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Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection
, 2013
"... Abstract: Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generall ..."
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Abstract: Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring theRemote Sens. 2013, 5 1705
Article Meeting Earth Observation Requirements for Global Agricultural Monitoring: An Evaluation of the Revisit Capabilities of Current and Planned Moderate Resolution Optical Earth Observing Missions
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Review The Potential and Uptake of Remote Sensing in Insurance: A Review
, 2014
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SPECTRAL UNMIXING OF LOW RESOLUTION IMAGES FOR MONITORING SOIL SEALING
"... The expansion of urban areas has a negative impact on the environment. The increase of impervious or sealed surfaces is directly proportional to this expansion. The estimation of sealed surfaces has often been executed using remote sensing imagery, although only on a local to regional scale, using m ..."
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The expansion of urban areas has a negative impact on the environment. The increase of impervious or sealed surfaces is directly proportional to this expansion. The estimation of sealed surfaces has often been executed using remote sensing imagery, although only on a local to regional scale, using medium and recently available high resolution images like LANDSAT TM and IKONOS. In order to develop a global policy and strategy on urban expansion matters, consistent time series of area statistics on urban land use on a national and global level will become indispensable. This research explores the possibilities of SPOT VEGETATION imagery, with a spatial resolution of 1 km, for urban monitoring in order to generate statistics of sealed surfaces over larger zones. While low resolution imagery offers the advantage of covering a large area in small temporal intervals, its spatial resolution is too coarse to monitor most urban objects. In order to tackle this problem, a sub-pixel classification was applied and unmixed sealed surface area statistics were produced. Endmember selection is a key element in the sub-pixel classification process in which the spectrally complex sealed surface class should be distinguished from other general classes. To find the most favourable temporal interval or period for endmember selection, several datasets were developed and explored. SPOT-VEGETATION images were acquired in summer and winter for Flanders (Belgium). This region is characterised by a highly fragmented urban land-cover and large availability of reference data. Spectral unmixing of the multitemporal datasets illustrates that the endmember spectra differs for three different endmember selection techniques, affecting the quality of the final sub-pixel classification. The paper argues that the
THE USE OF REMOTE SENSING WITHIN THE MARS CROP YIELD MONITORING SYSTEM OF THE EUROPEAN COMMISSION
"... timely forecasts for the main crops yields at EU level. The forecasts and analysis are used since 2001 as a benchmark by analysts from DG – Agriculture and Rural Development in charge of food balance estimates. The system is supported by the use of Remote Sensing data, namely SPOT-VEGETATION, NOAA-A ..."
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timely forecasts for the main crops yields at EU level. The forecasts and analysis are used since 2001 as a benchmark by analysts from DG – Agriculture and Rural Development in charge of food balance estimates. The system is supported by the use of Remote Sensing data, namely SPOT-VEGETATION, NOAA-AVHRR, MSG-SEVIRI and MODIS TERRA and in the future METOP AVHRR too. So a broad spectrum from low to medium resolution data at pan-European level is covered and historical time series go back to 1981 for NOAA and 1998 for SPOT VEGETATION. In order to work with the data operationally, processing chains have been set-up to make the data consistent with our requirements concerning near real time delivery (3 days), spatial coverage (pan-Europe), projection and ten day time steps. Moreover tailored indicators like NDVI, fAPAR and DMP are derived. In case of available time-series, difference values of the indicators (e.g. relative or absolute differences) and frequency analysis of the indicators (e.g. position in historical range or distribution) are calculated. The data is explored at full resolution or unmixed related to landcover types and aggregated at administrative NUTS 2 level (profile analysis of time series). Special tools to inspect and distribute the data to external users have been developed as well. Furthermore, it is the objective to develop a strategy for an optimal use of the different sensors and thus derived indicators at different aggregation levels for the ingestion into the MCYFS. As a first step smoothing algorithms have to be applied to the time series to diminish noise effects and to retrieve continuous information. Thus, an algorithm based on Swets et al. (1999) is employed. Thereafter, so-called Chronos Key Indicators are derived from the smoothed time-series.
Article A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
"... www.mdpi.com/journal/remotesensing ..."
Article Crop Condition Assessment with Adjusted NDVI Using the Uncropped Arable Land Ratio
, 2014
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