More well-maintained green spaces leading toward sustainable, smart green cities mean that alternative water resources (e.g., wastewater) are needed to fulfill the water demand of urban greenery. These alternative resources may introduce some environmental hazards, such as salt leaching through wastewater irrigation. Despite the necessity of salinity monitoring and management in urban green spaces, most attention has been on agricultural fields. This study was defined to investigate the capability and feasibility of monitoring and predicting soil salinity using proximal sensing and remote sensing approaches. The innovation of the study lies in the fact that it is one of the first research studies to investigate soil salinity in heterogeneous urban vegetation with two approaches: proximal sensing salinity mapping using Electromagnetic-induction Meter (EM38) surveys and remote sensing using the high-resolution multispectral image of WorldView3. The possible spectral band combinations that form spectral indices were calculated using remote sensing techniques. The results from the EM38 survey were validated by testing soil samples in the laboratory. These findings were compared to remote sensing-based soil salinity indicators to examine their competence on mapping and predicting spatial variation of soil salinity in urban greenery. Several regression models were fitted; the mixed effect modeling was selected as the most appropriate to analyze data, as it takes into account the systematic observation-specific unobserved heterogeneity. Our results showed that Soil Adjusted Vegetation Index (SAVI) was the only salinity index that could be considered for predicting soil salinity in urban greenery using high-resolution images, yet further investigation is recommended.

Declines in riparian ecosystem greenness and water use have been observed in the delta of the Lower Colorado River (LCR) since 2000. The purpose of our case study was to measure these metrics on the U.S. side of the border between Hoover and Morelos Dams to see if declining greenness was unique to the portion of the river in Mexico. In this case study, five riparian reaches of the LCR from Hoover to Morelos Dam since 2000 were studied to evaluate trends in riparian ecosystem health. We measure these riparian woodlands using remotely sensed measurements of the two-band Enhanced Vegetation Index (EVI2; a proxy for greenness); daily evapotranspiration (ET; mmd⁻¹) using EVI2 (ET(EVI2)); and an annualized ET based on EVI2, the Phenology Assessment Metric (PAM ET), an annualized ET using Landsat time-series. A key finding is that riparian health and its water use has been in decline since 2000 on the U.S. portion of the LCR, depicting a loss of green vegetation over the last two decades. EVI2 results show a decline of −13.83%, while average daily ET(EVI2) between the first and last decade had a decrease of over 1 mmd⁻¹ (−27.30%) and the respective average PAM ET losses were 170.91 mmyr⁻¹ (−17.95%). The difference between the first and last five-year periods, 2000–2005 and 2016–2020, showed the largest decrease in daily ET(EVI) of 1.24 mmd⁻¹ (−32.61%). These declines come from a loss in healthy, green, riparian plant-cover, not a change in plant water use efficiency nor efficient use of managed water resources. Our results suggest further deterioration of biodiversity, wildlife habitat and other key ecosystem services on the U.S. portion of the LCR.

Modeling of hydrological process has become increasingly complicated since we need to take into consideration an increasing number of descriptive variables. Soil, topography, land-use, rainfall and flow are some of the variables which are difficult to be spatially measured. In recent years black box solutions like artificial neural networks have been used in modeling complex process of hydrologic events. The potential applications of multilayer feedforward back propagation neural networks for developing rainfall-runoff relationships for some homogeneous catchments located in the north of Iran were studied and compared with those of a multiple regression model. A total of 24 sites yielding 356 pairs of observed data were studied. The most popular network in hydrology, i.e., multilayer feedforward back propagation was used. Results show that among the different backpropagation learning algorithms used in this research, the Levenberg-Marquardt resulted in the best performance. Keywords: artificial neural networks, estimation of flood, flood frequency, hydrological modeling.

Selection of the appropriate distribution function and estimation of its parameters are two fundamental steps in the accurate estimation of flood magnitude. This study relied on the concept of optimization by meta heuristic algorithms to improve the results obtained from the conventional methods of parameter estimation, such as maximum likelihood ...

Accurate estimation of evapotranspiration (ET) and water demand of urban green spaces (UGS) remain critical, especially in water-limited cities. Measuring ET helps decision‐makers, urban planners and urban water managers formulate strategies and plans for sustainable green cities worldwide. In this study, we used three satellites, WorldView2, Landsat (OLI, TM5 and ETM+), and MODIS to measure the greenness and ET of a 780‐ha public green space, the Adelaide Parklands in Australia. Different satellite‐based vegetation indices (VIs) including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2) were assessed. The VI-based ET from these three satellites were estimated. We then validated these remote sensing-based ET with a field-based method of Soil Water Balance (SWB) using Artificial Neural Network (ANN). Inter‐ and intra‐annual changes of VIs and their relevant ET were mapped and analyzed during 2010-2018. Our study, using multi-sensor remote sensing data fusion, systematic methods and machine learning techniques confirmed the suitability and feasibility of remote sensing-based ET as accurate long‐term monitoring mean for ET trends over large UGS. Our techniques rely on public and free-access satellite images, and therefore, can be adapted to other water-limited cities.