An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data

Abstract

Monitoring vegetation condition is an important issue in the Mediterranean region, in terms of both securing food and preventing fires. The recent abundance of remotely sensed data, such as the daily availability of MODIS imagery, raises the issue of appropriate temporal sampling when monitoring vegetation: undersampling may not accurately describe the phenomenon under consideration, whilst over-sampling would increase the cost of the project without additional benefit. The aim of this work is to estimate the optimum temporal resolution for vegetation monitoring on a nationwide scale using 250 m MODIS/Terra daily images and composites. Specific objectives include: (i) an investigation into the optimum temporal resolution for monitoring vegetation condition during the dry season on a nationwide scale using time-series analysis of Normalized Difference Vegetation Index, NDVI, datasets, (ii) an investigation into whether this temporal resolution differs between the two major vegetation categories of natural and managed vegetation, and (iii) a quality assessment of multi-temporal NDVI composites following the proposed optimum temporal resolution. A timeseries of daily NDVI data is developed for Greece using MODIS/Terra 250 m imagery. After smoothing to remove noise and cloud influence, it is subjected to temporal autocorrelation analysis, and its level of significance is the adopted objective function. In addition, NDVI composites are created at various temporal resolutions and compared using qualitative criteria. Results indicate that the proposed optimum temporal resolution is different for managed and natural vegetation. Finally, quality assessment of the multi-temporal NDVI composites reveals that compositing at the proposed optimum temporal resolution could derive products that are useful for operational monitoring of vegetation.

Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale

“…Several authors [43,44] suggest that vegetation indices such as NDVI recorded at critical times during the growing season can help characterise the spatial variability in crop performance. Others [13,37] suggest that accurate wheat yield predictions are possible using only one image, provided it is acquired towards the middle of the growing season when most wheat crop canopies are fully developed.…”

Spatial and Temporal Distribution of Soil Moisture at the Catchment Scale Using Remotely-Sensed Energy Fluxes

“…However, some cloud effects cannot be successfully removed, such as the cloud shadow [42] and artificial heterogeneity [43]. Persistent cloud cover for more than eight days could be dealt with by using longer compositing periods (e.g., 16 days or one month); however, there is a risk of losing important phenological changes of crops with short growing seasons [44,45].…”

Implicaciones del filtrado de calidad del índice de vegetación EVI para el seguimiento funcional de ecosistemas

“…El 50% de aproximadamente 35 trabajos encontrados sólo utiliza la información del Usefulness Index (Índice de utilidad), un indicador sintético sobre la calidad de los datos más fácil de utilizar (e.g. Alexandridis et al, 2008;Beck et al, 2007;Colditz et al, 2006;Giner et al, 2012;Gu et al, 2009;Hess et al, 2009;Jacquin et al, 2010;Li et al, 2008;Reinart et al, 2008;Wang et al, 2005). El problema que plantea este índice es que no permite conocer cuál es el origen de la incertidumbre en la calidad final de los datos.…”