The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of the Chl-a concentrations. The back propagation (BP) neural network learning method has been widely used for the remote sensing retrieval of water quality in first and second-class water bodies. However, many Chl-a concentration measurements must be used as learning samples with this method, which is constrained by the number of samples, due to the limited time and resources available for simultaneous measurements. In this paper, we conduct correlation analysis between the Chl-a concentration data measured at Dianshan Lake in 2020 and 2021 and synchronized Landat-8 data. Through analysis and study of the radiative transfer model and the retrieval method, a BP neural network retrieval model based on multi-phase Chl-a concentration data is proposed, which allows for the realization of remote sensing-based Chl-a monitoring in third-class water bodies. An analysis of spatiotemporal distribution characteristics was performed, and the method was compared with other constructed models. The research results indicate that the retrieval performance of the proposed BP neural network model is better than that of models constructed using multiple regression analysis and curve estimation analysis approaches, with a coefficient of determination of 0.86 and an average relative error of 19.48%. The spatial and temporal Chl-a distribution over Dianshan Lake was uneven, with high concentrations close to human production and low concentrations in the open areas of the lake. During the period from 2020 to 2021, the Chl-a concentration showed a significant upward trend. These research findings provide reference for monitoring the water environment in Dianshan Lake.

The research on urban innovation links is of great significance for revealing the structural characteristics of innovation space. At the same time, it is of great significance for formulating regional innovation development strategies. Based on statistics from 2008, 2013 and 2018, the time and spatial characteristics of the urban innovation contact network evolution in Henan Province were investigated using the entropy weight and gravity-model method. The results show that the overall pattern of the regional innovation network in Henan Province is radial, with the characteristics of a “core edge”. It shows that it radiates to the surrounding cities with Zhengzhou as the core. From the perspective of time evolution, the radiation range and intensity of the innovation center city have changed to varying degrees and the overall radiation pattern has not significantly changed. From the perspective of spatial evolution, the evolution of Henan Province’s innovation network presents the following development trends. The central and southern regions developed rapidly, and the western, northern and eastern regions developed slowly.