Operational Processing Chain Research Articles

Random Forest Classifier for Cloud Clearing of the Operational TROPOMI XCH4 Product

The TROPOMI XCH4 data product requires rigorous cloud filtering to achieve a product accuracy of <1%. To this end, operational XCH4 data processing has been based on SUOMI-NPP VIIRS cloud observations. However, SUOMI-NPP is nearing the end of its operational life and has encountered malfunctions in 2022 and 2023. In this study, we introduce a novel machine learning cloud-clearing approach based on a random forest classifier (RFC). The RFC is trained on collocated TROPOMI and SUOMI-NPP VIIRS data to emulate VIIRS-like cloud clearing. After training, cloud masking requires only TROPOMI data, and so becomes operationally independent of SUOMI-NPP. We demonstrate the RFC approach by applying cloud clearing to operational TROPOMI XCH4 data for August 2022, a period in which VIIRS was not operational. For validation, we analyze the TROPOMI XCH4 data at 12 TCCON stations. Comparison of cloud clearing using the RFC and the original VIIRS method reveals excellent agreement with a similar station-to-station bias (−7.4 ppb versus −5.6 ppb), a similar standard deviation of the station-to-station bias (11.6 ppb versus 12 ppb), and the same Pearson correlation coefficient of 0.9. Remarkably, the RFC cloud clearing provides a slightly higher volume of data (2182 versus 2035 daily means) and appears to have fewer outliers. Since 21 November 2023, the RFC approach is part of the operational processing chain of the European Space Agency (ESA). For now, the default practice is to utilize SNPP-VIIRS when accessible. Only in cases where VIIRS data are unavailable do we resort to the RFC cloud mask.

Assessment of ocean color atmospheric correction methods and development of a regional ocean color operational dataset for the Baltic Sea based on Sentinel-3 OLCI

The Baltic Sea is characterized by large gradients in salinity, high concentrations of colored dissolved organic matter, and a phytoplankton phenology with two seasonal blooms. Satellite retrievals of chlorophyll-a concentration (chl-a) are hindered by the optical complexity of this basin and the reduced performance of the atmospheric correction in its highly absorbing waters. Within the development of a regional ocean color operational processing chain for the Baltic Sea based on Sentinel-3 Ocean and Land Colour Instrument (OLCI) full-resolution data, the performance of four atmospheric correction processors for the retrieval of remote-sensing reflectance (Rrs) was analyzed. Assessments based on three Aerosol Robotic Network-Ocean Color (AERONET-OC) sites and shipborne hyperspectral radiometers show that POLYMER was the best-performing processor in the visible spectral range, also providing a better spatial coverage compared with the other processors. Hence, OLCI Rrs spectra retrieved with POLYMER were chosen as input for a bio-optical ensemble scheme that computes chl-a as a weighted sum of different regional multilayer perceptron neural nets. This study also evaluated the operational Rrs and chl-a datasets for the Baltic Sea based on OC-CCI v.6. The chl-a retrievals based on OC-CCI v.6 and OLCI Rrs, assessed against in-situ chl-a measurements, yielded similar results (OC-CCI v.6: R2 = 0.11, bias = −0.22; OLCI: R2 = 0.16, bias = −0.03) using a common set of match-ups for the same period. Finally, an overall good agreement was found between chl-a retrievals from OLCI and OC-CCI v.6 although differences between Rrs were amplified in terms of chl-a estimates.

A New Method for InSAR Stratified Tropospheric Delay Correction Facilitating Refinement of Coseismic Displacement Fields of Small-to-Moderate Earthquakes

Focusing on stratified tropospheric delay correction in the small-amplitude coseismic displacement field of small-to-moderate earthquakes (<Mw 6.5), we develop a Simple-Stratification-Correction (SSC) approach based on the empirical phase-elevation relationship and spatial properties of the troposphere, via an equal-size window segmentation. We validate our SSC method using 23 real earthquakes that occurred from January 2016 to May 2021 with a moment magnitude (Mw) ranging from 4.5 to 6.5. We conclude that SSC performs well according to the amount of reduction in semi-variance and the root-mean-square value. This method primarily focuses on stratification delay correction; thus, it is especially useful in regions with complex terrain, while it can mitigate partial large-scale turbulence signals. We investigate three parameters that are empirically setup in the correction working flow and inspect their optimal settings, when implementing SSC for quick response after earthquake. Our method is ready to be integrated into an operational InSAR processing chain to produce a reliable atmospheric phase screen map, which can also serve as an auxiliary product to quickly and timely quantify stratification delays in coseismic interferograms. Through improved accuracy of the coseismic displacement field, the focal mechanism could be better constrained to facilitate the building and expansion of the geodesy-based earthquake catalogue.

Cheap, Valid Regularizers for Improved Interferometric Phase Linking

Retrieving a consistent phase history from a multi-looked InSAR stack depends critically on the accuracy of the (coherence) magnitude estimates. To estimate the magnitudes more reliably, I propose three regularization methods: Hadamard, spectral, and Hadamard–spectral regularization. All three are computationally cheap and parameterized such that they are guaranteed to yield valid magnitude matrices. These regularizers achieved relative improvements in the phase history accuracy of up to 40% in simulations. The improvements were greatest for low long-term coherences. All three methods performed similarly in the simulations and in a Sentinel-1 stack, for which the local phase dispersion decreased with regularization. Implementation of the regularizers into operational processing chains is expected to improve deformation and uncertainty estimates, especially for local movements over decorrelating terrain.

Sentinel-1-Based Water and Flood Mapping: Benchmarking Convolutional Neural Networks Against an Operational Rule-Based Processing Chain

In this study, the effectiveness of several convolutional neural network architectures (AlbuNet-34/FCN/DeepLabV3+/U-Net/U-Net++) for water and flood mapping using Sentinel-1 amplitude data is compared to an operational rule-based processor (S-1FS). This comparison is made using a globally distributed dataset of Sentinel-1 scenes and the corresponding ground truth water masks derived from Sentinel-2 data to evaluate the performance of the classifiers on a global scale in various environmental conditions. The impact of using single versus dual-polarized input data on the segmentation capabilities of AlbuNet-34 is evaluated. The weighted cross entropy loss is combined with the Lovász loss and various data augmentation methods are investigated. Furthermore, the concept of atrous spatial pyramid pooling used in DeepLabV3+ and the multiscale feature fusion inherent in U-Net++ are assessed. Finally, the generalization capacity of AlbuNet-34 is tested in a realistic flood mapping scenario by using additional data from two flood events and the Sen1Floods11 dataset. The model trained using dual polarized data outperforms the S-1FS significantly and increases the intersection over union (IoU) score by 5%. Using a weighted combination of the cross entropy and the Lovász loss increases the IoU score by another 2%. Geometric data augmentation degrades the performance while radiometric data augmentation leads to better testing results. FCN/DeepLabV3+/U-Net/U-Net++ perform not significantly different to AlbuNet-34. Models trained on data showing no distinct inundation perform very well in mapping the water extent during two flood events, reaching IoU scores of 0.96 and 0.94, respectively, and perform comparatively well on the Sen1Floods11 dataset.

NWP calibration applied to Aeolus Mie channel winds

AbstractAeolus is the first Doppler wind lidar (DWL) to measure wind profiles from space. Aeolus is an ESA (European Space Agency) explorer mission with the objective to retrieve winds from the collected atmospheric return signal which is the result of Mie and Rayleigh scattering of laser‐emitted light by atmospheric molecules and particulates. The focus of this paper is on winds retrieved from instrument Mie channel collected data, that is, originating from Mie scattering by atmospheric aerosols and clouds. The use of simulated data from numerical weather prediction (NWP) models is a widely accepted and proven concept for the monitoring of the performance of many meteorological instruments, including Aeolus. Continuous monitoring of Aeolus Mie channel winds against model winds from the European Centre for Medium‐Range Weather Forecasts (ECMWF) has revealed systematic errors in retrieved Mie winds. Following a reverse engineering approach, the systematic errors could be traced back to imperfections of the data in the calibration tables which serve as input for the on‐ground wind processing algorithms. A new algorithm, denoted NWP calibration, makes use of NWP model winds to generate an updated calibration table. It is shown that Mie winds retrieved by making use of the NWP‐based calibration tables show reduced systematic errors, not only when compared to NWP model winds but also when compared to an independent dataset of very‐high‐resolution aircraft wind data. The latter gives high confidence that the NWP‐based calibration algorithm does not introduce model‐related errors into retrieved Aeolus Mie winds. Based on the presented results in this paper, the NWP‐based calibration table, as part of the level‐2B wind processing, has become part of the operational processing chain since 01 July 2021.

The impact of Aeolus wind retrievals on ECMWF global weather forecasts

AbstractAeolus is the world's first spaceborne Doppler Wind Lidar, providing profiles of horizontal line‐of‐sight (HLOS) wind retrievals. Numerical weather prediction (NWP) impact and error statistics of Aeolus Level‐2B (L2B) wind statistics have been assessed using the European Centre for Medium‐range Weather Forecasts (ECMWF) global data assimilation system. Random and systematic error estimates were derived from observation minus background departure statistics. The HLOS wind random error standard deviation is estimated to be in the range 4.0–7.0 m·s−1 for the Rayleigh‐clear and 2.8–3.6 m·s−1 for the Mie‐cloudy, depending on atmospheric signal levels which in turn depend on instrument performance, atmospheric backscatter properties and the processing algorithms. Complex systematic HLOS wind error variations on time‐scales less than one orbit were identified, most strongly affecting the Rayleigh‐clear winds. NWP departures and instrument housekeeping data confirmed that it is caused by temperature gradients across the primary mirror. A successful bias correction scheme was implemented in the operational processing chain in April 2020. In Observing System Experiments (OSEs), Aeolus provides statistically significant improvement in short‐range forecasts as verified by observations sensitive to temperature, wind and humidity. Longer forecast range verification shows positive impact that is strongest at the day two to three forecast range: ∼2% improvement in root‐mean‐square error for vector wind and temperature in the tropical upper troposphere and lower stratosphere, and polar troposphere. Positive impact up to 9 days is found in the tropical lower stratosphere. Both Rayleigh‐clear and Mie‐cloudy winds provide positive impact, but the Rayleigh accounts for most tropical impact. The Forecast Sensitivity Observation Impact (FSOI) metric is available since 9 January 2020, when Aeolus was operationally assimilated, which confirms Aeolus is a useful contribution to the global observing system, with the Rayleigh‐clear and Mie‐cloudy winds providing similar overall short‐range impact in 2020.

Greenland ice velocity maps from the PROMICE project

Abstract. We present the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) Ice Velocity product (https://doi.org/10.22008/promice/data/sentinel1icevelocity/greenlandicesheet, Solgaard and Kusk, 2021), which is a time series of Greenland Ice Sheet ice velocity mosaics spanning September 2016 through to the present. The product is based on Sentinel-1 synthetic aperture radar data and has a 500 m grid spacing. A new mosaic is available every 12 d and spans two consecutive Sentinel-1 cycles (24 d). The product is made available within ∼ 10 d of the last acquisition and includes all possible 6 and 12 d pairs within the two Sentinel-1A cycles. We describe our operational processing chain from data selection, mosaicking, and error estimation to final outlier removal. The product is validated against in situ GPS measurements. We find that the standard deviation of the difference between satellite- and GPS-derived velocities (and bias) is 20 m yr−1 (−3 m yr−1) and 27 m yr−1 (−2 m yr−1) for the components in an eastern and northern direction, respectively. Over stable ground the values are 8 m yr−1 (0.1 m yr−1) and 12 m yr−1 (−0.6 m yr−1) in an eastern and northern direction, respectively. This is within the expected values; however, we expect that the GPS measurements carry a considerable part of this uncertainty. We investigate variations in coverage from both a temporal and spatial perspective. The best spatial coverage is achieved in winter due to the comprehensive data coverage by Sentinel-1 and high coherence, while summer mosaics have the lowest coverage due to widespread melt. The southeast Greenland Ice Sheet margin, along with other areas of high accumulation and melt, often has gaps in the ice velocity mosaics. The spatial comprehensiveness and temporal consistency make the product ideal both for monitoring and for studying ice-sheet-wide and glacier-specific ice discharge and dynamics of glaciers on seasonal scales.

Review of recent GNSS modelling improvements based on CODEs Repro3 contribution

The Center for Orbit Determination in Europe (CODE) is contributing to the most recent reprocessing effort of the International GNSS Service (IGS) with a triple-system solution including GPS (since 1994), GLONASS (since 2002) and Galileo (since 2013). Several model improvements with respect to the operational processing scheme have been implemented and applied for the reprocessing effort: The first group of improvements is related to the update of IERS-related models (mean pole and high-frequency pole models). The second group is related to the inclusion of Galileo with calibrated receiver and satellite antennas. The consistency of the scale for the ground station coordinates was verified based on the estimation of GNSS-specific coordinate biases. It turned out that currently widely used GPS-based receiver antenna calibrations do compensate (just by chance) a discrepancy in the scale when using the pre-launch satellite antenna calibrations for Galileo. Furthermore and third, a long-arc solution over three days is introduced where in particular empirical velocity changes are rescheduled with respect to the operational processing chain at CODE. Instead of simply estimating them every 12 h they are now setup at orbit midnight. This rescheduling reduces the size of orbit misclosures by 10% for GPS and 15% for Galileo; no improvement for GLONASS was observed because there the orbit misclosures are dominated by other effects. Another feature applied for the first time in this reprocessing series is to downweight the observations of a number of GPS satellites with a reduced stability in the attitude control around the year 2000. This change in the analysis strategy reduces the noise level of GNSS-derived products, e.g., of the Earth rotation series. Even if the article is focusing on the reprocessing series as provided by the CODE analysis center many of the conclusions may also be applied to other GNSS series in future.

Simulating Multi-Directional Narrowband Reflectance of the Earth’s Surface Using ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions)

The ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions) product (a climatological database coupled to its companion calculation toolkit) enables users to simulate realistic hyperspectral and directional global Earth surface reflectances (i.e., top-of-canopy/bottom-of-atmosphere) over the 240–4000 nm spectral range (at 1-nm resolution) and in any illumination/observation geometry, at 0.1° × 0.1° spatial resolution for a typical year. ADAM aims to support the preparation of optical Earth observation missions as well as the design of operational processing chains for the retrieval of atmospheric parameters by characterizing the expected surface reflectance, accounting for its anisotropy. Firstly, we describe (1) the methods used in the development of the gridded monthly ADAM climatologies (over land surfaces: monthly means of normalized reflectances derived from MODIS observations in seven spectral bands for the year 2005; over oceans: monthly means over the 1999–2009 period of chlorophyll content from SeaWiFS and of wind speed from SeaWinds), and (2) the underlying modeling approaches of ADAM toolkit to simulate the spectro-directional variations of the reflectance depending on the assigned surface type. Secondly, we evaluate ADAM simulation performances over land surfaces. A comparison against POLDER multi-spectral/multi-directional measurements for year 2008 shows reliable simulation results with root mean square differences below 0.027 and R2 values above 0.9 for most of the 14 land cover IGBP classes investigated, with no significant bias identified. Only for the “Snow and ice” class is the performance lower pointing to a limitation of climatological data to represent actual snow properties. An evaluation of the modeled reflectance in the specific backscatter direction against CALIPSO data reveals that ADAM tends to overestimate (underestimate) the so-called “hot-spot” by a factor of about 1.5 (1.5 to 2) for barren (vegetated) surfaces.

A methodology based on GEOBIA and WorldView-3 imagery to derive vegetation indices at tree crown detail in olive orchards

Precision agriculture (PA) is becoming an essential practice for the implementation of sustainable agriculture that encompasses the efficient use of resources and a systematic crops monitoring. The increasing temporal and spatial resolution of satellite imagery, coupled with their availability and decreasing costs, create new possibilities for generating accurate datasets on different crops variables, more frequently available as ready-to-use data. The availability of very high-resolution (VHR) satellite imagery, such as the WorldView-3 (WV-3), leads to the advanced potential of satellite Remote Sensing (RS), becoming in the last decade one of the main data source in precision agriculture (PA). In the broad overview of these procedures, geographic object-based image classification (GEOBIA) techniques, gained broad interest as methods to produce geographic information in GIS-ready format. In this paper, methodologies for a semiautomatic process workflow is presented, providing olive tree crown detection in two different olive orchards in Calabria (Italy), collected by means of GEOBIA procedures, in order to investigate olive tree spectral behavior and the reliability of WW-3 derived vegetation indices (VIs). The semi-automated classification method, accomplished by imagery pre-processing steps, may constitute an operational processing chain for mapping and monitoring olive orchards at tree scale detail. Five VIs were investigated: Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index 2 (MSAVI 2), Normalized Difference Red Edge Vegetation Index (NDRE), Modified Chlorophyll Absorption Ratio Index Improved (MCARI2), and NDVI2. The obtained results were statistically tested and their accuracy assessed. Thematic accuracy ranges from 95.33% to 96% in both study areas with an overall tree detection rate of 96.8%. Statistical analysis showed that the major differences in spectral behavior, over different plots of the investigated olive orchards, are mainly due to the component of the red-infrared regions of the electromagnetic spectrum (EM), where the red-edge becomes important in assessing the state of general vigor. Moreover, the proposed methodology increases the possibility of detecting tree stress at earlier stages and the benefits of using satellite-based approaches in terms of: larger area coverage, less processing and operator interaction coupled with more spectral information, thus reducing the need to collect costly reference data sampling.

Algorithmic Chain for Lightning Detection and False Event Filtering Based on the MTG Lightning Imager

Meteosat Third Generation (MTG) is the next generation of European meteorological geostationary satellites, set to be launched in 2021. Besides ensuring continuity with Meteosat Second Generation imagery mission, the new series will feature new instruments, such as the Lightning Imager (LI), a high-speed optical detector providing near real-time lightning detection capabilities over Europe and Africa. The instrument will register events on pixels, where a lightning pulse generates a transient in the acquired radiance. In parallel, signal variations due to a number of unwanted sources, e.g., acquisition noise or jitter movement, are expected to produce false events. The challenge for on-board and on-ground processing is, thus, to discard as many false events as possible while keeping a majority of the true lightning events. This paper discusses a chain of algorithms that can be used by the LI for the detection of lightning and for the filtering of false events. Some of these algorithms have been developed in the framework of internal research and simulations conducted by the MTG team at the European Space Agency on an in-house LI simulator and therefore will not necessarily reflect the ultimate operational processing chain. The application of the chain on a representative scenario shows that 99.5% of the false events can be eliminated while keeping 83.6% of the true events, before generating the LI higher level data products. Machine learning techniques have also been studied as an alternative for on-ground event processing, and preliminary results indicate promising potential.

Altimetry in a Regional Tropical Sea [Space Agencies

The satellite for Argos and AltiKa (SARAL/AltiKa) is the first ocean altimeter mission to operate in the Ka-band frequency. The objective of this article is to investigate the extent to which SARAL/AltiKa sea-level measurements provide valuable information in a complex bathymetric region, i.e., the semienclosed Solomon Sea. The data-editing procedure is revisited, and we propose two new data-editing criteria. The first is based on the detection of erroneous sea-level values after computation, and the second directly analyzes the radar measurements and geophysical corrections. We show that both methods are significantly more efficient than the standard procedure used in operational processing chains.

A two parameter model to simulate thermal infrared directional effects for remote sensing applications

Abstract Measurements of land surface temperature (LST) performed in the thermal infrared (TIR) domain are prone to strong directional anisotropy. Instead of detailed analytical physical TIR models requiring too much input information and computational capacities, simplified parametric approaches capable to mimic and correct with precision the angular effects on LST will be deemed suitable for practical satellite applications. In this study, we present a simple two parameters model, so-called RL (Roujean-Lagouarde), which shows capabilities to properly depict the directional signatures of both urban and vegetation targets within an accuracy better than 1 °C. This latter value is the RMSE (root mean square error) obtained as the best adjustment of the RL model against in situ datasets. Then the RL approach was compared to a synthetic dataset generated by the model Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) in which large variability in meteorological scenarios, canopy structure and water status conditions was accounted for. Results indicate RMSE ≤ 0.6 °C which is a very hopeful result. Besides, the RL model performs even better than the popular parametric model of Vinnikov that encompasses two unknowns. The ability of RL model to better reproduce the hotspot phenomenon explains this feature. The RL model appears as a potential candidate for future operational processing chains of TIR satellite data because it fulfills the requirements of both simple analytical formulation and limited number of input parameters. Efforts nevertheless remain to be done on inversion methodologies.

Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series

Reliable drought information is of utmost importance for efficient drought management. This paper presents a fully operational processing chain for mapping drought occurrence, extent and strength based on Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data at 250 m resolution. Illustrations are provided for the territory of Kenya. The processing chain was developed at BOKU (University of Natural Resources and Life Sciences, Vienna, Austria) and employs a modified Whittaker smoother providing consistent (de-noised) NDVI “Monday-images” in near real-time (NRT), with time lags between zero and thirteen weeks. At a regular seven-day updating interval, the algorithm constrains modeled NDVI values based on reasonable temporal NDVI paths derived from corresponding (multi-year) NDVI “climatologies”. Contrary to other competing approaches, an uncertainty range is produced for each pixel, time step and time lag. To quantify drought strength, the vegetation condition index (VCI) is calculated at pixel level from the de-noised NDVI data and is spatially aggregated to administrative units. Besides the original weekly temporal resolution, the indicator is also aggregated to one- and three-monthly intervals. During spatial and temporal aggregations, uncertainty information is taken into account to down-weight less reliable observations. Based on the provided VCI, Kenya’s National Drought Management Authority (NDMA) has been releasing disaster contingency funds (DCF) to sustain counties in drought conditions since 2014. The paper illustrates the successful application of the drought products within NDMA by providing a retrospective analysis applied to droughts reported by regular food security assessments. We also present comparisons with alternative products of the US Agency for International Development (USAID)’s Famine Early Warning Systems Network (FEWS NET). We found an overall good agreement (R2 = 0.89) between the two datasets, but observed some persistent (seasonal and spatial) differences that should be assessed against external reference information.

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain for hyperspectral satellite missions. We assessed six retrieval algorithms for estimating LNC from canopy reflectance of winter wheat in eight field experiments. These experiments represented variations in the N application rates, planting densities, ecological sites and cultivars and yielded a total of 821 samples from various places in Jiangsu, China over nine consecutive years. Based on the reflectance spectra and their first derivatives, six methods using different numbers of wavelengths were applied to construct predictive models for estimating wheat LNC, including continuum removal (CR), vegetation indices (VIs), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector machines (SVMs). To assess the performance of these six methods, we provided a systematic evaluation of the estimation accuracies using the six metrics that were the coefficients of determination for the calibration (R2C) and validation (R2V) sets, the root mean square errors of prediction (RMSEP) for the calibration and validation sets, the ratio of prediction to deviation (RPD), the computational efficiency (CE) and the complexity level (CL). The following results were obtained: (1) For the VIs method, SAVI(R1200, R705) produced a more accurate estimation of the LNC than other indices, with R²C, R²V, RMSEP, RPD and CE values of 0.844, 0.795, 0.384, 2.005 and 0.10 min, respectively; (2) For the SMLR, PLSR, ANNs and SVMs methods, the SVMs using the first derivative canopy spectra (SVM-FDS) offered the best accuracy in terms of R²C, R²V, RMSEP, RPD, and CE, at 0.96, 0.78, 0.37, 2.02, and 21.17, respectively; (3) The PLSR-FDS, ANN-OS and SVM-FDS methods yield similar accuracies if the CE and CL are not considered, however, ANNs and SVMs performed better on calibration set than the validation set which indicate that we should take more caution with the two methods for over-fitting. Except PLS method, the performance for most methods did not enhance when the spectrum were operated by the first derivative. Moreover, the evaluation of the robustness demonstrates that SVM method may be better suited than the other methods to cope with potential confounding factors for most varieties, ecological site and growth stage; (4) The prediction accuracy was found to be higher when more wavelengths were used, though at the cost of a lower CE. The findings are of interest to the remote sensing community for the development of improved inversion schemes for hyperspectral applications concerning other types of vegetation. The examples provided in this paper may also serve to illustrate the advantages and shortcomings of empirical hyperspectral models for mapping important vegetation biophysical properties of other crops.

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

Forthcoming superspectral satellite missions dedicated to land monitoring, as well as planned imaging spectrometers, will unleash an unprecedented data stream. The processing requirements for such large data streams involve processing techniques enabling the spatio-temporally explicit quantification of vegetation properties. Typically retrieval must be accurate, robust and fast. Hence, there is a strict requirement to identify next-generation bio-geophysical variable retrieval algorithms which can be molded into an operational processing chain. This paper offers a review of state-of-the-art retrieval methods for quantitative terrestrial bio-geophysical variable extraction using optical remote sensing imagery. We can categorize these methods into (1) parametric regression, (2) non-parametric regression, (3) physically-based and (4) hybrid methods. Hybrid methods combine generic capabilities of physically-based methods with flexible and computationally efficient methods, typically non-parametric regression methods. A review of the theoretical basis of all these methods is given first and followed by published applications. This paper focusses on: (1) retrievability of bio-geophysical variables, (2) ability to generate multiple outputs, (3) possibilities for model transparency description, (4) mapping speed, and (5) possibilities for uncertainty retrieval. Finally, the prospects of implementing these methods into future processing chains for operational retrieval of vegetation properties are presented and discussed.

Satellite-based drought monitoring in Kenya in an operational setting

Abstract. The University of Natural Resources and Life Sciences (BOKU) in Vienna (Austria) in cooperation with the National Drought Management Authority (NDMA) in Nairobi (Kenya) has setup an operational processing chain for mapping drought occurrence and strength for the territory of Kenya using the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI at 250 m ground resolution from 2000 onwards. The processing chain employs a modified Whittaker smoother providing consistent NDVI “Mondayimages” in near real-time (NRT) at a 7-daily updating interval. The approach constrains temporally extrapolated NDVI values based on reasonable temporal NDVI paths. Contrary to other competing approaches, the processing chain provides a modelled uncertainty range for each pixel and time step. The uncertainties are calculated by a hindcast analysis of the NRT products against an “optimum” filtering. To detect droughts, the vegetation condition index (VCI) is calculated at pixel level and is spatially aggregated to administrative units. Starting from weekly temporal resolution, the indicator is also aggregated for 1- and 3-monthly intervals considering available uncertainty information. Analysts at NDMA use the spatially/temporally aggregated VCI and basic image products for their monthly bulletins. Based on the provided bio-physical indicators as well as a number of socio-economic indicators, contingency funds are released by NDMA to sustain counties in drought conditions. The paper shows the successful application of the products within NDMA by providing a retrospective analysis applied to droughts in 2006, 2009 and 2011. Some comparisons with alternative products (e.g. FEWS NET, the Famine Early Warning Systems Network) highlight main differences.

Relativistic Effects in Bistatic Synthetic Aperture Radar

This paper addresses relativistic effects in bistatic and multistatic synthetic aperture radar (SAR) systems and missions. It is shown that the use of different reference frames for bistatic SAR processing and bistatic radar synchronization is prone to notable phase and time errors. These errors are a direct consequence of the relativity of simultaneity and can be explained in good approximation within the framework of Einstein's special theory of relativity. Using the invariance of the space-time interval, an analytic expression is derived that shows that the time and phase errors increase with increasing along-track distance between the satellites. The predicted errors are in excellent agreement with measurements from TanDEM-X and provide a satisfactory explanation for previously observed digital elevation map (DEM) height offsets that exceeded ±10 m. Consideration of the unexpected relativistic effects is essential for accurate DEM generation in TanDEM-X and has in the meantime been implemented in the operational processing chain.

Evaluation of the new French operational weather radar product for the field of urban hydrology

The main objective of this paper is to evaluate, at the urban scale, the accuracy of the new French operational radar processing chain deployed within the French operational weather radar network. Such an evaluation is conducted by comparing radar data resulting from this processing chain (with a 1-km 2 resolution) to rain gauge data at four different time scales, i.e. 5, 15, 30 and 60 min. These data are supplied by the Trappes Radar Station, located 30 km southwest of Paris. A total of 69 rain gauges installed within a radius of 80 km from the weather radar have provided the ground reference data. The dataset comprises 50 varied rainy days. The influence of distance, quality code of the radar data and type of rainfall, as well as the adjustment factor, is analyzed. The present analysis moreover seeks to evaluate the error specific to reference data by considering instrumental and representativeness errors, for the purpose of taking reference data accuracy into account in the radar/rain gauge comparison. This study shows that radar data obtained by means of this new operational radar processing chain are not yet reliable enough for direct use in quantitative applications within the field of urban hydrology.