of the vegetation index. Furthermore, we found that the vegetation index has varied hour by hour during the day of the experiment. Finally, we brought about an increase phase of the NDVI in a forest fire, which caused considerable damage, by developing new ground remote sensing technology.
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The University of Nebraska has developed a multiwavelength airborne polarimetric lidar (MAPL) system to support its Airborne Remote Sensing Program for vegetation remote sensing. The MAPL design and instrumentation are described in detail. Characteristics of the MAPL system include lidar waveform capture and polarimetric measurement capabilities, which provide enhanced opportunities for vegetation remote sensing compared with current sensors. Field tests were conducted to calibrate the range measurement. Polarimetric calibration of the system is also discussed. Backscattered polarimetric returns, as well as the cross-polarization ratios, were obtained from a small forested area to validate the system's ability for vegetation canopy detection. The system has been packaged to fly abroad a Piper Saratoga aircraft for airborne vegetation remote sensing applications.
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1. Remote Sensing of the Environment 2. Electromagnetic Radiation Principles 3. History of Aerial Photography and Aerial Platforms 4. Aerial Photography - Vantage Point, Cameras, Filters, and Film 5. Elements of Visual Image Interpretation 6. Photogrammetry 7. Multispectral Remote Sensing Systems 8. Thermal Infrared Remote Sensing 9. Active and Passive Microwave Remote Sensing 10. LIDAR Remote Sensing (new) 11. Remote Sensing of Vegetation 12. Remote Sensing of Water 13. Remote Sensing the Urban Landscape 14. Remote Sensing of Soils, Minerals, and Geomorphology 15. In situ Spectral Reflectance Measurement (new) Index Appendix A-Sources of Remote Sensing Information
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The Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne and satellite based sensors. Often, ground-truth measurements at these tests sites are not always successful due to weather and funding availability. Therefore, RSG has also employed automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor. This work studies the feasibility of determining the radiometric calibration of a hyperspectral imager using multispectral imagery. The work relies on the Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. Hyperion bands are compared to MODIS by band averaging Hyperion's high spectral resolution data with the relative spectral response of MODIS. The results compare cross-calibration scenarios that differ in image acquisition coincidence, test site used for the calibration, and reference sensor. Cross-calibration results are presented that show agreement between the use of coincident and non-coincident image pairs within 2% in most bands as well as similar agreement between results that employ the different MODIS sensors as a reference.
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Remote sensing data from multi-source optical and SAR (Synthetic Aperture Radar) sensors have been widely utilized to detect forest dynamics under a variety of conditions. Due to different temporal coverage, spatial resolution, and spectral characteristics, these sensors usually perform differently from one another. To conduct statistical modeling accuracies evaluation and comparison among several sensors, a linear statistical model was applied in this study for retrieval and comparative analysis based on remote-sensing indices from optical sensors of ALOS AVNIR-2 (Advanced Land Observing Satellite Advanced Visible and Near Infrared Radiometer type 2), Landsat-5 TM (Thematic Mapper), MODIS NBAR (Moderate Resolution Imaging Spectroradiometer Nadir BRDF-Adjusted Reflectance), and the SAR sensor of ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar), respectively. This modeling used the forest leaf area index (LAI) as the field measured variable. During modeling, six optical vegetation indices were selected for evaluation and comparison between the three optical sensors, while simultaneously, two radar indices were calculated for the comparison between ALOS AVNIR-2 and PALSAR sensors. The gap between the spatial resolution of remote-sensing data and field plot size can account for the different accuracies found in this study. This study provides a reference for the selection of remote-sensing data types and spatial resolution in specific forest monitoring applications with different data acquisition costs and accuracy needs. Normally, at regional and national scales, remote sensing data with 30 m spatial resolution (e.g., Landsat) could provide significant results in the statistical modelling and retrieval of LAI while the MODIS cannot always meet the requirements.
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