Drought has become one of the major constraints to agricultural development, particularly in areas that lack water. Studying the effects of different water stresses on the photosynthesis, growth, yield, water use efficiency (WUE) and irrigation water productivity (IWP) of winter wheat will provide data for the development of scientific irrigation strategies for water-saving agricultural methods. According to the size of the field water capacity, four different water stress levels were set, i.e., 30–40% (severe stress), 40–50% (moderate stress), 50–60% (mild stress) and 60–80% (well-watered) of field water capacity, controlling the amount of irrigation through an automatic irrigation system. The results showed that the seasonal changes in photosynthetic parameters, such as net photosynthetic rate (Pn), intercellular carbon concentration (Ci), stomatal conductance (Gs) and transpiration (E), significantly decreased under moderate and severe stress. As a result, the height, biomass and grain size of winter wheat decreased significantly, which led to low WUE and IWP. The Pn of the mild stress group only slightly decreased compared to that of the well-watered group, and was actually higher during the flowering and grain-filling stages, resulting in increases in dry biomass and 1000 grain weight of 2.07% and 1.95%, respectively. Higher WUE and IWP were attributed to higher yields and less water use. Thus, mild stress (60–80% field water capacity) resulted in the optimal use of water resources without a significant reduction in yield in the North China Plain (NCP). Therefore, mild stress can be considered a suitable environment for winter wheat growth in arid areas.

At present, convolutional neural networks (CNN) have been widely used in building extraction from remote sensing imagery (RSI), but there are still some bottlenecks. On the one hand, there are so many parameters in the previous network with complex structure, which will occupy lots of memories and consume much time during training process. On the other hand, low-level features extracted by shallow layers and abstract features extracted by deep layers of artificial neural network cannot be fully fused, which leads to an inaccurate building extraction from RSI. To alleviate these disadvantages, a dense residual neural network (DR-Net) was proposed in this paper. DR-Net uses a deeplabv3+Net encoder/decoder backbone, in combination with densely connected convolution neural network (DCNN) and residual network (ResNet) structure. Compared with deeplabv3+net (containing about 41 million parameters) and BRRNet (containing about 17 million parameters), DR-Net contains about 9 million parameters; So, the number of parameters reduced a lot. The experimental results for both the WHU Building Dataset and Massachusetts Building Dataset, DR-Net show better performance in building extraction than other two state-of-the-art methods. Experiments on WHU building data set showed that Intersection over Union (IoU) increased by 2.4% and F1 score increased by 1.4%; in terms of Massachusetts Building Dataset, IoU increased by 3.8% and F1 score increased by 2.9%.

Due to its long-term and high-frequency observation capabilities, remote sensing is widely recognized as an indispensable and preferred technology for large-scale and cross-regional water quality monitoring. This paper comprehensively reviews the recent progress of remote sensing for water environment monitoring, predominantly focusing on remote sensing data sources, inversion indices, and inversion models. Specifically, we summarize the inversion methods for commonly monitored water quality parameters, including optically active constituents (such as chlorophyll-a, colored dissolved organic matter, total suspended solids, and water clarity) and non-optically active constituents (including total nitrogen, total phosphorus, and chemical oxygen demand). Furthermore, the applications of remote sensing in the field of environmental sciences such as spatiotemporal evolution and driver factor analysis of water quality, carbon budget research, and pollution source identification are also systematically reviewed. Finally, we propose that atmospheric correction algorithm improvement, multi-source data fusion, and high-precision large-scale inversion algorithms should be further developed to reduce the current dependence on empirical observation algorithms in remote sensing and overcome the limitations imposed by temporal and spatial scales and that more inversion models for non-optically active parameters should be explored to realize accurate remote sensing monitoring of these components in the future. This review not only enhances our understanding of the critical role of remote sensing in inland water quality monitoring but also provides a scientific basis for water environment management.

Solar-induced chlorophyll fluorescence (SIF) is closely related to vegetation photosynthesis, and is considered as a direct and non-invasive indicator of the functional status of photosynthetic machinery; the normalized difference vegetation index (NDVI) can also reflect photosynthetic characteristics. Both are related to surface soil moisture (SSM), which is susceptible to drought, and phenology plays an important role in these vegetation-moisture relationships. Based on a variety of gridded SIF, NDVI, and SSM datasets obtained from satellite sensors, we presented the detailed relationships of SSM with SIF and NDVI in different phenological stages. Results showed that SIF and NDVI were significantly correlated with SSM in different phenological stages, especially during the maturity and senescence stages. For different vegetation types, SIF was more sensitive than NDVI to SSM anomalies in grasslands and forests during the maturity stage and rainfed croplands during the senescence stage. These relationships were regulated by precipitation and mean temperature, and decreased precipitation had the most significant impact when accompanied by increased temperature during the maturity stage or decreased temperature during the senescence stage. Our findings shed light on the role that phenology has in the relationships of SSM with SIF and NDVI on a large scale, which can further improve the understanding of vegetation-moisture relationships.