查看更多>>摘要：Rivers provide key ecosystem services that are inherently engineered and optimized to meet the strategic and economic needs of countries around the world.However,limited water quality records of a full river continuum hindered the understanding of how river systems response to the multiple stressors acting on them.This study highlights the use of Sentinel-2 Multi-Spectral Imager(MSI)data to monitor changes in water color in two optically complex river systems:the Yangtze and Danube using the Forel-Ule Index(FUI).FUI divides water color into 21 classes from dark blue to yellowish brown stemming from the historical Forel-Ule water color scale and has been promoted as a useful indicator showing water turbidity variations in water bodies.The results revealed contrasting water color patterns in the two rivers on both spatial and seasonal scales.Spatially,the FUI of the Yangtze River gradually increased from the upper reaches to the lower reaches,while the FUI of the Danube River declined in the lower reaches,which is possibly due to the sediment sink effect of the Iron Gate Dams.The regional FUI peaks and valleys observed in the two river systems have also been shown to be related to the dams and hydropower stations along them.Seasonally,the variations of FUI in both systems can be attributed to climate seasonality,especially precipitation in the basin and the water level.Moreover,land cover within the river basin was possibly a significant determinant of water color,as higher levels of vegetation in the Danube basin were associated with lower FUI values,whereas higher FUI values and lower levels of vegetation were observed in the Yangtze system.This study furthers our knowledge of using Sentinel-2 MSI to monitor and understand the spatial-temporal variations of river systems and highlights the capabilities of the FUI in an optically complex environment.
Francesco RossiRaffaele CasaWenjiang HuangGiovanni Laneve...
870-891页
查看更多>>摘要：This research investigates the suitability of PRISMA and Sentinel-2 satellite imagery for retriev-ing topsoil properties such as Organic Matter(OM),Nitrogen(N),Phosphorus(P),Potassium(K),and pH in croplands using different Machine Learning(ML)algorithms and signal pre-treat-ments.Ninety-five soil samples were collected in Quzhou County,Northeast China.Satellite images captured soil reflectance data when bare soil was visible.For PRISMA data,a Linear Mixture Model(LMM)was used to separate soil and Photosynthetic Vegetation(PV)end-members,excluding Non-Photosynthetic Vegetation(NPV)using Band Depth values at the 2100 nm absorption peak of cellulose.Sentinel-2 bare soil reflectance spectra were obtained using thresholds based on NDVI and NBR2 indices.Results showed PRISMA data provided slightly better accuracy in retrieving topsoil nutrients than Sentinel-2.While no optimal pre-dictive algorithm was best,absorbance data proved more effective than reflectance.PRISMA results demonstrated potential for predicting soil nutrients in real scenarios.
查看更多>>摘要：Icebergs are big chunks of ice floating on the ocean surface,and melting of icebergs con-tributes for the major part of freshwater flux into ocean.Dynamic monitoring of the icebergs in Antarctica and accurate estimation of their volume are important for predicting the trend of freshwater budget of the Southern Ocean.The iceberg freeboard is a key parameter for measuring the thickness and volume of an iceberg and is defined as the difference between the elevation of iceberg surface and sea level.So far,freeboards of icebergs have been successfully extracted using InSAR DEM,and the laser and radar altimeter.However,uncertain-ties exist in these results mainly caused by missed detection of small icebergs due to the spatially sparse and temporally incomplete data coverage.In addition to the above techniques,optical images can also be used to extract the iceberg freeboard based on its geometric relationship with shadow length,which can effectively compensate for the above shortcom-ings.Although the feasibility has been preliminarily presented,the precision and extensive application of shadow-height method deserves further research,such as estimating the basal melting of icebergs.In this work,we tested an optical image-based freeboard extraction method over icebergs in Prydz Bay,Antarctica.A normalized shadow pixel index(NSPI)is designed to identify iceberg shadows with different shapes in HY-1C/D CZI and Sentinel-2 MSI optical images.The iceberg freeboard can be determined with an acceptable precision(2 m)in optical images compared with laser altimeter(i.e.ICESat-2)measurements.Moreover,basal melting of icebergs has been assessed according to the variation of freeboard using repeated optical observations.The results indicate that icebergs in the study area were with a mean freeboard of about 56 m in early December 2022,and experienced a decrease in freeboard of 1.9 m within two months,in correspondence with the Antarctic seasonal trend.The methodo-logical framework,therefore,turns out to be a reliable complementary approach to studying the iceberg freeboard in polar regions.
查看更多>>摘要：The Tibetan Plateau(TP)has been the focus of numerous studies examining the energy and water cycle variations,but there is still a lack of long-term,quantitative precise assessments of evapotranspiration.This research first provided two sets of long-term comprehensive observa-tional datasets,and an advanced monitoring technique to measure soil moisture,which can improve the estimation accuracy of evapotranspiration.Subsequently,using microwave data,the Surface Energy Balance System model and Machine Learning methods,it calculated a complete set of long-term evapotranspiration data.At the same time,based on reasonable assumptions,it also estimated the total evaporation from plateau lakes.These findings con-tribute significantly to the understanding of the relationship between the Asian monsoon,the TP's physical characteristics,and its atmosphere,thereby improving predictions of water resource variability in the TP.The study's innovative methodologies and synthesis of diverse data sources provide critical information for informed and sustainable water management strategies in the region.
查看更多>>摘要：Reservoirs play a critical role in terrestrial hydrological systems,but the contribution of small and medium-sized ones is rarely considered and recorded.Particularly in developing countries,there is a rapid increase of such reservoirs due to their quick construction.Accurately identify-ing these reservoirs is important for understanding social and economic development,but distinguishing them from other natural water bodies poses a significant challenge.Thus,we propose a method to identify reservoirs using high-resolution satellite images and deep learning algorithms.We trained models with various parameters and network structures,and You Only Look Once version 7(YOLOv7)outperformed other algorithms and was selected to build the final model.The method was applied to a region in northwestern Iran,characterized by an abundance of reservoirs of various sizes.Evaluation results indicated that our method was highly accurate(mAP:0.79,Recall:0.76,Precision:0.82).The YOLOv7 model was able to automatically identify 650 reservoirs in the entire study region,indicating that the proposed method can accurately detect reservoirs and has the potential for broader-scale surveys,even global applications.
查看更多>>摘要：Improving irrigation water management is a key concern for the agricultural sector,and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and requirements.This study presents a novel methodology to explicitly estimate daily gross and net crop water requirements,actual crop water use,and irrigation efficiency ofcenter pivot irrigation systems,by mainly utilizing the Sentinel-2 MultiSpectral Instrument(MSI)imagery at the farm scale.ETMonitor model is adapted to estimate actual water use(as the sum of canopy transpiration and evaporation of water intercepted by canopy and evaporation from soil)at daily/10-m resolution,benefiting from the high-resolution Sentinel-2 data and thus to assess the irrigation efficiency at the farm scale.The gross irrigation water requirement is estimated from the net crop water requirement and the water loss,including the water droplet evaporation directly into the air during application before droplets fall on the canopy and canopy interception loss.The method was applied to a pilot farmland with two major crops(wheat and potato)in the Inner Mongolia Autonomous Region of China,where modern equipment and appropriate irrigation methods are deployed for efficient water use.The estimated actual crop water use showed good agreement with the ground observations,e.g.the determination coefficients range from 0.67 to 0.81 and root mean square errors range from 0.56mm/day to 1.24mm/day for wheat and potato when comparing the estimated evapo-transpiration with the measurement by the eddy covariance system.It also showed that the losses of total irrigated volume were 25.4％for wheat and 23.7％for potato,respectively,and found that the water allocation was insufficient to meet the water requirement in this irrigated area.This suggests that the amount of water applied was insufficient to meet the crop water requirement and the inherent water losses in the center pivot irrigation system,which imply the necessity to improve the irrigation practice to use the water more efficiently.
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