Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces

• Published in: Hydrological processes Vol. 34; no. 15; pp. 3183 - 3199
• Main Authors: Nouri, Hamideh, Nagler, Pamela, Chavoshi Borujeni, Sattar, Barreto Munez, Armando, Alaghmand, Sina, Noori, Behnaz, Galindo, Alejandro, Didan, Kamel
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.07.2020; Wiley Subscription Services, Inc
• Subjects: Artificial neural networks; Correlation; Drought; Estimation; Evapotranspiration; Evapotranspiration estimates; Evapotranspiration measurements; EVI2; Green infrastructure; Heterogeneity; Image acquisition; Imagery; In situ measurement; Irrigation practices; Landsat; Landsat satellites; Microclimate; MODIS; Moisture content; Neural networks; Normalized difference vegetative index; Open spaces; Remote sensing; Resolution; Resource management; Satellite imagery; Satellites; Seasonal variation; Seasonal variations; Soil; Soil water; Spaceborne remote sensing; Spatial discrimination; Spatial resolution; Urban planning; Vegetation; Vegetation index; Water balance; water consumption; Water resources; Water scarcity; WorldView
• DOI: 10.1002/hyp.13790
• ISSN: 0885-6087

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 (R2 > 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 (R2 = 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. This is the first research to investigate the capability of free‐access images in estimating the ET of oversized urban green spaces. We compared different Vegetation Indices (VIs) from satellites with different spatial resolutions (WorldView2, Landsat and MODIS) and estimated the ET. We assessed the long‐term intra and inter‐annual variations of VIs and ETs and then compared them against the in situ ET method, and explored how local climate impacts the greenness and water demand of a large urban green space (780 ha) on a seasonal scale.