Simon Richard Proud

Modeling Angular Dependences in Land Surface Temperatures From the SEVIRI Instrument Onboard the Geostationary Meteosat Second Generation Satellites

Satellite-based estimates of land surface temperature (LST) are widely applied as an input to models. A model output is often very sensitive to error in the input data, and high-quality inputs are therefore essential. One of the main sources of errors in LST estimates is the dependence on vegetation structure and viewing and illumination geometry. Despite this, these effects are not considered in current operational LST products from neither polar-orbiting nor geostationary satellites. In this paper, we simulate the angular dependence that can be expected when estimating LST with the viewing geometry of the geostationary Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager sensor across the African continent and compare it to a normalized view geometry. We use the modified geometric projection model that estimates the scene thermal infrared radiance from a surface covered by different land covers. The results show that the sun-target-sensor geometry plays a significant role in the estimated temperature, with variations strictly due to the angular configuration of more than ±3°C in some cases. On the continental scale, the average error is small except in hot-spot conditions, but large variations occur both geographically and temporally. The sun zenith angle, the amount of vegetation, and the vegetation structure are all shown to affect the magnitude of the errors. The findings highlight the need for taking the angular effects into account when applying LST estimates in models and when comparing LST estimates from different sensors or from different times, both on the daily and seasonal scale.

Rapid response flood detection using the MSG geostationary satellite

Abstract A novel technique for the detection of flooded land using satellite data is presented. This new method takes advantage of the high temporal resolution of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) series of satellites to derive several parameters that describe the sensitivity of land surface reflectivity to variation in solar position throughout the day. Examination of these parameters can then yield information describing the nature of the surface being viewed, including the presence of water due to flooding, on a 3-day basis. An analysis of data gathered during the 2009 flooding events in West Africa shows that the presented method can detect floods of comparable size to the SEVIRI pixel resolution on a short timescale, making it a valuable tool for large scale flood mapping.

Detecting Canopy Water Status Using Shortwave Infrared Reflectance Data From Polar Orbiting and Geostationary Platforms

Various canopy water status estimates have been developed from recent advances in Earth Observation (EO) technology. A promising methodology is based on the sensitivity of shortwave infrared (SWIR) reflectance to variations in leaf water content. This study explores the potential of SWIR-based canopy water status detection from geostationary Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data as compared to polar orbiting environmental satellite (POES)-based moderate resolution imaging spectroradiometer (MODIS) data. The EO-based SWIR water stress index (SIWSI) is evaluated against in situ measured canopy water content indicators at a semi-arid grassland savanna site in Senegal 2008. Daily SIWSI from both MODIS and SEVIRI data show an overall inverse relation to Normalized Difference Vegetation Index (NDVI) throughout the growing season. SIWSI observations from SEVIRI are furthermore sensitive to short-term variations of in situ measured plant water content indicators when aboveground biomass increases from 500 to 900 gm-2 (LAI ≈ 1-2). MODIS SIWSI observations in contrast do not covary with in situ measured moisture indicators. Spatio-temporal trend analyses performed on SEVIRI SIWSI during a dry period within the growing season support these findings. These results suggest that the combined advantage of an improved temporal resolution and a fixed viewing angle potentially makes the SEVIRI sensor an interesting complementary data source to POES data for SWIR-based canopy water status and stress monitoring in a semi-arid environment.

The influence of seasonal rainfall upon Sahel vegetation

Throughout the Sahelian region of Africa, vegetation growth displays substantial inter-annual variation, causing widespread concern in the region as rain-fed agriculture and pastoralism are a means of sustenance for the predominantly rural population. Previously proposed factors behind variations include changes in total yearly rainfall, land-use change and migration. But these factors are not fully explanatory. This study addresses other possible factors for variation in vegetation patterns through the analysis of the Normalized Difference Vegetation Index (NDVI) produced by satellite sensors. We focus on precipitation, but instead of looking at the total yearly amount of rainfall, the intra-annual variation is examined. Here we show that plant growth is strongly correlated with the number and frequency of days within the rainy season upon which there is no rainfall. Furthermore, we find that if the start of the growing season, or the period in which the peak growth of vegetation occurs, is especially dry then plant growth may be stunted throughout the remainder of the season. These results enable better understanding of climate dynamics in the Sahel and allow more accurate forecasting of crop yields, carbon storage and landscape changes without the need to resort to rainfall estimates that are sometimes of low accuracy. In addition, it may be possible to apply the results to other dry land regions worldwide.

Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking

A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.

The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors

We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (CloudAerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multiinstrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.

The Normalization of Surface Anisotropy Effects Present in SEVIRI Reflectances by Using the MODIS BRDF Method

A modified version of the MODerate resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) algorithm is presented for use in the angular normalization of surface reflectance data gathered by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellites. We present early and provisional daily nadir BRDF-adjusted reflectance (NBAR) data in the visible and near-infrared MSG channels. These utilize the high temporal resolution of MSG to produce BRDF retrievals with a greatly reduced acquisition period than the comparable MODIS products while, at the same time, removing many of the angular perturbations present within the original MSG data. The NBAR data are validated against reflectance data from the MODIS instrument and in situ data gathered at a field location in Africa throughout 2008. It is found that the MSG retrievals are stable and are of high-quality across much of the SEVIRI disk while maintaining a higher temporal resolution than the MODIS BRDF products. However, a number of circumstances are discovered whereby the BRDF model is unable to function correctly with the SEVIRI observations-primarily because of an insufficient spread of angular data due to the fixed sensor location or localized cloud contamination.

Observation of Polar Mesospheric Clouds by Geostationary Satellite Sensors

Polar mesospheric clouds (PMCs) form at very high altitude (around 80 km) over high latitudes in the Northern and Southern Hemispheres. Because of their altitude, seasonality, and tenuous nature, PMCs are not well understood. This letter presents a method for detecting PMCs using geostationary satellite images of the Earths limb. The method is tested against data from the Aeronomy of Ice in the Mesosphere mission that was specifically designed for PMC detection. It is found that the new method is successful in detecting seasonal trends in PMC formation and, due to the larger data set of geostationary images, may allow examination of the temporal properties of PMCs in greater detail than possible by satellites in polar orbit.