积雪物候是指季节性积雪随季节周期的变化趋势和变化规律,对融雪径流、土壤冻融、植被物候和动物迁徙等过程有着重要影响,是积雪区能量平衡、水文、生态及气象模型的重要输入因子,也是积雪变化研究主要内容之一。中国是中低纬度主要的季节性积雪区,积雪物候研究具有重要的意义。本文基于1980–2020年5 km AVHRR逐日无云积雪面积产品,制备了中国长时间序列积雪物候数据集,该数据集包含积雪日数、积雪初日、积雪终日三个数据子集。利用地面气象台站实测雪深数据对产品进行验证,验证结果表明:积雪日数、积雪初日和积雪终日验证相关系数R2分别为0.80,0.76和0.94,均方根误差RMSE分别为22.78天,17.87天和16.39天,平均绝对误差MAE分别为13.26天,7.51天和7.76天,精度可靠。本数据集可服务于中国积雪时空变化分析,为气候变化,水文水资源,生态环境,人文经济等科学研究、工程建设以及社会服务提供基础数据资料。
Advanced Very High Resolution Radiometer(AVHRR)onboard National Oceanic and Atmospheric Administration(NOAA)satellites can provide over 40 years of global remote sensing observations,which can be used to retrieve long-term aerosol optical depth(AOD).This is of great significance to the study of global climate change.In this paper,we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations.With assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days,the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function(BRDF shape)and obtained AOD and surface property by optimal estimation(OE)method.The algorithm has been applied to NOAA-7,9,11,14,16,18,and 19 satellites and AVHRR-retrieved AOD with 5×10 km over China(15°–60°N,70°–140°E)has been obtained from 1982 to 2016.Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork(AERONET)(in and around China)and China Aerosol Remote Sensing Network(CARSNET)AOD show good consistency with 62.62%points within the uncertainty ofΔτ=±(0.05+0.25τ)and root-mean-square error(RMSE)of 0.26.Further comparison of the monthly mean AOD of multiple AOD datasets in the‘Beijing’,‘Dalanzadgad’,‘NCU_Taiwan’and‘Kanpur’stations shows that the results of the algorithm are stable.The yearly averaged AOD data also has similar agreements with MERRA-2(The Modern-Era Retrospective analysis for Research and Applications,Version 2)and AVHRRDB data(AVHRR‘Deep Blue’aerosol data set).The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01%and 67.29%compared with MERRA-2 AOD and AVHRRDB AOD respectively.
Chunlin JinYong XueXingxing JiangShuhui WuYuxin Sun
Image blending is one of the alternative methods to fill temporal gaps in the monitoring of historical vegetation properties using continuous NDVI derived from Landsat 5 TM/7 ETM+and 8 OLI images.Frequent cloud occurrence in the tropical upstream catchment limits the use of image blending methods that allow to employment of a single pair base reference.This study aims to evaluate two image blending methods with nine input data configurations to select the most applicable one.Scatter plots and statistical indices such as ME,RMSE,model efficiency and structure similarity showed FSDAF outperforms STARFM in generating both synthetic Landsat 8 OLI NDVI and Landsat 5 TM/7 ETM+NDVI when employing unsupervised and supervised classification images,respectively,where both were applied along with MODIS NDVI 250 m v.005.When generating synthetic Landsat 5 TM/7 ETM+NDVI using AVHRR NDVI,both algorithms performed similarly.However,when considering the temporal over spatial variance ratio between base reference and predictor images,both algorithms performed almost similar when the value close to minimum.This study shows that selection of image blending algorithm with use single pair base reference image should consider input data configuration and temporal over spatial variance ratio.
