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In ‘Beijing 24’ peach [Prunus persica (L.) Batch] trees, a series of source leaves with differing levels of end products were created by retaining fruit (“+fruit”), removing fruit (“−fruit”), or reducing the light period. To alter the light period, leaves were covered with a bag made of brown inner paper and outer silver paper, which was then removed at different times the next day. The highest level of end products were obtained by fruit removal, while reducing the light period resulted in a lower level than “+fruit.” Net photosynthetic rate (Pn) and stomatal conductance (gs) decreased, but leaf temperatures (Tleaf) increased, following an increase in end product levels in leaves. After the “−fruit” treatment, reduced Pn was correlated with lower gs, and Tleaf increase was concomitant with decreases in maximal quantum yield of photosystem II (Fv/Fm), actual photochemical efficiency of photosystem II (ΦPSII), and photochemical quenching, and with an increase in nonphotochemical quenching. However, there were no significant differences in chlorophyll fluorescence between “+fruit” and the two treatments reducing the light period. The ΦPSII decreased following an increase in foliar sorbitol level, and it linearly decreased as sucrose and starch increased. Although fruit removal resulted in a significant accumulation of sucrose, sorbitol, and starch in leaves throughout the day, the extractable activities of several important enzymes involved in carbohydrate leaf storage and translocation did not decrease. Therefore, instead of feedback regulation by the accumulation of end products in source leaves, a high Tleaf induced by decreased stomatal aperture may play a key role in regulation of photosynthesis by limiting the photochemical efficiency of the PSII reaction centers under high levels of the end products in peach leaves.

Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, “marks” left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings.

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