Herman Eerens

Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data

One of the aims of the EU funded project Long-term regional effects of climate change on European forests: impact assessment and consequences for carbon budgets (LTEEF-II, ENV4-CT97-0577) is to quantify the fluxes of carbon and water between vegetation (forests) and atmosphere and to assess the carbon balance of forests in Europe. This paper presents the results of the application of the C-Fix model within the frame of part of the objectives of the LTEEF-II project, as defined for the European continental scale. A description of the C-Fix model is presented in the first part of this paper. C-Fix is a Monteith type parametric model driven by temperature, radiation and fraction of Absorbed Photosynthetically Active Radiation (fAPAR), the last variable derived by processing NOAA/AVHRR data of 1997 acquired over Europe as well as VEGETATION (VGT) data for the same region for the period 1998–1999. In a second part of the paper, the main results obtained are described, e.g. net ecosystem fluxes for forest, calculated locally for the Euroflux CO2 measurement sites as well as for the whole continent. The model simulations were evaluated with eddy covariance measurements of NEP performed during 1997 at the Euroflux sites. Moreover, a comparison between NEP determined with NOAA/AVHRR data, opposed to NEP simulations based on VGT data is performed. A difference up to 20% is obtained between both data sets. Finally A C-Fix estimate of total European vegetation NEP obtained with NOAA/AVHRR data for 1997 is 2.70F32% P gC , (P=10 15 g, % is 95% confidence limit). This estimate decreases to 2.15F43% Pg C when VGT data for the period April 1998–March 1999 are used. The total forest NEP for Europe, however, is estimated at 0.735 Pg C for 1997. Along a North–South transect at 13j east, through Europe a clear increase in NEP and the other basic carbon mass fluxes GPP and NPP is modelled from northern to southern latitudes. Moreover, along this transect the values simulated with C-Fix for NEP corresponds well with the eddy covariance NEP estimates for the Euroflux sites. D 2002 Published by Elsevier Science Inc.

Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium

Abstract Global time series of low resolution images are available with high repeat frequency and at low cost, but their analysis is hampered by the presence of mixed pixels and the difficulty in locating detailed spatial features. This study examined the potential of sub-pixel classification for regional crop area estimation using time series of monthly NDVI-composites of the 1xa0km resolution sensor SPOT-VEGETATION. Belgium was selected as test zone, because of the availability of ample reference data in the form of a vectorial GIS with the boundaries and cover type of the large majority of agricultural fields. Two different methods were investigated: the linear mixture model and neural networks. Both result in area fraction images (AFIs), which contain for each 1xa0km pixel the estimated area proportions occupied by the different cover types (crops or other land use). Both algorithms were trained with part of the reference data and validated with the remainder. Validation was repeated at three different levels: the 1xa0km pixel, the municipality and the agro-statistical district. In general, the neural network outperformed the linear mixture model. For the major classes (winter wheat, maize, forest) the obtained acreage estimates showed good agreement with the true values, especially when aggregated to the level of the municipality ( R 2 xa0≈xa085%) or district ( R 2 xa0≈xa095%). The method seems attractive for wide-scale, regional area estimation in data-poor countries.

Image time series processing for agriculture monitoring

Given strong year-to-year variability, increasing competition for natural resources, and climate change impacts on agriculture, monitoring global crop and natural vegetation conditions is highly relevant, particularly in food insecure areas. Data from remote sensing image series at high temporal and low spatial resolution can help to assist in this monitoring as they provide key information in near-real time over large areas. The SPIRITS software, presented in this paper, is a stand-alone toolbox developed for environmental monitoring, particularly to produce clear and evidence-based information for crop production analysts and decision makers. It includes a large number of tools with the main aim of extracting vegetation indicators from image time series, estimating the potential impact of anomalies on crop production and sharing this information with different audiences. SPIRITS offers an integrated and flexible analysis environment with a user-friendly graphical interface, which allows sequential tasking and a high level of automation of processing chains. It is freely distributed for non-commercial use and extensively documented. Image time series analysis is of increasing relevance for environmental monitoring.Dedicated tools are needed to process remote sensing image time series.SPIRITS is free software to process image time series for crop monitoring.SPIRITS has a user-friendly interface and is extensively documented.

Evaluation of Agreement Between Space Remote Sensing SPOT-VEGETATION fAPAR Time Series

Satellite-derived time series of the fraction of absorbed photosynthetically active radiation (fAPAR) are widely used to monitor vegetation dynamics and to detect vegetation anomalies. Several global data sets are available for this purpose. They are produced using different algorithms and/or satellite sensors. This paper compares and analyzes three multitemporal fAPAR data sets derived from SPOT-VEGETATION instrument by explicitly distinguishing between spatial and temporal agreement. The first two data sets are currently used by the Joint Research Centre-Monitoring Agricultural ResourceS Unit (JRC-MARS) for operational yield forecasting and food security assessments. The third time series (named GEOV1) is from a new processing algorithm developed within the European FP7 Geoland2 project. The comparative analysis was conducted for the years 2003 and 2004 over three 10° × 10° regions with different eco-climatic characteristics (Niger, Brazil, and France). Our study revealed that GEOV1 fAPAR estimates were systematically higher than those of JRC-MARS. The spatial analysis showed moderate to high agreement between data sets with specific seasonality in the three study regions. The temporal agreement showed spatial (and land cover-related) variability spanning from very low to almost perfect. Large differences were observed in regions and periods with large cloud occurrence where GEOV1 provides more reliable and smooth temporal profiles due to improved cloud screening and longer compositing periods. Other sources of disagreement between data sets were identified in differences in the fAPAR retrieval algorithm definitions.

Remote sensing time series analysis for crop monitoring with the SPIRITS software: new functionalities and use examples

Monitoring crop and natural vegetation conditions is highly relevant, particularly in the food insecure areas of the world. Data from remote sensing image time series at high temporal and medium to low spatial resolution can assist this monitoring as they provide key information about vegetation status in near real-time over large areas. The Software for the Processing and Interpretation of Remotely sensed Image Time Series (SPIRITS) is a stand-alone flexible analysis environment created to facilitate the processing and analysis of large image time series and ultimately for providing clear information about vegetation status in various graphical formats to crop production analysts and decision makers. In this paper we present the latest functional developments of SPIRITS and we illustrate recent applications. The main new developments include: HDF5 importer, Image re-projection, additional options for temporal Smoothing and Periodicity conversion, computation of a rainfall-based probability index (Standardized Precipitation Index) for drought detection and extension of the Graph composer functionalities. In particular,. The examples of operational analyses are taken from several recent agriculture and food security monitoring reports and bulletins. We conclude with considerations on future SPIRITS developments also in view of the data processing requirements imposed by the coming generation of remote sensing products at high spatial and temporal resolution, such as those provided by the Sentinel sensors of the European Copernicus programme.

Crop mapping in countries with small-scale farming: a case study for West Shewa, Ethiopia

Remote sensing is nowadays considered to be a valuable input for the annual collection of crop statistics. Derived crop maps can serve as a baseline for yield or area estimation or to target next years census. For subsistence farming, where small parcels are mixed with other land use, crop mapping remains very challenging. This article evaluates the potential of discriminating crops in West Shewa, an area with small-scale farming in central Ethiopia. A hard classification of high-resolution (30 m) images, yielding good results for commercial farming, could not deal with mixed pixels due to the small parcels. Very high resolution (4 m) images have a more appropriate pixel size, although they only cover subsets of the region. The very high resolution classification was used to calibrate a neural network for sub-pixel classification of the high resolution images. The accuracies were not satisfactory, but did at least demonstrate the potential of this approach.

Estimating crop-specific evapotranspiration using remote-sensing imagery at various spatial resolutions for improving crop growth modelling

By governing water transfer between vegetation and atmosphere, evapotranspiration (ET) can have a strong influence on crop yields. An estimation of ET from remote sensing is proposed by the EUMETSAT ‘Satellite Application Facility’ (SAF) on Land Surface Analysis (LSA). This ET product is obtained operationally every 30 min using a simplified SVAT scheme that uses, as input, a combination of remotely sensed data and atmospheric model outputs. The standard operational mode uses other LSA-SAF products coming from SEVIRI imagery (the albedo, the downwelling surface shortwave flux, and the downwelling surface longwave flux), meteorological data, and the ECOCLIMAP database to identify and characterize the land cover. With the overall objective of adapting this ET product to crop growth monitoring necessities, this study focused first on improving the ET product by integrating crop-specific information from high and medium spatial resolution remote-sensing data. A Landsat (30 m)-based crop type classification is used to identify areas where the target crop, winter wheat, is located and where crop-specific Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m) time series of green area index (GAI) can be extracted. The SVAT model was run for 1 year (2007) over a study area covering Belgium and part of France using this supplementary information. Results were compared to those obtained using the standard operational mode. ET results were also compared with ground truth data measured in an eddy covariance station. Furthermore, transpiration and potential transpiration maps were retrieved and compared with those produced using the Crop Growth Monitoring System (CGMS), which is run operationally by the European Commissions Joint Research Centre to produce in-season forecast of major European crops. The potential of using ET obtained from remote sensing to improve crop growth modelling in such a framework is studied and discussed. Finally, the use of the ET product is also explored by integrating it in a simpler modelling approach based on light-use efficiency. The Carnegie–Ames–Stanford Approach (CASA) agroecosystem model was therefore applied to obtain net primary production, dry matter productivity, and crop yield using only LSA-SAF products. The values of yield were compared with those obtained using CGMS, and the dry matter productivity values with those produced at the Flemish Institute for Technological Research (VITO). Results showed the potential of using this simplified remote-sensing method for crop monitoring.

FAO’s AVHRR-based Agricultural Stress Index System ASIS for global drought monitoring

ABSTRACT Agricultural production is highly dependent on climate variability in many parts of the world. In particular, drought may severely reduce crop yields, potentially affecting food availability at local, regional, and global scales. The Food and Agriculture Organization of the United Nations (FAO) operates the Global Early Warning System (GIEWS), which monitors global food supply and demand. One of the key challenges is to obtain synoptic information on a recurrent and timely basis about drought-affected agricultural zones. This is needed to quickly identify areas requiring immediate attention. The Agricultural Stress Index System (ASIS), based on imagery from the Advanced Very High Resolution Radiometer (AVHRR) sensors on board the National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational Satellite (METOP) satellites, was specifically developed to meet this need. The system is based on a methodology developed by Rojas, Vrieling, and Rembold over the African continent. This approach has been modified and adapted to the global scale to produce an agricultural stress index (ASI) representing, per administrative unit, the percentage of cropland (or pasture) areas affected by drought over the growing season. The vegetation health index (VHI), based on normalized difference vegetation index (NDVI) and temperature anomalies, is used as a drought indicator. A fused time series of AVHRR data from METOP and NOAA was used to produce a consistent time series of VHI at 1 km resolution. Global phenology maps, indicating the number of growing seasons and their start and end dates, were derived from a multi-annual image set of SPOT-Vegetation (1999–2011). The VHI time series and phenology maps were then combined to produce the ASI for the years 1984 to the present. This allowed evaluation of the suitability of the ASIS to identify drought using historical reports and ancillary data. As a result of this analysis, ASIS was positively evaluated to support the FAO early warning system.

Sub-pixel land-cover classification with SPOT-VEGETATION imagery

Knowledge about global land cover is an important input for the modelling of ecological and environmental processes. Production of such global vegetation maps can be facilitated by using automated methods for classification. Two neural network strategies, an overall and class-specific network(s), were tested on a part of Europe. This study indicates that sub-pixel proportion estimates can be assessed quite accurately from 1-km resolution SPOT-VEGETATION imagery.

Efficient collection of training data for sub-pixel land cover classification using neural networks

Abstract Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation ( R 2 xa0>xa080%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.