Facilitating soil-and plant-water research through the use of TDR

The development of TDR for measurement of soil water content and electrical conductivity has resulted in a large shift in measurement methods for a breadth of soil and hydrological characterization efforts. TDR has also opened new possibilities for soil and plant research. Five examples show how TDR has enhanced our ability to conduct our soil- and plant-water research. (i) Oxygen is necessary for healthy root growth and plant development but quantitative evaluation of the factors controlling oxygen supply in soil depends on knowledge of the soil water content by TDR. With water content information we have modeled successfully some impact of tillage methods on oxygen supply to roots and their growth response. (ii) For field assessment of soil mechanical properties influencing crop growth, water content capability was added to two portable soil strength measuring devices; (a) A TDT (Time Domain Transmittivity)-equipped soil cone penetrometer was used to evaluate seasonal soil strengthwater content relationships. In conventional tillage systems the relationships are dynamic and achieve the more stable no-tillage relationships only relatively late in each growing season; (b) A small TDR transmission line was added to a modified sheargraph that allowed shear strength and water content to be measured simultaneously on the same sample. In addition, the conventional graphing procedure for data acquisition was converted to datalogging using strain gauges. Data acquisition rate was improved by more than a factor of three with improved data quality. (iii) How do drought tolerant plants maintain leaf water content? Non-destructive measurement of TDR water content using a flat serpentine triple wire transmission line replaces more lengthy procedures of measuring relative water content. Two challenges remain: drought-stressed leaves alter salt content, changing electrical conductivity, and drought induced changes in leaf morphology affect TDR measurements. (iv) Remote radar signals are reflected from within the first 2 cm of soil. Appropriate calibration of radar imaging for soil water content can be achieved by a parallel pair of blades separated by 8 cm, reaching 1.7 cm into soil and forming a 20 cm TDR transmission line. The correlation between apparent relative permittivity from TDR and synthetic aperture radar (SAR) backscatter coefficient was 0.57 from an airborne flyover. These five examples highlight the diversity in the application of TDR in soil and plant research.

Bayesian retrieval of scatterometer wind fields

The retrieval of wind fields from scatterometer observations has traditionally been separated into two phases; local wind vector retrieval and ambiguity removal. Operationally, a forward model relating wind vector to backscatter is inverted, typically using look up tables, to retrieve up to four local wind vector solutions. A heuristic procedure, using numerical weather prediction forecast wind vectors and, often, some neighbourhood comparison is then used to select the correct solution. In this paper we develop a Bayesian method for wind field retrieval, and show how a direct local inverse model, relating backscatter to wind vector, improves the wind vector retrieval accuracy. We compare these results with the operational U.K. Meteorological Office retrievals, our own CMOD4 retrievals and a neural network based local forward model retrieval. We suggest that the neural network based inverse model, which is extremely fast to use, improves upon current forward models when used in a variational data assimilation scheme.

NEUROSAT: an overview

This report gives an overview of the work being carried out, as part of the NEUROSAT project, in the Neural Computing Research Group at Aston University. The aim is to give a general review of the work and methods, with reference to other documents which provide the detail. The document is ongoing and will be updated as parts of the project are completed. Thus some of the references are not yet present. In the broadest sense, the Aston part of NEUROSAT is about using neural networks (and other advanced statistical techniques) to extract wind vectors from satellite measurements of ocean surface radar backscatter. The work involves several phases, which are outlined below. A brief summary of the theory and application of satellite scatterometers forms the first section. The next section deals with the forward modelling of the scatterometer data, after which the inverse problem is addressed. Dealiasing (or disambiguation) is discussed, together with proposed solutions. Finally a holistic framework is presented in which the problem can be solved.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

Improved multi-beam neural network scatterometer forward models

Current methods for retrieving near surface winds from scatterometer observations over the ocean surface require a foward sensor model which maps the wind vector to the measured backscatter. This paper develops a hybrid neural network forward model, which retains the physical understanding embodied in ¸mod, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the mid-beam and using a common model for the fore- and aft-beams, we show a significant improvement in local wind vector retrieval. The hybrid model also fits the scatterometer observations more closely. The model is trained in a Bayesian framework, accounting for the noise on the wind vector inputs. We show that adding more high wind speed observations in the training set improves wind vector retrieval at high wind speeds without compromising performance at medium or low wind speeds.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

Improved neural network scatterometer forward models

Current methods for retrieving near-surface winds from scatterometer observations over the ocean surface require a forward sensor model which maps the wind vector to the measured backscatter. This paper develops a hybrid neural network forward model, which retains the physical understanding embodied in CMOD4, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the midbeam and using a common model for the fore and aft beams, we show a significant improvement in local wind vector retrieval. The hybrid model also fits the scatterometer observations more closely. The model is trained in a Bayesian framework, accounting for the noise on the wind vector inputs. We show that adding more high wind speed observations in the training set improves wind vector retrieval at high wind speeds without compromising performance at medium or low wind speeds. Copyright 2001 by the American Geophysical Union.

Neural Network Modelling with Input Uncertainty: Theory and Application

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which accounts for input noise provided that a model of the noise process exists. In the limit where the noise process is small and symmetric it is shown, using the Laplace approximation, that this method adds an extra term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable, and sampling this jointly with the network’s weights, using a Markov chain Monte Carlo method, it is demonstrated that it is possible to infer the regression over the noiseless input. This leads to the possibility of training an accurate model of a system using less accurate, or more uncertain, data. This is demonstrated on both the, synthetic, noisy sine wave problem and a real problem of inferring the forward model for a satellite radar backscatter system used to predict sea surface wind vectors.

A pragmatic Bayesian approach to wind field retrieval

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about 800 km, carrying a C-band scatterometer. A scatterometer measures the amount of backscatter microwave radiation reflected by small ripples on the ocean surface induced by sea-surface winds, and so provides instantaneous snap-shots of wind flow over large areas of the ocean surface, known as wind fields. Inherent in the physics of the observation process is an ambiguity in wind direction; the scatterometer cannot distinguish if the wind is blowing toward or away from the sensor device. This ambiguity implies that there is a one-to-many mapping between scatterometer data and wind direction. Current operational methods for wind field retrieval are based on the retrieval of wind vectors from satellite scatterometer data, followed by a disambiguation and filtering process that is reliant on numerical weather prediction models. The wind vectors are retrieved by the local inversion of a forward model, mapping scatterometer observations to wind vectors, and minimising a cost function in scatterometer measurement space. This thesis applies a pragmatic Bayesian solution to the problem. The likelihood is a combination of conditional probability distributions for the local wind vectors given the scatterometer data. The prior distribution is a vector Gaussian process that provides the geophysical consistency for the wind field. The wind vectors are retrieved directly from the scatterometer data by using mixture density networks, a principled method to model multi-modal conditional probability density functions. The complexity of the mapping and the structure of the conditional probability density function are investigated. A hybrid mixture density network, that incorporates the knowledge that the conditional probability distribution of the observation process is predominantly bi-modal, is developed. The optimal model, which generalises across a swathe of scatterometer readings, is better on key performance measures than the current operational model. Wind field retrieval is approached from three perspectives. The first is a non-autonomous method that confirms the validity of the model by retrieving the correct wind field 99% of the time from a test set of 575 wind fields. The second technique takes the maximum a posteriori probability wind field retrieved from the posterior distribution as the prediction. For the third technique, Markov Chain Monte Carlo (MCMC) techniques were employed to estimate the mass associated with significant modes of the posterior distribution, and make predictions based on the mode with the greatest mass associated with it. General methods for sampling from multi-modal distributions were benchmarked against a specific MCMC transition kernel designed for this problem. It was shown that the general methods were unsuitable for this application due to computational expense. On a test set of 100 wind fields the MAP estimate correctly retrieved 72 wind fields, whilst the sampling method correctly retrieved 73 wind fields.

Hydrologic Dynamics of the Ground-Water-Dependent Sian Ka’an Wetlands, Mexico, Derived from InSAR and SAR Data

The 5,280 km2 Sian Ka’an Biosphere Reserve includes pristine wetlands fed by ground water from the karst aquifer of the Yucatan Peninsula, Mexico. The inflow through underground karst structures is hard to observe making it difficult to understand, quantify, and predict the wetland dynamics. Remotely sensed Synthetic Aperture Radar (SAR) amplitude and phase observations offer new opportunities to obtain information on hydrologic dynamics useful for wetland management. Backscatter amplitude of SAR data can be used to map flooding extent. Interferometric processing of the backscattered SAR phase data (InSAR) produces temporal phase-changes that can be related to relative water level changes in vegetated wetlands. We used 56 RADARSAT-1 SAR acquisitions to calculate 38 interferograms and 13 flooding maps with 24 day and 48 day time intervals covering July 2006 to March 2008. Flooding extent varied between 1,067 km2 and 2,588 km2 during the study period, and main water input was seen to take place in sloughs during October–December. We propose that main water input areas are associated with water-filled faults that transport ground water from the catchment to the wetlands. InSAR and Landsat data revealed local-scale water divides and surface water flow directions within the wetlands.

Vertical profiles of environmental parameters measured from physical, optical and imaging sensors during station TARA_200 of the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set includes properties of seawater, particulate matter and dissolved matter from physical, optical and imaging sensors mounted on a vertical sampling system (Rosette) used during the 2009-2013 tara Oceans Expedition. It comprised 2 pairs of conductivity and temperature sensors (SEABIRD components), and a complete set of WEtLabs optical sensors, including chrorophyll and CDOM fluorometers, a 25 cm transmissiometer, and a one-wavelength backscatter meter. In addition, a SATLANTIC ISUS nitrate sensor and a Hydroptic Underwater Vision Profiler (UVP) were mounted on the rosette. In the Arctic Ocean and Arctic Seas (2013), a second oxygen sensor (SBE43) and a four frequency Aquascat acoustic profiler were added. The system was powered on specific Li-Ion batteries and data were self-recorded at 24HZ. Sensors have all been factory calibrated before, during and after the four year program. Oxygen was validated using climatologies (WOA09). Nitrate and Fluorescence data were adjusted with discrete measurements from Niskin bottles mounted on the Rosette, and optical darks were performed monthly on board. A total of 839 quality checked vertical profiles were made during the tara Oceans expedition 2009-2013.

Vertical profiles of environmental parameters measured from physical, optical and imaging sensors during station TARA_201 of the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set includes properties of seawater, particulate matter and dissolved matter from physical, optical and imaging sensors mounted on a vertical sampling system (Rosette) used during the 2009-2013 tara Oceans Expedition. It comprised 2 pairs of conductivity and temperature sensors (SEABIRD components), and a complete set of WEtLabs optical sensors, including chrorophyll and CDOM fluorometers, a 25 cm transmissiometer, and a one-wavelength backscatter meter. In addition, a SATLANTIC ISUS nitrate sensor and a Hydroptic Underwater Vision Profiler (UVP) were mounted on the rosette. In the Arctic Ocean and Arctic Seas (2013), a second oxygen sensor (SBE43) and a four frequency Aquascat acoustic profiler were added. The system was powered on specific Li-Ion batteries and data were self-recorded at 24HZ. Sensors have all been factory calibrated before, during and after the four year program. Oxygen was validated using climatologies (WOA09). Nitrate and Fluorescence data were adjusted with discrete measurements from Niskin bottles mounted on the Rosette, and optical darks were performed monthly on board. A total of 839 quality checked vertical profiles were made during the tara Oceans expedition 2009-2013.

Vertical profiles of environmental parameters measured from physical, optical and imaging sensors during station TARA_202 of the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set includes properties of seawater, particulate matter and dissolved matter from physical, optical and imaging sensors mounted on a vertical sampling system (Rosette) used during the 2009-2013 tara Oceans Expedition. It comprised 2 pairs of conductivity and temperature sensors (SEABIRD components), and a complete set of WEtLabs optical sensors, including chrorophyll and CDOM fluorometers, a 25 cm transmissiometer, and a one-wavelength backscatter meter. In addition, a SATLANTIC ISUS nitrate sensor and a Hydroptic Underwater Vision Profiler (UVP) were mounted on the rosette. In the Arctic Ocean and Arctic Seas (2013), a second oxygen sensor (SBE43) and a four frequency Aquascat acoustic profiler were added. The system was powered on specific Li-Ion batteries and data were self-recorded at 24HZ. Sensors have all been factory calibrated before, during and after the four year program. Oxygen was validated using climatologies (WOA09). Nitrate and Fluorescence data were adjusted with discrete measurements from Niskin bottles mounted on the Rosette, and optical darks were performed monthly on board. A total of 839 quality checked vertical profiles were made during the tara Oceans expedition 2009-2013.

Vertical profiles of environmental parameters measured from physical, optical and imaging sensors during station TARA_204 of the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set includes properties of seawater, particulate matter and dissolved matter from physical, optical and imaging sensors mounted on a vertical sampling system (Rosette) used during the 2009-2013 tara Oceans Expedition. It comprised 2 pairs of conductivity and temperature sensors (SEABIRD components), and a complete set of WEtLabs optical sensors, including chrorophyll and CDOM fluorometers, a 25 cm transmissiometer, and a one-wavelength backscatter meter. In addition, a SATLANTIC ISUS nitrate sensor and a Hydroptic Underwater Vision Profiler (UVP) were mounted on the rosette. In the Arctic Ocean and Arctic Seas (2013), a second oxygen sensor (SBE43) and a four frequency Aquascat acoustic profiler were added. The system was powered on specific Li-Ion batteries and data were self-recorded at 24HZ. Sensors have all been factory calibrated before, during and after the four year program. Oxygen was validated using climatologies (WOA09). Nitrate and Fluorescence data were adjusted with discrete measurements from Niskin bottles mounted on the Rosette, and optical darks were performed monthly on board. A total of 839 quality checked vertical profiles were made during the tara Oceans expedition 2009-2013.

Vertical profiles of environmental parameters measured from physical, optical and imaging sensors during station TARA_206 of the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set includes properties of seawater, particulate matter and dissolved matter from physical, optical and imaging sensors mounted on a vertical sampling system (Rosette) used during the 2009-2013 tara Oceans Expedition. It comprised 2 pairs of conductivity and temperature sensors (SEABIRD components), and a complete set of WEtLabs optical sensors, including chrorophyll and CDOM fluorometers, a 25 cm transmissiometer, and a one-wavelength backscatter meter. In addition, a SATLANTIC ISUS nitrate sensor and a Hydroptic Underwater Vision Profiler (UVP) were mounted on the rosette. In the Arctic Ocean and Arctic Seas (2013), a second oxygen sensor (SBE43) and a four frequency Aquascat acoustic profiler were added. The system was powered on specific Li-Ion batteries and data were self-recorded at 24HZ. Sensors have all been factory calibrated before, during and after the four year program. Oxygen was validated using climatologies (WOA09). Nitrate and Fluorescence data were adjusted with discrete measurements from Niskin bottles mounted on the Rosette, and optical darks were performed monthly on board. A total of 839 quality checked vertical profiles were made during the tara Oceans expedition 2009-2013.