Brian Barrett

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

The importance of land surface-atmosphere interactions, principally the effects of soil moisture, on hydrological, meteorological, and ecological processes has gained widespread recognition over recent decades. Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively using traditional methods. Microwave remote sensing technology has demonstrated the potential to map and monitor relative soil moisture changes over large areas at regular intervals in time and also the opportunity of measuring, through inverse modelling, absolute soil moisture values. This ability has been demonstrated under a variety of topographic and land cover conditions using both active and passive microwave instruments. The purpose of this paper is to review the current status of soil moisture determination from active microwave remote sensing systems and to highlight the key areas of research that will have to be addressed to achieve routine use of the proposed retrieval approaches.

Temporal optimisation of image acquisition for land cover classification with random forest and MODIS time-series

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8–10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.

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.

Detecting changes in surface soil moisture content using differential SAR interferometry

The feasibility of measuring changes in surface soil moisture content with differential interferometric synthetic aperture radar (DInSAR) has received little attention in comparison with other active microwave techniques. In this study, multi-polarization C- and L-band DInSAR is explored as a potential tool for the measurement of changes in surface soil moisture in agricultural areas. Using 10 ascending phased array L-band SAR (PALSAR) scenes acquired by the Japanese Advanced Land Observing Satellite (ALOS) and 12 descending advanced SAR (ASAR) scenes acquired by the European ENVISAT satellite between July 2007 and November 2009, a series of 27 differential interferograms covering a common study area over southern Ireland were generated to investigate whether small-scale changes in phase are linked to measured soil moisture changes. Comparisons of observed mean surface displacement and in situ mean soil moisture change show that C-band cross-polarization pairs displayed the highest correlation coefficients over both the barley (correlation coefficient, r = 0.51, p = 0.04)- and potato crop (r = 0.81, p = 0.003)-covered fields. Current results support the hypothesis that a soil moisture phase contribution exists within differential interferograms covering agricultural areas.

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.

The Use of C- and L-Band Repeat-Pass Interferometric SAR Coherence for Soil Moisture Change Detection in Vegetated Areas

Soil moisture estimation studies using Synthetic Aperture Radar (SAR) routinely utilise only the amplitude part of the received echo. In this study, repeat-pass C- and L-band interferometric SAR coherence from 2007 - 2009 was evaluated for the detection of surface soil moisture changes in the presence of vegetation using two different approaches. In the first analysis, the association between low coherence and large in situ soil moisture changes was investigated using 24 interferometric pairs and the decorrelation effects due to vegetation and weather were also assessed. Results reveal that, in very few cases soil moisture differences between acquisitions contributed to the signal decorrelation. For the majority of cases, particularly in C-band, the change in vegetation tended to be the predominant source of decorrelation, suppressing the influence of any soil moisture changes. The second analysis applied thresholds to both coherence and intensity data to determine if a coalesced coherence (�) and intensity change (�� 0 ) approach could improve detection of changes in measured soil moisture content. The aim was to test the usefulness of a � > 0.3 and �� 0 > 1.5 dB thresholding approach to separate the effects of a vegetation change and a soil moisture change on the SAR signal. Results suggest that the approach improves the reliability of the soil moisture change detection although clearly limits the use of available image pairs. These analyses demonstrate the increased information the coherence adds to SAR studies over agricultural areas.

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.

Upland vegetation mapping using Random Forests with optical and radar satellite data

Abstract Uplands represent unique landscapes that provide a range of vital benefits to society, but are under increasing pressure from the management needs of a diverse number of stakeholders (e.g. farmers, conservationists, foresters, government agencies and recreational users). Mapping the spatial distribution of upland vegetation could benefit management and conservation programmes and allow for the impacts of environmental change (natural and anthropogenic) in these areas to be reliably estimated. The aim of this study was to evaluate the use of medium spatial resolution optical and radar satellite data, together with ancillary soil and topographic data, for identifying and mapping upland vegetation using the Random Forests (RF) algorithm. Intensive field survey data collected at three study sites in Ireland as part of the National Parks and Wildlife Service (NPWS) funded survey of upland habitats was used in the calibration and validation of different RF models. Eight different datasets were analysed for each site to compare the change in classification accuracy depending on the input variables. The overall accuracy values varied from 59.8% to 94.3% across the three study locations and the inclusion of ancillary datasets containing information on the soil and elevation further improved the classification accuracies (between 5 and 27%, depending on the input classification dataset). The classification results were consistent across the three different study areas, confirming the applicability of the approach under different environmental contexts.

Evaluation of multi-temporal and multi-sensor atmospheric correction strategies for land-cover accounting and monitoring in Ireland

Accurate atmospheric correction is an important preprocessing step for studies of multi-temporal land-cover mapping using optical satellite data. Model-based surface reflectance predictions (e.g. 6S – Second Simulation of Satellite Signal in the Solar Spectrum) are highly dependent on the adjustment of aerosol optical thickness (AOT) data. For regions with no or insufficient spatial and temporal coverage of meteorological ground measurements, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived AOT data are a valuable alternative, especially with regard to the dynamics of atmospheric conditions. In this study, atmospheric correction strategies were assessed based on the change in standard deviation (σ) compared to the raw data and also by machine learning land-cover classification accuracies. For three Landsat 8 OLI (acquired in 2013) and two RapidEye (acquired in 2010 and 2014) scenes, seven different correction strategies were tested over an agricultural area in southeast Ireland. Visibility calculated from daily spatial averaged TERRA-MODIS estimates (1° × 1° Aerosol Product) served as input for the atmospheric correction. In almost all cases the standard deviation of the raw data is reduced after incorporation of terrain correction, compared to the atmospheric-corrected data. ATCOR®-IDL-based correction decreases the standard deviation almost consistently (ranging from −0.3 to −26.7). The 6S implementation in GRASS GIS showed a tendency of increasing the variation in the data, especially for the RapidEye data. No major differences in overall accuracies (OAs) and kappa values were observed between the three machine learning classification approaches. The results indicate that the ATCOR®-IDL-based correction and MODIS parameterization methods are able to decrease the standard deviation and are therefore an appropriate approach to approximate the top-of-canopy reflectance.

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

The availability of satellite-derived global surface soil moisture products during the last decade has opened up great opportunities to incorporate these observations into applications in hydrology, meteorology, and climatic modeling. This study evaluates a new global soil moisture product developed under the framework of the European Space Agency (ESA) climate change initiative (CCI), using finer spatial resolution synthetic aperture radar (SAR) and ground-based measurements of soil moisture. The analysis is carried out over selected in situ networks over Ireland, Spain, and Finland with the aim of assessing the temporal representativeness of the coarse-scale CCI essential climate variable (ECV) soil moisture (ECV SM) product in these different areas. A good agreement (correlation coefficient (R) values between 0.53 and 0.92) was observed between the three soil moisture data sets for the Irish and Spanish sites while a reasonable agreement (R values between 0.41 and 0.52) was observed between the SAR and ECV SM soil moisture data sets at the Finnish sites. Overall, the two different satellite-derived products captured the soil moisture temporal variations well and were in good agreement with each other, highlighting the confidence of using the coarse-scale ECV SM product to track soil moisture variability in time.