Nowadays, availability of hyper-temporal satellite data offers options for studying natural environments that were unattainable by traditional measurement approaches. This work focuses on monitoring vegetation using the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and rainfall data in the state of San Luis Potosi, Mexico. A hyper-temporal dataset comprising 489 MODIS MCD43A4 imagery (500 m spatial resolution) taken over the last 11 years (2000 to 2010) was used. Ten temporal yearly composites were constructed by fusing normalized difference vegetation indices (NDVI) data along each year using the ‘maximum composite value approach’. The time-series dataset was also fused using the principal component analysis (PCA) technique; the second principal component captured the main inter-annual anomalies from maximum yearly NDVI values. The analysis of this time-series indicates two periods: a) 2000 to 2005 characterized by a drought during 2000, 2001 and 2005, and b) 2006 to 2010 marked by a trend indicating wetter conditions with maxima in 2008 and 2010. Finally, an upward trend in NDVI time-series was observed during the period studied and successfully validated with ancillary historical data of crop production area.
Key words: Moderate resolution imaging spectroradiometer, MCD43A4, time-series, principal component analysis, maximum value composite.
Copyright © 2022 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0