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Solar-induced fluorescence (SIF) is a promising proxy for photosynthesis, but it is unclear whether it performs well in tracking the gross primary productivity (GPP) under different environmental conditions. In this study, we investigated the dynamics of the two parameters from October 2020 to June 2021 in field-grown winter wheat (Triticum aestivum) and found that the ability of SIF to track GPP was weakened at low temperatures. Accounting for the coupling of light and temperature at a seasonal scale, we found that SIF yield showed a lower temperature sensitivity and had a lower but broader optimal temperature range compared with light-use efficiency (LUE), although both SIF yield and LUE decreased in low-temperature conditions. The discrepancy between the temperature responses of SIF yield and GPP caused an increase in the ratio of SIF/GPP in winter, which indicated the variation in the relationship between them during this period. The results of our study highlight the impact of low temperature on the relationship between SIF and GPP and show the necessity of reconsidering the dynamics of energy distribution inside plants under changing environments. [ABSTRACT FROM AUTHOR]

In this letter, we address the problem of road extraction in Wildland–urban interface (WUI) areas. In recent years, with the great success of convolutional neural networks (CNNs) in various vision-related tasks, researchers have developed many CNN-based methods for road extraction on remote sensing images. Nevertheless, these methods mostly treat road extraction as a binary classification problem on semantic labeling. In WUI areas, the road is narrower and tends to be occluded by trees, which may result in the serious discontinuous problem of inferred road maps. To address this issue, we propose transforming the input representation of the binary classification map into a continuous signed distance map. In this way, our model is forced to predict the continuous distance representations and, thus, improve the spatial continuities of inferred roads. In addition, a real-value regression task is designed to train along with the original binary classification task to generate spatially continuous and semantically accurate road maps. Then, we conduct experiments on the public Massachusetts road data set and a homemade data set collected from Yajishan Mountain, Beijing, China. Finally, our proposed method achieves intersection-over-unions (IoUs) of 64.11% and 65.92% for the Massachusetts and WUI-Yajishan data sets, respectively, without any postprocessing. In addition, the ablation analysis shows that introducing the regression task on the proposed signed distance representation can effectively alleviate the problem of discontinuous road prediction. Furthermore, comparing with the state-of-the-art methods demonstrates the superiority of our method for road extraction in WUI areas. [ABSTRACT FROM AUTHOR]

Building extraction from high-resolution remote sensing images plays a vital part in urban planning, safety supervision, geographic databases updates, and some other applications. Several researches are devoted to using convolutional neural network (CNN) to extract buildings from high-resolution satellite/aerial images. There are two major methods, one is the CNN-based semantic segmentation methods, which can not distinguish different objects of the same category and may lead to edge connection. The other one is CNN-based instance segmentation methods, which rely heavily on pre-defined anchors, and result in the highly sensitive, high computation/storage cost and imbalance between positive and negative samples. Therefore, in this paper, we propose an improved anchor-free instance segmentation method based on CenterMask with spatial and channel attention-guided mechanisms and improved effective backbone network for accurate extraction of buildings in high-resolution remote sensing images. Then we analyze the influence of different parameters and network structure on the performance of the model, and compare the performance for building extraction of Mask R-CNN, Mask Scoring R-CNN, CenterMask, and the improved CenterMask in this paper. Experimental results show that our improved CenterMask method can successfully well-balanced performance in terms of speed and accuracy, which achieves state-of-the-art performance at real-time speed. [ABSTRACT FROM AUTHOR]