Remotely sensed soil moisture to estimate savannah NDVI

• Published in: PloS one 2018-07, Vol.13 (7), p.e0200328-e0200328
• Main Authors: Boke-Olén, Niklas, Ardö, Jonas, Eklundh, Lars, Holst, Thomas, Lehsten, Veiko
• Format: Article
• Language: English
• Subjects: Analysis; Climate change; Clouds; Computer simulation; Correlation; Correlation analysis; Datasets; Drought; Earth and Related Environmental Sciences; Earth Observing System (EOS); Earth Sciences; Ecological monitoring; Ecosystems; Environmental aspects; Environmental changes; Geovetenskap och miljövetenskap; Global temperature changes; Grassland; Microwave radiometers; Missing data; Natural Sciences; Naturvetenskap; Normalized difference vegetative index; People and Places; Phenology; Physical Sciences; Plants; Remote sensing; Salinity; Satellite Imagery - methods; Satellites; Savannahs; Scatterometers; Seasonal variations; Sensors; Soil - chemistry; Soil moisture; Vegetation; Vegetation dynamics; Vegetation index; Water - analysis
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0200328

Satellite derived normalized difference vegetation index (NDVI) is a common data source for monitoring regional and global ecosystem properties. In dry lands it has contributed to estimation of inter-annual and seasonal vegetation dynamics and phenology. However, due to the spectral properties of NDVI it can be affected by clouds which can introduce missing data in the time series. Remotely sensed soil moisture has in contrast to NDVI the benefit of being unaffected by clouds due to the measurements being made in the microwave domain. There is therefore a potential in combining the remotely sensed NDVI with remotely sensed soil moisture to enhance the quality and estimate the missing data. We present a step towards the usage of remotely sensed soil moisture for estimation of savannah NDVI. This was done by evaluating the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture and three of its individual products with respect to their relative performance. The individual products are from the advance scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), and the Land Parameter Retrieval Model-Advanced Microwave Scanning Radiometer-Earth Observing System (LPRM-AMSR-E). Each dataset was used to simulate NDVI, which was subsequently compared to remotely sensed NDVI from MODIS. Differences in their ability to estimate NDVI indicated that, on average, CCI soil moisture differs from its individual products by showing a higher average correlation with measured NDVI. Overall NDVI modelled from CCI soil moisture gave an average correlation of 0.81 to remotely sensed NDVI which indicates its potential to be used to estimate seasonal variations in savannah NDVI. Our result shows promise for further development in using CCI soil moisture to estimate NDVI. The modelled NDVI can potentially be used together with other remotely sensed vegetation datasets to enhance the phenological information that can be acquired, thereby, improving the estimates of savannah vegetation phenology.