Quantification of the effect of wind driven wheat motion on SAR interferometric coherence

This report quantifies the motion of wheat subject to wind and assesses the effect of this motion on the coherence obtained from Synthetic Aperture Radar (SAR) interferometry. Over vegetation, the loss of coherence due to the change in backscatter between two SAR images taken at a different time (temporal decorrelation) is related to the wind induced motion of vegetation elements. The research aims to provide simultaneous in situ measurements of crop motion and wind velocity at canopy height and to use these measurements in a coherence model to determine the quantitatively the parameters which infer temporal decorrelation. The potential of coherence for agricultural applications is assessed. The three-dimensional motion of wheat is measured by a photogrammetry method using two commercially available video cameras. Simultaneously, wind velocity at canopy height is measured by anemometers at a high sampling frequency. Wheat motion and wind velocity data were collected in a field local to Cranfield University in summer 2000. The CD attached to this report contains the wheat motion and wind velocity data. They show that the motion of wheat is correlated with the wind speed, and that wheat plants adjacent to each other move coherently. The coherence model is based on a statistical approach, which represents the total backscatter from vegetation as the phasor addition of a fixed component and one or more components which are weather dependent. The relative contributions of the total backscatter are estimated with the RT2 backscatter intensity model. The motion measurements are used to define the variability of the phase of the weather dependent components in the model. Outputs of the model show that a C-band SAR with an incidence angle of 23° (typical configuration of the ERS satellites) yields coherence values highly variable with the wind conditions at the time of the radar passes. The potential use of coherence for agricultural applications is limited by this variability, which infers the need for an accurate coherent backscatter model

Wheat video experiments 2000 data archive

Experiment summary: Experiments took place during the summer of 2000 on the dates in the following table. Four digit labels (“mmdd”, i.e. two digits for the month and two for the day) are often used in file or directory names and are given alongside each date. Date Label (“mmdd”) 02 Jun 2000 0602 06 Jun 2000 0606 21 Jun 2000 0621 19 Jul 2000 0719 25 Jul 2000 0725 02 Aug 2000 0802 15 Aug 2000 0815 Summary of files in this archive: This archive contains the main files describing the motion of the targets attached to the wheat plants and simultaneous wind velocity data. Pre-processing has been carried out to synchronize the video and wind data, to obtain coherent trajectories for each target tracked, and to present the data in a common reference system. Directory File Comments: docs DVA data 2000 summary.xls Summary of the anemometer data processing parameters and key results from the experiments DVA data 2000.doc Word document describing the digital vane anemometer (DVA) data processing sample video&DVA data.doc Describes sample data for target trajectories and the wind data. vidreport2.pdf Technical report documenting the videogrammetry technique used to measure the target positions, calibrate the system, and form coherent trajectories. Wheat video expts 2000.doc Documents much of the data pre-processing to allow it to be repeated if necessary from the original video files. Wheat video expts 2000.xls Tabulates data to be used with Wheat video expts 2000.doc, Contains many parameters used to process the video data. validation wheat video validation.doc Documents basic end-to-end validation of the trajectory measurements. Seynat_trajectories.zip File of all the 1 min trajectories from the analysis of Seynat (2000) used to compare with 2006 data for validation of the technique (and for independent analysis). When unzipped gives 6 directories for dates 6/6/00 to 15/8/00 with ASCII files of target trajectories. mmdd Table 1. Overview of directory structure and contents for this archive. Notes ----- * Wind data are not synchronized with video data for 2 June + Data for two video segments (a,b) are available for 2 Aug Table 2. Summary of contents of each of the experiment directories.Observations of wheat motion in wind were made through the later part of the growing season (June - August) for wheat on Cranfield Airfield in 2000. The data collected include stereo videogrammetric measurements of the motion of small targets attached to wheat plants along with simultaneous local wind velocity measurements. The experiments and their analysis have been described in the following publications: Seynat, C., Quantification of the effect of wind driven wheat motion on SAR interferometric coherence. PhD thesis, Cranfield University, 2000 Hobbs, SE, Seynat, C, and Matakidis, P, Videogrammetry: a practical method for measuring vegetation motion in wind demonstrated on wheat. Agricultural and Forest Meteorology, 2007 (doi: 10.1016/j.agrformet.2006.12.008) This archive contains the main data files for each day of the experiments together with some documentation for the experiments. There should be enough information so that users can analyse the data for their own requirements. EACH DIRECTORY IS PROVIDED AS ONE ZIPPED FILE (E.G. docs.zip, validation.zip, 0602.zip, CREATED USING 7-ZIP, AN OPEN SOURCE PROGRAM AVAILABLE FROM WWW.7-ZIP.ORG). THE ZIPPED FILES SHOULD BE READABLE BY WINZIP, WINRAR, 7-ZIP AND SIMILAR PROGRAMS

Videogrammetry: A practical method for measuring vegetation motion in wind demonstrated on wheat.

Plant motion in wind is a common phenomenon but has rarely been quantified. Among other effects, plant motion is known to affect the quality (or “coherence”) of interferometric radar images although the loss of quality is so far only understood qualitatively. The videogrammetry technique reported here was developed to obtain measurements of wheat plant motion in wind through a growing season to enable quantitative modelling of radar interferometric coherence for wheat fields, and so to improve our understanding of the radar imaging process for real vegetation. Videogrammetry using standard consumer camcorders was used to measure plant motion since it is a practical field technique which does not disturb the plants significantly. Small targets placed on the plants are tracked in 3D using stereo pairs of video images and allow the motion of individual plant elements to be measured. Local wind measurements were recorded in parallel with the video data. Examples of the data obtained and their analysis are presented. Specific results are shown for the amplitude of wheat plant motion versus windspeed, the variation of the plants’ oscillation frequency through the growing season, and the spatial coherence of the motion of neighbouring

Low-cost deformation measurement system for volcano monitoring

Ground deformation due to volcanic magma intrusion is recognised as an important precursor of eruptive activity at a volcano. The Global Positioning System (GPS) is ideally suited for this application. With the advent of inexpensive GPS receiver boards, the development of a low-cost GPS-based volcano monitoring system is now possible. It provides an expendable way of measuring volcanic activity. This paper presents a novel, autonomous, deformation monitoring system based on the use of the low-cost Novatel Superstar II receiver. The system uses several of those GPS units, one of which being at a known reference location and the others being scattered around the area of interest. The GPS Superstar II receivers provide measurements of the L1 carrier phase and of the GPS ephemeris. Those measurements are logged at a user-defined sampling rate, and transmitted via a radio link to a central processing station. The post-processing engine uses those data in ambiguity resolution and baseline computation algorithms. The measurement of changes in GPS baseline easting, northing and height components over time forms the basis for measuring the volcano's expansion prior to eruption. The paper reviews the major practical design considerations for GPS-based volcano monitoring systems, together with the dominant error sources. The data processing steps necessary to obtain the baseline between the reference receiver and each slave unit is also detailed. The system validation is presented, showing the performance results obtained for several baseline lengths, data sampling rates and observation session lengths. Each hardware and software component is described, as well as the system architecture and the special challenges in deploying and operating such a system in an inhospitable environment

Wheat video experiments 2000 data archive

Observations of wheat motion in wind were made through the later part of the growing season (June - August) for wheat on Cranfield Airfield in 2000. The data collected include stereo videogrammetric measurements of the motion of small targets attached to wheat plants along with simultaneous local wind velocity measurements. <div><br></div><div>This archive contains the main data files for each day of the experiments together with some documentation for the experiments. There should be enough information so that users can analyse the data for their own requirements. EACH DIRECTORY IS PROVIDED AS ONE ZIPPED FILE (E.G. docs.zip, validation.zip, 0602.zip, CREATED USING 7-ZIP, AN OPEN SOURCE PROGRAM AVAILABLE FROM WWW.7-ZIP.ORG). THE ZIPPED FILES SHOULD BE READABLE BY WINZIP, WINRAR, 7-ZIP AND SIMILAR PROGRAMS.<br><div><b><br></b></div><div><b>Experiment summary: </b>Experiments took place during the summer of 2000 on the dates in the following table. Four digit labels (“mmdd”, i.e. two digits for the month and two for the day) are often used in file or directory names and are given alongside each date. Date Label (“mmdd”) 02 Jun 2000 0602 06 Jun 2000 0606 21 Jun 2000 0621 19 Jul 2000 0719 25 Jul 2000 0725 02 Aug 2000 0802 15 Aug 2000 0815 <div><br></div><div><b>Summary of files in this archive:</b> This archive contains the main files describing the motion of the targets attached to the wheat plants and simultaneous wind velocity data. Pre-processing has been carried out to synchronize the video and wind data, to obtain coherent trajectories for each target tracked, and to present the data in a common reference system. </div><div><b><br></b></div><div><b>Directory File Comments: </b></div><div><u>docs:</u></div><div>DVA data 2000 summary.xls - Summary of the anemometer data processing parameters and key results from the experiments </div><div>DVA data 2000.doc - Word document describing the digital vane anemometer (DVA) data processing sample video</div><div>DVA data.doc - Describes sample data for target trajectories and the wind data. </div><div>vidreport2.pdf - Technical report documenting the videogrammetry technique used to measure the target positions, calibrate the system, and form coherent trajectories. </div><div>Wheat video expts 2000.xls - Tabulates data to be used with...</div><div>Wheat video expts 2000.doc - Documents much of the data pre-processing to allow it to be repeated if necessary from the original video files. Contains many parameters used to process the video data.</div><div><br></div><div><u>validation:</u></div><div>wheat video validation.doc - Documents basic end-to-end validation of the trajectory measurements. </div><div>Seynat_trajectories.zip - File of all the 1 min trajectories from the analysis of Seynat (2000) used to compare with 2006 data for validation of the technique (and for independent analysis). When unzipped gives 6 directories for dates 6/6/00 to 15/8/00 with ASCII files of target trajectories. mmdd Table 1. Overview of directory structure and contents for this archive. Notes ----- * Wind data are not synchronized with video data for 2 June + Data for two video segments (a,b) are available for 2 Aug Table 2. Summary of contents of each of the experiment directories.</div></div></div

Deep Learning-Based Emulation of Radiative Transfer Models for Top-of-Atmosphere BRDF Modelling Using Sentinel-3 OLCI

The Bidirectional Reflectance Distribution Function (BRDF) defines the anisotropy of surface reflectance and plays a fundamental role in many remote sensing applications. This study proposes a new machine learning-based model for characterizing the BRDF. The model integrates the capability of Radiative Transfer Models (RTMs) to generate simulated remote sensing data with the power of deep neural networks to emulate, learn and approximate the complex pattern of physical RTMs for BRDF modeling. To implement this idea, we used a one-dimensional convolutional neural network (1D-CNN) trained with a dataset simulated using two widely used RTMs: PROSAIL and 6S. The proposed 1D-CNN consists of convolutional, max poling, and dropout layers that collaborate to establish a more efficient relationship between the input and output variables from the coupled PROSAIL and 6S yielding a robust, fast, and accurate BRDF model. We evaluated the proposed approach performance using a collection of an independent testing dataset. The results indicated that the proposed framework for BRDF modeling performed well at four simulated Sentinel-3 OLCI bands, including Oa04 (blue), Oa06 (green), Oa08 (red), and Oa17 (NIR), with a mean correlation coefficient of around 0.97, and RMSE around 0.003 and an average relative percentage error of under 4%. Furthermore, to assess the performance of the developed network in the real domain, a collection of multi-temporals OLCI real data was used. The results indicated that the proposed framework has a good performance in the real domain with a coefficient correlation (R2), 0.88, 0.76, 0.7527, and 0.7560 respectively for the blue, green, red, and NIR bands

Deep Learning-Based Emulation of Radiative Transfer Models for Top-of-Atmosphere BRDF Modelling Using Sentinel-3 OLCI

The Bidirectional Reflectance Distribution Function (BRDF) defines the anisotropy of surface reflectance and plays a fundamental role in many remote sensing applications. This study proposes a new machine learning-based model for characterizing the BRDF. The model integrates the capability of Radiative Transfer Models (RTMs) to generate simulated remote sensing data with the power of deep neural networks to emulate, learn and approximate the complex pattern of physical RTMs for BRDF modeling. To implement this idea, we used a one-dimensional convolutional neural network (1D-CNN) trained with a dataset simulated using two widely used RTMs: PROSAIL and 6S. The proposed 1D-CNN consists of convolutional, max poling, and dropout layers that collaborate to establish a more efficient relationship between the input and output variables from the coupled PROSAIL and 6S yielding a robust, fast, and accurate BRDF model. We evaluated the proposed approach performance using a collection of an independent testing dataset. The results indicated that the proposed framework for BRDF modeling performed well at four simulated Sentinel-3 OLCI bands, including Oa04 (blue), Oa06 (green), Oa08 (red), and Oa17 (NIR), with a mean correlation coefficient of around 0.97, and RMSE around 0.003 and an average relative percentage error of under 4%. Furthermore, to assess the performance of the developed network in the real domain, a collection of multi-temporals OLCI real data was used. The results indicated that the proposed framework has a good performance in the real domain with a coefficient correlation (R2), 0.88, 0.76, 0.7527, and 0.7560 respectively for the blue, green, red, and NIR bands