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The goal of the present review is to collect data on trending scientific discussions on applying green infrastructure (GI) approaches to the effective use of urban floodplains and conceptualize potential future directions. A systematic literature review methodology was employed for this review. We reviewed 120 scholarly articles published between 2011 and 2022 under a predefined protocol. In this review, we discuss the trending dialogues on GI approaches and their applications. The research gap in applying GI approaches for macro-level urban-flood-plain management is addressed by (a) speculative arguments drawn from reviewed GI case studies, (b) an analysis of the trends’ strengths, weaknesses, opportunities, and threats (SWOT), and (c) presenting the concurrent ‘green–gray’ debate on neutral ground. Evidently, GI has its strengths and opportunities, as well as weaknesses and threats. The approaches to GI can be customized according to the application purpose, the regional or locational context, and the intended capacity. Following the analysis of emerging GI discussions, we position the current GI dialogues into four categories: (i) the green–gray continuum; (ii) GI for sustainable and resilient cities; (iii) GI as a resolution for urban issues; and (iv) the green–gray debate. In this classification, we strongly argue that placing GI in a more certain and instrumental position can be optimally achieved in the ‘green–gray continuum’ concept with a win–win scenario. Therefore, scientifically investigating the ‘green–gray continuum’ possibilities in a futuristic approach is strongly recommended. Full article

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R²_c = 0.91; R²_v = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R²_c = 0.93; R²_v = 0.76); compared with the FD-RF model, R²_c increased only slightly, but R²_v increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant growth monitoring based on ground and UAV multisource RS. Full article