Actual evapotranspiration (ET A ) is a fundamental component of the land water cycle that can be predicted by the com- bination of meteorological data and remotely sensed normalized difference vegetation index (NDVI) observations. The proficient ap- plication of this approach to the retrospective study of fragmented areas, however, depends on the preliminary use of spatio-temporal fusion (STF) methods capable of integrating different satellite datasets. One of these methods is the Spatial Enhancer of Vegetation Index image Series (SEVIS), which has been recently developed to improve the annual NDVI datasets based on one or a few high spatial resolution images. This STF method is currently applied to moderate resolution imaging spectroradiometer (MODIS) and TM/ETM+/OLI imagery taken over three fragmented areas in Tuscany (Central Italy), representative of different Mediterranean ecosystems, i.e., an urban grassland, a tomato field, and an olive grove. The performance of SEVIS is evaluated by comparing the ET A estimates obtained from the original (MODIS) and synthetic (MODIS plus TM/ETM+/OLI) NDVI datasets to ground ET A observations. The experimental results indicate that the original MODIS NDVI data cannot properly characterize the seasonal veg- etation evolutions of the three study sites, which negatively affects the performance of ET A simulation. In contrast, such evolutions are reasonably reproduced by the synthetic NDVI datasets, which improves the accuracy of the ET A estimates both in terms of corre- lation and errors. The improvements are particularly evident dur- ing the summer dry period when the MODIS images are incapable of characterizing the actual vegetation response to water stress.

Estimation of Actual Evapotranspiration in Fragmented Mediterranean Areas by the Spatio-Temporal Fusion of NDVI Data

Pieri M;Cantini C;Giovannelli A;Maselli F;Chiesi M;Battista P;Fibbi L;Gardin L;Rapi B;Romani M;Sabatini F;
2019

Abstract

Actual evapotranspiration (ET A ) is a fundamental component of the land water cycle that can be predicted by the com- bination of meteorological data and remotely sensed normalized difference vegetation index (NDVI) observations. The proficient ap- plication of this approach to the retrospective study of fragmented areas, however, depends on the preliminary use of spatio-temporal fusion (STF) methods capable of integrating different satellite datasets. One of these methods is the Spatial Enhancer of Vegetation Index image Series (SEVIS), which has been recently developed to improve the annual NDVI datasets based on one or a few high spatial resolution images. This STF method is currently applied to moderate resolution imaging spectroradiometer (MODIS) and TM/ETM+/OLI imagery taken over three fragmented areas in Tuscany (Central Italy), representative of different Mediterranean ecosystems, i.e., an urban grassland, a tomato field, and an olive grove. The performance of SEVIS is evaluated by comparing the ET A estimates obtained from the original (MODIS) and synthetic (MODIS plus TM/ETM+/OLI) NDVI datasets to ground ET A observations. The experimental results indicate that the original MODIS NDVI data cannot properly characterize the seasonal veg- etation evolutions of the three study sites, which negatively affects the performance of ET A simulation. In contrast, such evolutions are reasonably reproduced by the synthetic NDVI datasets, which improves the accuracy of the ET A estimates both in terms of corre- lation and errors. The improvements are particularly evident dur- ing the summer dry period when the MODIS images are incapable of characterizing the actual vegetation response to water stress.
2019
Istituto di Ricerca sugli Ecosistemi Terrestri - IRET
Istituto per la BioEconomia - IBE
Moderate Resolution Imaging Spectroradiometer (MODIS)
Normalized Difference Vegetation Index (NDVI)
Thematic Mapper (TM)
Enhanced Thematic Mapper Plus (ETM+)
Operational Land Imager (OLI).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/368270
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