Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products ha...Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products have been developed since the 1970s,they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD,especially in complex mountainous regions.In this paper,we developed afine-resolution SD retrieval model(FSDM)using machine learning to improve SD estimation quality for Northeast China and produced a long-term,fine-resolution,daily SD dataset.The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products.The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation,with the root-mean-square error(RMSE),bias,and correlation coefficient(R)of 7.10 cm,-0.13 cm,and 0.60.Additionally,we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range,Lesser Khingan Mountains,and Changbai Mountain regions.The SD exhibited high-low distribution patterns with the increased latitude.The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018.展开更多
作物行结构作为耕地表面的典型周期性结构特征,其方向会对测量雷达后向散射系数和光学反射率的结果造成显著影响。针对高分辨遥感影像纹理特征提取作物行向时效率低、计算资源需求大导致难以应用于大区域的问题,以黑龙江省友谊县为研究...作物行结构作为耕地表面的典型周期性结构特征,其方向会对测量雷达后向散射系数和光学反射率的结果造成显著影响。针对高分辨遥感影像纹理特征提取作物行向时效率低、计算资源需求大导致难以应用于大区域的问题,以黑龙江省友谊县为研究区,将地块作为最小研究对象,验证利用地块形态特征识别作物行向的可行性。本研究利用多种图像处理算法计算地块长边与短边的长度比值(长宽比),分析作物行向和地块长边方向之间的相关性,对比不同地块长宽比对作物行向识别率和识别精度的影响。结果表明:随着地块长宽比阈值增加,行向的识别率从82.0%降低到34.8%,行向识别均方根误差(Root Mean Square Error,RMSE)从21.46°降低至1.78°;在不同长宽比阈值条件下,直线检测器算法识别作物行向的平均精度(R^(2)=0.93,RMSE=9.53°)高于概率霍夫变换(R^(2)=0.81,RMSE=20.80°)。该方法可以实现对大范围农田地块作物行向的识别,为遥感卫星影像识别作物行方向提供新的思路。展开更多
基金supported by Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA28110502]National Natural Science Foundation of China[grant number 41871248]+1 种基金Changchun Science and Technology Development Plan Project[grant number 21ZY12]Innovation and Entrepreneurship Talent Project of Jilin Province[grant number 2023QN15].
文摘Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products have been developed since the 1970s,they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD,especially in complex mountainous regions.In this paper,we developed afine-resolution SD retrieval model(FSDM)using machine learning to improve SD estimation quality for Northeast China and produced a long-term,fine-resolution,daily SD dataset.The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products.The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation,with the root-mean-square error(RMSE),bias,and correlation coefficient(R)of 7.10 cm,-0.13 cm,and 0.60.Additionally,we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range,Lesser Khingan Mountains,and Changbai Mountain regions.The SD exhibited high-low distribution patterns with the increased latitude.The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018.
文摘作物行结构作为耕地表面的典型周期性结构特征,其方向会对测量雷达后向散射系数和光学反射率的结果造成显著影响。针对高分辨遥感影像纹理特征提取作物行向时效率低、计算资源需求大导致难以应用于大区域的问题,以黑龙江省友谊县为研究区,将地块作为最小研究对象,验证利用地块形态特征识别作物行向的可行性。本研究利用多种图像处理算法计算地块长边与短边的长度比值(长宽比),分析作物行向和地块长边方向之间的相关性,对比不同地块长宽比对作物行向识别率和识别精度的影响。结果表明:随着地块长宽比阈值增加,行向的识别率从82.0%降低到34.8%,行向识别均方根误差(Root Mean Square Error,RMSE)从21.46°降低至1.78°;在不同长宽比阈值条件下,直线检测器算法识别作物行向的平均精度(R^(2)=0.93,RMSE=9.53°)高于概率霍夫变换(R^(2)=0.81,RMSE=20.80°)。该方法可以实现对大范围农田地块作物行向的识别,为遥感卫星影像识别作物行方向提供新的思路。
