Alexander Gruber

Recent advances in (soil moisture) triple collocation analysis

Abstract To date, triple collocation (TC) analysis is one of the most important methods for the global-scale evaluation of remotely sensed soil moisture data sets. In this study we review existing implementations of soil moisture TC analysis as well as investigations of the assumptions underlying the method. Different notations that are used to formulate the TC problem are shown to be mathematically identical. While many studies have investigated issues related to possible violations of the underlying assumptions, only few TC modifications have been proposed to mitigate the impact of these violations. Moreover, assumptions, which are often understood as a limitation that is unique to TC analysis are shown to be common also to other conventional performance metrics. Noteworthy advances in TC analysis have been made in the way error estimates are being presented by moving from the investigation of absolute error variance estimates to the investigation of signal-to-noise ratio (SNR) metrics. Here we review existing error presentations and propose the combined investigation of the SNR (expressed in logarithmic units), the unscaled error variances, and the soil moisture sensitivities of the data sets as an optimal strategy for the evaluation of remotely-sensed soil moisture data sets.

Evaluation of a global soil moisture product from finer spatial resolution sar data and ground measurements at Irish sites

In the framework of the European Space Agency Climate Change Initiative, a global, almost daily, soil moisture (SM) product is being developed from passive and active satellite microwave sensors, at a coarse spatial resolution. This study contributes to its validation by using finer spatial resolution ASAR Wide Swath and in situ soil moisture data taken over three sites in Ireland, from 2007 to 2009. This is the first time a comparison has been carried out between three sets of independent observations from different sensors at very different spatial resolutions for such a long time series. Furthermore, the SM spatial distribution has been investigated at the ASAR scale within each Essential Climate Variable (ECV) pixel, without adopting any particular model or using a densely distributed network of in situ stations. This approach facilitated an understanding of the extent to which geophysical factors, such as soil texture, terrain composition and altitude, affect the retrieved ECV SM product values in temperate grasslands. Temporal and spatial variability analysis provided high levels of correlation (p < 0.025) and low errors between the three datasets, leading to confidence in the new ECV SM global product, despite limitations in its ability to track the driest and wettest conditions.

Validation practices for satellite based earth observation data across communities

Assessing the inherent uncertainties in satellite data products is a challenging task. Different technical approaches have been developed in the Earth Observation (EO) communities to address the validation problem which results in a large variety of methods as well as terminology. This paper reviews state-of-the-art methods of satellite validation and documents their similarities and differences. First, the overall validation objectives and terminologies are specified, followed by a generic mathematical formulation of the validation problem. Metrics currently used as well as more advanced EO validation approaches are introduced thereafter. An outlook on the applicability and requirements of current EO validation approaches and targets is given.

Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data

ABSTRACT Synthetic aperture radar (SAR)-based multi-temporal backscatter analysis is a common approach that has been widely used for rice mapping. Co-polarized C-band microwave backscatter (HH or VV) and the co-polarization ratio (HH/VV) are the most commonly used data sets for rice mapping due to their high data availability while the utilization of cross-polarized backscatter (HV or VH) has received less attention. In this study, Sentinel 1A time series – acquired in the dual-polarized (VV/VH) Interferometric Wide (IW) swath mode during the spring growing season (October 2015 to March 2016) in the Mekong Delta – were used to analyse the relationship between the growing cycle of rice plants and the temporal variation of SAR backscatter at different polarizations. Results show that VH backscatter is more sensitive to rice growth than VV backscatter. Several vegetation phenological parameters including beginning date, heading date and the length of the growing season were extracted from the VH backscatter time series. A decision tree approach was applied to delineate rice-cultivated areas based on seasonal phenological parameters. The classification result was validated against a 2015 land use map. The overall classification accuracy is 87.2% (kappa coefficient – κ = 0.71). In addition, the SAR-derived rice area was compared against ground statistical data at the provincial level (coefficient of determination R2 = 0.98).

Quality Assessment of the CCI ECV Soil Moisture Product Using ENVISAT ASAR Wide Swath Data over Spain, Ireland and Finland

During the last decade, great progress has been made by the scientific community in generating satellite-derived global surface soil moisture products, as a valuable source of information to be used in a variety of applications, such as hydrology, meteorology and climatic modeling. Through the European Space Agency Climate Change Initiative (ESA CCI), the most complete and consistent global soil moisture (SM) data record based on active and passive microwaves sensors is being developed. However, the coarse spatial resolution characterizing such data may be not sufficient to accurately represent the moisture conditions. The objective of this work is to assess the quality of the CCI Essential Climate Variable (ECV) SM product by using finer spatial resolution Advanced Synthetic Aperture Radar (ASAR) Wide Swath and in situ soil moisture data taken over three regions in Europe. Ireland, Spain, and Finland have been selected with the aim of assessing the spatial and temporal representativeness of the ECV SM product over areas that differ in climate, topography, land cover and soil type. This approach facilitated an understanding of the extent to which geophysical factors, such as soil texture, terrain composition and altitude, affect the retrieved ECV SM product values. A good temporal and spatial agreement has been observed between the three soil moisture datasets for the Irish and Spanish sites, while poorer results have been found at the Finnish sites. Overall, the two different satellite derived products capture the soil moisture temporal variations well and are in good agreement with each other.

Performance inter-comparison of soil moisture retrieval models for the MetOp-A ASCAT instrument

In this study we evaluate five different retrieval algorithms, applied on MetOp-A ASCAT backscatter data, in their ability to retrieve soil moisture on a global scale. Correlation and triple collocation analysis are performed using in situ and land surface model data as a reference. Results do not clearly identify one best algorithm. We therefore conclude that future work should focus on the exploitation of the strengths and weaknesses of different modelling approaches in a synergetic way rather than trying to find one model that suits every possible situation.

Visual Tracing for the Eclipse Java Debugger

In contrast to stepping, tracing is a debugging technique that does not suspend the execution. This technique is more suitable for debugging programs whose correctness is compromised by the suspension of execution. In this work we present a tool for visually tracing Java programs in Eclipse. Trace point hits are collected on a per-instance basis. This enables finding out which trace points were hit for which objects at which time. The interactive visualization provides detailed information about the hits such as thread, stack trace, and assigned values. We implemented the tool as an Eclipse plug in that integrates with other features of Eclipse Java debugger. In an informal evaluation, developers appreciated the utility of our method as a solution in the middle between full tracing and stop-and-go debugging. They suggested scenarios in which our tool can help them in debugging and understanding their programs.

The Impact of Quadratic Nonlinear Relations between Soil Moisture Products on Uncertainty Estimates from Triple Collocation Analysis and Two Quadratic Extensions

AbstractThe error characterization of soil moisture products, for example, obtained from microwave remote sensing data, is a key requirement for using these products in applications like numerical weather prediction. The error variance and root-mean-square error are among the most popular metrics: they can be estimated consistently for three datasets using triple collocation (TC) without assuming any dataset to be free of errors. This technique can account for additive and multiplicative biases; that is, it assumes that the three products are linearly related. However, its susceptibility to nonlinear relations (e.g., due to sensor saturation and scale mismatch) has not been addressed. Here, a simulation study investigates the impact of quadratic relations on the TC error estimates [also when the products are first rescaled using the nonlinear cumulative distribution function (CDF) matching technique] and on those by two novel methods. These methods—based on error-in-variables regression and probabilistic ...

Potential of Sentinel-1 for high-resolution soil moisture monitoring

Soil moisture is a crucial variable for a large variety of applications with different requirements on the spatial and temporal resolution of the observations. Coarse-scale instruments can provide data operationally with a nearly-daily global coverage at a spatial resolution of several hundreds of square kilometers, whereas SAR instruments provide a spatial resolution of less than one hectare to about one square kilometer but with a revisit time varying from several days to several months. This study uses coarse-scale MetOp ASCAT data and higher resolution Envisat ASAR data taken in the GM mode and the WS mode together with in-situ measurements to demonstrate (i) the potential of Sentinel-1 to capture very local soil moisture variations and (ii) the expected impact of the significantly improved radiometric accuracy of Sentinel-1 compared to existing soil moisture missions.

Identification of soil moisture retrieval errors: Learning from the comparison of SMOS and ASCAT

Due to the strong contrast of the dielectric properties of dry and wet soil at microwave frequencies soil moisture can be retrieved on a global scale from active and passive microwave remote sensing instruments. Recent validation studies carried out over a number of in situ networks in Europe, the US and Australia have demonstrated that the soil moisture retrieval skill achieved with the Soil Moisture and Ocean Salinity (SMOS) mission and the Advanced Scatterometer (ASCAT) compares well in many regions of the world. But of course, there are also areas where the retrieved soil moisture values from these instruments do not match well. In this study global SMOS and ASCAT soil moisture data are compared with the aim to identify the areas of agreement and disagreement. The analysis confirms the findings of the previous studies that over most regions worldwide the temporal evolution of SMOS and ASCAT retrievals compare quite well. Notable exceptions are arid environments where the ASCAT retrievals fail to reproduce the real soil moisture trends, while the SMOS retrievals perform as expected. More work is required to understand the contrasting behavior of the ASCAT and SMOS soil moisture retrievals in these environments.