Soil water content in vegetation indices dynamics through a recurrence plots approach

Grasslands are one of the most important and complex ecological systems due to their characteristic dynamics influenced by meteorological and climate patterns. In this sense, drought is one of the most challenging obstacles to overcome. Especially in semi-arid areas, where biomass production is greatly limited by the amount of precipitation. In this line, remote sensing methods have been demonstrated to be a valuable instrument for monitoring vegetation in wide areas, and vegetation indices (VIs) have shown a high sensitivity to vegetation variations. In this line, the soil water content has been shown to be a key factor in vegetation growth. In this work, we compare the temporal dynamics of two semi-arid grassland areas based on a soil water content index estimated in each area.

We selected two semi-arid areas in Spain and time series of VIs are built based on multispectral images of MODIS TERRA product with a temporal resolution of 8 days in each area. Red (620-670 nm) and Near Infrared (841-876 nm) reflectance channels were extracted and filtered by the quality of the pixel. Then, a soil water content index (WCI) is calculated based on the water balance of the soil over time. Recurrence plots (RP) and recurrence quantification analysis (RQA) were calculated to characterize the influence of soil water content on vegetation index dynamics. The characterisation was based on various RQA complexity measurements, including Determinism (DET), among others.

In general, our results revealed that WCI was able to distinguish between areas. RPs revealed a different temporal pattern in each area using WCI and VIs. Furthermore, RQA measurements revealed that the dry area presented a different dynamic in contrast to the wetter area. In general, WCI was shown to be a useful index in characterizing soil water content, and recurrence plots were able to describe and characterise the dynamics of each area.

Acknowledgements: The authors acknowledge the support of Clasificación de Pastizales Mediante Métodos Supervisados - SANTO, from Universidad Politécnica de Madrid (project number: RP220220C024).

 

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