Analysing the advantages of high temporal resolution geostationary MSG SEVIRI data compared to Polar Operational Environmental Satellite data for land surface monitoring in Africa

Fensholt, Rasmus; Anyamba, Assaf; Huber, Silvia; Proud, Simon; Tucker, Compton J.; Small, Jennifer; Pak, Ed; Rasmussen, Mads Olander; Sandholt, Inge; Shisanya, C.A.

doi:10.1016/j.jag.2011.05.009

Cited by 41 publications

(26 citation statements)

References 47 publications

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“…In contrast to the other products, gaps do not accumulate during the rainy season here. A reason might be the higher temporal frequency of the SEVIRI observations (15 min) compared to the daily records of the near-polar orbiting SPOT-VGT and MODIS instruments, which increases the chances for cloud-free observations in tropical areas [47]. Among the LAI products derived from near-polar orbiting instruments, MOD15A2 shows best characteristics with respect to data gaps, particularly in the densely vegetated areas south of 10°N.…”

Section: Discussionmentioning

confidence: 99%

Intercomparison of Leaf Area Index Products for a Gradient of Sub-Humid to Arid Environments in West Africa

et al. 2013

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Abstract:The Leaf Area Index (LAI) is a key variable in many land surface and climate modeling studies. To date, a number of LAI datasets have been developed based on time series of medium resolution optical remote sensing observations. Global validation exercises show the high value of these datasets, but at the same time they point out shortcomings, particularly in the presence of persistent cloud coverage and dense vegetation. For regional modeling studies, the choice of an ideal LAI input dataset is not straightforward as global validation, and intercomparison studies do not necessarily allow conclusions on data quality at regional scale. This paper provides a comprehensive relative intercomparison of four freely available LAI products for a wide gradient of ecosystems in Africa. The region of investigation, West Africa, comprises typical African sub-humid to arid landscapes. The selected LAI time series are the Satellite Pour l'Observation de la Terre-VEGETATION (SPOT-VGT)-based Carbon Cycle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) LAI, the SPOT-VGT-based Bio-geophysical Parameters (BioPar) LAI product GEOV1, the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD15A2, and the Meteosat-SEVIRI-based Satellite Application Facility on Land Surface Analysis (LSA-SAF) LAI. The comparative analyses focus on data gap occurrence, on the consistency of temporal LAI profiles, on their ability to adequately reproduce the phenological cycle and on the plausibility of LAI magnitudes for major land cover types in West Africa. A detailed quantitative validation of the LAI datasets, however, was not possible due to insufficient ground LAI measurements in the study region.

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Section: Discussionmentioning

confidence: 99%

Intercomparison of Leaf Area Index Products for a Gradient of Sub-Humid to Arid Environments in West Africa

et al. 2013

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“…2015, 7, page-page 3 of this study are (1) to propose a method for the reconstruction of daily 30 m remote-sensing data from HJ CCD, GF-1 WFV, Landsat, and MODIS data; (2) to extract phenology using the reconstructed daily 30 m data; and (3) to test the ability of those data in crop mapping.…”

Section: Study Areamentioning

confidence: 99%

“…Remote-sensing data with coarse spatial resolution [1][2][3][4] are the main data sources for time series analysis. The temporal for phenology monitoring and crop mapping.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring

Zhang

Huang

et al. 2015

Remote Sensing

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Abstract:With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R 2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data.

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“…Although these data are not always available in real time and go back only a few decades, it may serve a variety of applications related to model verification. There are two main types of satellites providing data for meteorological purposes: geostationary (e.g., the Meteosat series; FENSHOLT et al 2011) and polar-orbiting (e.g. NASA's Terra and Aqua; KING et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Study of Cloud Cover and Ozone Modeling to Microphysics Parameterization

Wałaszek

Kryza

Szymanowski

et al. 2016

Pure Appl. Geophys.

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Abstract-Cloud cover is a significant meteorological parameter influencing the amount of solar radiation reaching the ground surface, and therefore affecting the formation of photochemical pollutants, most of all tropospheric ozone (O 3 ). Because cloud amount and type in meteorological models are resolved by microphysics schemes, adjusting this parameterization is a major factor determining the accuracy of the results. However, verification of cloud cover simulations based on surface data is difficult and yields significant errors. Current meteorological satellite programs provide many high-resolution cloud products, which can be used to verify numerical models. In this study, the Weather Research and Forecasting model (WRF) has been applied for the area of Poland for an episode of June 17th-July 4th, 2008, when high ground-level ozone concentrations were observed. Four simulations were performed, each with a different microphysics parameterization: Purdue Lin, Eta Ferrier, WRF Single-Moment 6-class, and Morrison Double-Moment scheme. The results were then evaluated based on cloud mask satellite images derived from SEVIRI data. Meteorological variables and O 3 concentrations were also evaluated. The results show that the simulation using Morrison Double-Moment microphysics provides the most and Purdue Lin the least accurate information on cloud cover and surface meteorological variables for the selected high ozone episode. Those two configurations were used for WRF-Chem runs, which showed significantly higher O 3 concentrations and better model-measurements agreement of the latter.

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Analysing the advantages of high temporal resolution geostationary MSG SEVIRI data compared to Polar Operational Environmental Satellite data for land surface monitoring in Africa

Cited by 41 publications

References 47 publications

Intercomparison of Leaf Area Index Products for a Gradient of Sub-Humid to Arid Environments in West Africa

Intercomparison of Leaf Area Index Products for a Gradient of Sub-Humid to Arid Environments in West Africa

Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring

Sensitivity Study of Cloud Cover and Ozone Modeling to Microphysics Parameterization

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