Publications by Simon Proud


The Community Cloud retrieval for CLimate (CC4CL) - Part 1: A framework applied to multiple satellite imaging sensors

ATMOSPHERIC MEASUREMENT TECHNIQUES 11 (2018) 3373-3396

O Sus, M Stengel, S Stapelberg, G McGarragh, C Poulsen, AC Povey, C Schlundt, G Thomas, M Christensen, S Proud, M Jerg, R Grainger, R Hollmann


The Community Cloud retrieval for CLimate (CC4CL) - Part 2: The optimal estimation approach

ATMOSPHERIC MEASUREMENT TECHNIQUES 11 (2018) 3397-3431

GR McGarragh, CA Poulsen, GE Thomas, AC Povey, O Sus, S Stapelberg, C Schlundt, S Proud, MW Christensen, M Stengel, R Hollmann, RG Grainger


Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project

EARTH SYSTEM SCIENCE DATA 9 (2017) 881-904

M Stengel, S Stapelberg, O Sus, C Schlundt, C Poulsen, G Thomas, M Christensen, CC Henken, R Preusker, J Fischer, A Devasthale, U Willen, K-G Karlsson, GR McGarragh, S Proud, AC Povey, RG Grainger, JF Meirink, A Feofilov, R Bennartz, JS Bojanowski, R Hollmann


Unveiling aerosol-cloud interactions - Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate

ATMOSPHERIC CHEMISTRY AND PHYSICS 17 (2017) 13151-13164

MW Christensen, D Neubauer, CA Poulsen, GE Thomas, GR McGarragh, AC Povey, SR Proud, RG Grainger


Analysis of aircraft flights near convective weather over Europe

WEATHER 70 (2015) 292-296

SR Proud


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

REMOTE SENSING 7 (2015) 1529-1539

A Taravat, S Proud, S Peronaci, F Del Frate, N Oppelt


Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin

Remote sensing of environment. 159 (2015) 57-69

JRL Olsen, Rasmus Fensholt, Simon R. Proud, Simon Stisen

Satellite remote sensing of vegetation parameters and stress is a key issue for semi-arid areas such as the Sahel, where vegetation is an important part of the natural resource base. In this study we examine if additional information can be obtained on intra-seasonal short term scale by using the Shortwave Infrared Water Stress Index (SIWSI) as compared to Normalized Difference Vegetation Index (NDVI). We perform a spatio-temporal evaluation of NDVI and SIWSI using geostationary remote sensing imagery from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The indices and their seasonally detrended anomalies are evaluated using a gridded rainfall product (RFE2) and modeled actual evapotranspiration (ETa) for the Senegal River basin in 2008. Daily NDVI and SIWSI were found spatially highly correlated to ETa with r=0.73 for both indices, showing the importance of the north/south vegetation gradient in the river catchment. The hypothesis that short term evolution of index anomalies are related to canopy water status was tested by comparing 10-day averages of ETa with short term changes in daily NDVI and SIWSI anomalies, and moderate to strong coefficients of determination where found when anomaly variations where aggregated by Land Cover Classes (LCCs) with R2 values of 0.65 for savanna, 0.60 for grassland, 0.72 for shrubland, and 0.58 for barren or sparsely vegetated areas. This is higher than for the same method applied to NDVI anomalies, with R2 values of 0.57 for savanna, 0.50 for grassland, 0.32 for shrubland, and 0.57 for barren or sparsely vegetated areas. The approach of detrending NIR/SWIR based indices and spatially aggregating the anomalies do offer improved detection of intra-seasonal stress. However, quite coarse spatial aggregation is found necessary for a significant analysis outcome.


Hygroscopic and phase separation properties of ammonium sulfate/organics/water ternary solutions

ATMOSPHERIC CHEMISTRY AND PHYSICS 15 (2015) 8975-8986

MA Zawadowicz, SR Proud, SS Seppalainen, DJ Cziczo


Observation of Polar Mesospheric Clouds by Geostationary Satellite Sensors

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 12 (2015) 1332-1336

S Proud


Analysis of overshooting top detections by Meteosat Second Generation: a 5-year dataset

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 141 (2015) 909-915

SR Proud


Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery

Remote sensing of environment. 140 (2014) 23-35

GL Feyisa, Henrik Meilby, Rasmus Fensholt, Simon R. Proud

Classifying surface cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic classification tasks is to distinguish water bodies from dry land surfaces. Landsat imagery is among the most widely used sources of data in remote sensing of water resources; and although several techniques of surface water extraction using Landsat data are described in the literature, their application is constrained by low accuracy in various situations. Besides, with the use of techniques such as single band thresholding and two-band indices, identifying an appropriate threshold yielding the highest possible accuracy is a challenging and time consuming task, as threshold values vary with location and time of image acquisition. The purpose of this study was therefore to devise an index that consistently improves water extraction accuracy in the presence of various sorts of environmental noise and at the same time offers a stable threshold value. Thus we introduced a new Automated Water Extraction Index (AWEI) improving classification accuracy in areas that include shadow and dark surfaces that other classification methods often fail to classify correctly. We tested the accuracy and robustness of the new method using Landsat 5 TM images of several water bodies in Denmark, Switzerland, Ethiopia, South Africa and New Zealand. Kappa coefficient, omission and commission errors were calculated to evaluate accuracies. The performance of the classifier was compared with that of the Modified Normalized Difference Water Index (MNDWI) and Maximum Likelihood (ML) classifiers. In four out of five test sites, classification accuracy of AWEI was significantly higher than that of MNDWI and ML (P-value<0.01). AWEI improved accuracy by lessening commission and omission errors by 50% compared to those resulting from MNDWI and about 25% compared to ML classifiers. Besides, the new method was shown to have a fairly stable optimal threshold value. Therefore, AWEI can be used for extracting water with high accuracy, especially in mountainous areas where deep shadow caused by the terrain is an important source of classification error.


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

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 52 (2014) 6026-6039

SR Proud, Q Zhang, C Schaaf, R Fensholt, MO Rasmussen, C Shisanya, W Mutero, C Mbow, A Anyamba, E Pak, I Sandholt


Reconstructing the orbit of the Chelyabinsk meteor using satellite observations

GEOPHYSICAL RESEARCH LETTERS 40 (2013) 3351-3355

SR Proud


Relation between Seasonally Detrended Shortwave Infrared Reflectance Data and Land Surface Moisture in Semi-Arid Sahel

REMOTE SENSING 5 (2013) 2898-2927

JL Olsen, P Ceccato, SR Proud, R Fensholt, M Grippa, E Mougin, J Ardo, I Sandholt


Evaluation of Earth Observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series

Remote sensing of environment. 119 (2012) 131-147

R Fensholt, SR Proud

A new and updated version of the AVHRR (Advanced Very High Resolution Radiometer) based GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index) dataset is now available covering 1981 to 2010 (GIMMS3g). Earlier versions of this global coverage 15-day composite dataset have been used for numerous local to global scale vegetation time series studies during recent years. However, several aspects of the AVHRR sensor design and data processing potentially introduce substantial noise into the NDVI dataset if not corrected for. The more recent NDVI dataset from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) is considered an improvement over AVHRR data and with the release of GIMMS3g an overlapping period of 11years now provides a possibility to perform a robust evaluation of the accuracy of GIMMS3g data and derived trends. In this study the accuracy is evaluated by comparison with the global Terra MODIS NDVI (MOD13C2 Collection 5) data using linear regression trend analysis. The trends of GIMMS NDVI were found to be in overall acceptable agreement with MODIS NDVI data. A significant trend in NDVI (α=0.05) was found for 11.8% of the MODIS NDVI pixels on a global scale (5.4% characterised by positive trends and 6.3 with negative trends) whereas GIMMS NDVI analysis produced a total of 10.5% significant pixels (4.9% positive, 5.6% negative). However, larger differences were found for the Southern Hemisphere land masses (South America and Australia) and the high northern latitude Arctic regions. From a linear regression analysis the correlation coefficient between the two datasets was found to be highly significant for areas with a distinct phenological cycle. Discrepancies between the GIMMS and MODIS datasets were found in equatorial areas (broadleaved, evergreen forest), Arctic areas (sparse herbaceous or sparse shrub cover) and arid areas (herbaceous cover, closed–open). Linear regression of QA filtered Terra and Aqua MODIS NDVI (2003–2010) revealed similar inconsistencies for Arctic and equatorial areas suggesting that robust long-term NDVI trend estimates in these areas are difficult to obtain from both GIMMS and MODIS data. Additionally, GIMMS based NDVI trend analysis in arid areas of limited photosynthetic activity should be interpreted with caution. The regression coefficient (slope value) (p<0.01) was found to be close to 1 for most land cover types on a global scale (global land cover class average slope=1.00) suggesting overall compatibility between MODIS and GIMMS NDVI, but with land cover class specific variations (within class and between classes).


A system dynamics approach to land use changes in agro-pastoral systems on the desert margins of Sahel

AGRICULTURAL SYSTEMS 107 (2012) 56-64

LV Rasmussen, K Rasmussen, A Reenberg, S Proud


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

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 13 (2011) 721-729

R Fensholt, A Anyamba, S Huber, SR Proud, CJ Tucker, J Small, E Pak, MO Rasmussen, I Sandholt, C Shisanya


Rapid response flood detection using the MSG geostationary satellite

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 13 (2011) 536-544

SR Proud, R Fensholt, LV Rasmussen, I Sandholt


The influence of seasonal rainfall upon Sahel vegetation

REMOTE SENSING LETTERS 2 (2011) 241-249

SR Proud, LV Rasmussen


A comparison of the effectiveness of 6S and SMAC in correcting for atmospheric interference of Meteosat Second Generation images

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 115 (2010) ARTN D17209

SR Proud, R Fensholt, MO Rasmussen, I Sandholt

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