Quantitative estimation of the influence factors on snow/ice albedo

Quantitative estimation of the influence of various factors,such as black carbon,snow grain,dust content,and water con tent on albedo is essential in obtaining an accurate albedo.In this paper,field measurement data,including snow grain size,density,liquid water content,and snow depth was obtained.Black carbon and dust samples were collected from the snow surface.A simultaneous observation using ASD(Analytical Spectral Devices)spectral data was employed in the Qiyi glacier located on Qilian Mountain.The measurements were compared with results obtained from the Snow,Ice,and Aerosol Radiation(SNICAR)model.Additionally,a HYbrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT)air mass backward trajectory model was used to track the source of black carbon.The simulation was found to correlate well with observed data.Liquid water content was the most influential factor of albedo among the several influencing fac tors,followed by black carbon content and snow grain size.Finally,snow density change had the least toward albedo.HYSPLIT atmospheric trajectories model can only approximately show the source of black carbon and not clearly indicate the source region of black carbon.

The Influence of Time Scale on the Quantitative Study of Soil and Water Conservation Effect of Grassland

Quantitative analysis of time scale effects is conducive to further understanding of vegetation water and soil conservation mechanism.Based on the observation data of the grass covered and bare soil( control) experimental plots located in Hetian Town,Changting County of Fujian Province from 2007 to 2010,the characteristics of 4 parameters( precipitation,vegetation,RE and SE) were analyzed at precipitation event,month,season,and annual scales,and then the linear regression models were established to describe the relationships between RE( SE)and its influencing factors of precipitation and vegetation. RE( SE) means the ratio of runoff depth( soil loss) of grass covered plot to that of the control plot. Results show that these 4 parameters presented different magnitude and variation on different time scales. RE and SE were relatively stable either within or among different time scales due to their ratios reducing the influence of other factors. The coupling of precipitation and vegetation led to better water conservation effect at lower RE( < 0. 3) at precipitation event scale as well as at season scale,while the water conservation effect was dominated by precipitation at slightly higher( 0. 3- 0. 4) and higher( > 0. 7) REs at precipitation event scale as well as at annual scale( R2> 0. 78). For the soil conservation effect,precipitation or / and vegetation was / were the dominated influence factor( s) at precipitation event and annual scales,and the grass LAI could basically describe the positive conservation effect( SE <1,R2> 0. 55),while the maximum 30 min intensity( I30) could describe the negative conservation effect more accurately( SE >1,R2> 0. 79). More uncertainties( R2≈0. 4) exist in the models of both RE and SE at two moderate time scales( month and season). Consequently,factors influencing water and soil conservation effect of grass present different variation and coupling characteristics on different time scales,indicating the importance of time scale at the study on water and soil conservation.

Comparison of Water and Soil Conservation Effect of Trees,Shrubs and Grasses in the Red Soil Area of Southern China

Assessing the effects of vegetation on water and soil conversation is the key basis for research and management of ecological restoration on water-eroded areas.In this study,the runoff depth,soil loss and corresponding precipitation of five plots planted respectively with Pueraria lobata,Lespedeza bicolor Turcz,Manglietia yuyuanensis Law,Paspalum natatu Fliigge,Paspalum wettsteinii Hackel and one control plot were observed monthly from 2003 to 2010 in Hetian Town of Changting County,Fujian Province,a typical water-eroded area in southern China.Then the effects of different vegetation on water/soil conversation(RE/SE)were determined using the ratios of runoff depth/soil loss between vegetated plots to the control plot.Meanwhile,the effect of precipitation on the water and soil loss was also analyzed.The results showed that,both the water and soil conservation effects of Pueraria lobata and Manglietia yuyuanensis Law are better than Lespedeza bicolor Turcz and Paspalum natatu,while Paspalum wettsteinii Hackel are the worst.The differences of effects of water conservation are more significantly than those of soil conversation between five kinds of vegetations.The runoff depth is mainly affected by precipitation,the determination coefficients(R2)of linear regression models between precipitation and runoff depth of all planted plots are all greater than 0.9,whereas the determination coefficients of the linear regression models between precipitation and soil loss vary form 0.3 to 0.8 for different vegetated plots.These results provide a reference for vegetation reconstruction in the current and similar areas.

磁控胶嚢胃镜与传统胃镜对儿童胃及十二指肠检查的对比分析

目的探讨磁控胶嚢胃镜(magnetically controlled capsule gastroscopy,MCCG)在儿童胃、十二指肠检查中的临床应用价值、安全性及与传统胃镜间的差异。方法回顾性分析2020年3月—2022年3月期间在上海市儿童医院门诊或住院的腹痛伴幽门螺杆菌感染的160例8~16岁患儿临床资料。患儿根据胃镜检查方法不同分为MCCG检查组及传统胃镜检查组各80例。观察分析两组患儿上消化道病变的检出情况、检查时间、耐受度及安全性。结果两组患儿分别顺利完成MCCG检查79例及传统胃镜检查78例,病变检出率两组比较,食管[1.3%(1/79)比1.3%(1/78),χ^(2)=0.000,P>0.999]、胃[87.3%(69/79)比91.0%(71/78),χ^(2)=0.552,P=0.327]、十二指肠[15.2%(12/79)比19.2%(15/78),χ^(2)=0.450,P=0.533],组间差异无统计学意义。MCCG组与传统胃镜组检查时间比较差异有统计学意义[72.0(41.0,109.5)min比6.0(4.3,7.0)min,U=24,P<0.001]。两组患儿均无不良事件发生。结论MCCG对于胃部及十二指肠疾病的检出率与传统胃镜无明显差异,且安全、稳定,可作为诊断儿童胃及十二指肠疾病的检查工具。

MDSNet:a multiscale decoupled supervision network for semantic segmentation of remote sensing images

Recent deep-learning successes have led to a new wave of semantic segmentation in remote sensing(RS)applications.However,most approaches rarely distinguish the role of the body and edge of RS ground objects;thus,our understanding of these semantic parts has been frustrated by the lack of detailed geometry and appearance.Here we present a multiscale decoupled supervision network for RS semantic segmentation.Our proposed framework extends a densely supervised encoder-decoder network with a feature decoupling module that can decouple semantic features with different scales into distinct body and edge components.We further conduct multiscale supervision of the original and decoupled body and edge features to enhance inner consistency and spatial boundaries in remote sensing image(RSl)ground objects,enabling new segmentation designs and semantic components that can learn to perform multiscale geometry,and appearance.Our results outperform the previous algorithm and are robust to different datasets.These results demonstrate that decoupled supervision is an effective solution to semantic segmentation tasks of RS images.

Sensitivity analysis for leaf area index （LAI） estimation from CHRIS/PROBA data

Sensitivity analyses were conducted for the retrieval of vegetation leaf area index （LAI） from multi- angular imageries in this study. Five spectral vegetation indices （VIs） were derived from Compact High Resolution Imaging Spectrometer onboard the Project for On Board Autonomy （CHRIS/PROBA） images, and were related to LAI, acquired from in situ measurement in Jiangxi Province, China, for five vegetation communities. The sensitivity of LAI retrieval to the variation of VIs from different observation angles was evaluated using the ratio of the slope of the best-fit linear VI-LAI model to its root mean squared error. Results show that both the sensitivity and reliability of VI-LAI models are influenced by the heterogeneity of vegetation communities, and that perfor- mance of vegetation indices in LAI estimation varies along observation angles. The VI-LAI models are more reliable for tall trees than for low growing shrub-grasses and also for forests with broad leaf trees than for coniferous forest. The greater the tree height and leaf size, the higher the sensitivity. Forests with broad-leaf trees have higher sensitivities, especially at oblique angles, while relatively simple-structured coniferous forests, shrubs, and grasses show similar sensitivities at all angles. The multi-angular soil and/or atmospheric parameter adjustments will hope- fully improve the performance of VIs in LAI estimation, which will require further investigation.