Nouri, H; Nagler, P; Borujeni, SC; Munez, AB; Alaghmand, S; Noori, B; Galindo, A; Didan, K (2020). Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces. HYDROLOGICAL PROCESSES, 34(15), 3183-3199.

Urban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high-, medium- and coarse-spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI-based ET, over a 780-ha urban park in Adelaide, Australia. We validated ET with the ground-based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long-term changes of VIs and ET calculated from the different imagery acquired for this study (2011-2018). We found high correspondence between ET-MODIS and ET-Landsat (R-2 > 0.99 for all VIs). Landsat-VIs captured the seasonal changes of greenness better than MODIS-VIs. We used artificial neural network (ANN) to relate the RS-ET and ground data, and ET-MODIS (EVI2) showed the highest correlation (R-2 = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS-ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water-limited cities.