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|Title:||Near real-time vegetation anomaly detection with MODIS NDVI: timeliness vs. accuracy and effect of anomaly computation options|
|Authors:||MERONI MICHELE; FASBENDER DOMINIQUE; REMBOLD FELIX; ATZBERGER CLEMENT; KLISCH ANJA|
|Citation:||REMOTE SENSING OF ENVIRONMENT vol. 221 p. 508-521|
|Publisher:||ELSEVIER SCIENCE INC|
|Type:||Articles in periodicals and books|
|Abstract:||For food crises early warning purposes, coarse spatial resolution NDVI data are widely used to monitor vegetation conditions in near real-time (NRT). Different types of NDVI anomalies are typically employed to assess the current state and seasonal development of crops and rangelands as compared to previous years. Timeliness and accuracy of such anomalies are critical factors to an effective monitoring. Temporal smoothing can efficiently reduce noise and cloud contamination in the time series of historical observations, where data points are available before and after each data point to be smoothed. With NRT data, smoothing methods are adapted to cope with the unbalanced availability of data before and after the most recent data points. These NRT approaches provide successive updates of the estimation of the same data point as more observations become available. Anomalies compare the current NDVI value with some statistics (e.g. indicators of central tendency and dispersion) extracted from the historical record of observations. With multiple updates of the same datasets being available, two options can be selected to compute anomalies, i.e. using the same update level for the NRT data and the historical records or using the most reliable update for the statistics. In this study we assess the accuracy of three commonly employed 1 km MODIS NDVI anomalies (standard scores, non-exceedance probability and vegetation condition index) with respect to (1) delay with which they become available and (2) option selected for their computation. We show that a large estimation error affects the earlier estimates and that this error is efficiently reduced in subsequent updates. In addition, with regards to the preferable option to compute anomalies, we empirically observe that it depends on the type of application (e.g. averaging anomalies value over an area of interest vs. detecting “drought” conditions by setting a threshold on the anomaly value) and the employed anomaly type. Finally, we map the spatial pattern in the magnitude of NRT anomaly estimation errors over the globe and relate it to average cloudiness.|
|JRC Directorate:||Sustainable Resources|
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