南京大学学报(自然科学版) ›› 2015, Vol. 51 ›› Issue (5): 1022–1029.

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巴音布鲁克典型区MODIS亚像元积雪覆盖率估算

李 云1,2,3,冯学智1,2,3?,肖鹏峰1,2,3,耶 楠1,2,3   

  • 出版日期:2015-09-09 发布日期:2015-09-09
  • 作者简介:(1. 江苏省地理信息技术重点实验室,南京大学,南京,210023; 2. 卫星测绘技术与应用国家测绘地理信息局重点实验室,南京大学,南京,210023; 3. 南京大学地理信息科学系,南京,210023)
  • 基金资助:
    国家高分辨率对地观测系统重大专项项目(95-Y40B02-9001-13/15-04).

Estimating per-pixel snow cover fraction from MODIS in typical area of Bayanbulak

1,2,3, Feng Xuezhi1,2,3*, Xiao Pengfeng1,2,3, Ye Nan1,2,3   

  • Online:2015-09-09 Published:2015-09-09
  • About author:(1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, 210023, China; 2. Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing, 210023, China; 3. Department of Geographic Information Science, Nanjing University, Nanjing, 210023, China)

摘要: snow cover fraction, SCF)的估算可以减少混合像元造成的误差,提高雪盖监测精度。本文以新疆巴音布鲁克为研究区,以环境与灾害监测预报小卫星B星(HJ-1B)数据作为地面真值,计算出中分辨率成像光谱仪(moderate-resolution imaging spectroradiometer, MODIS) 数据对应的雪盖率,建立所得雪盖率与归一化差分积雪指数(normalized difference snow index, NDSI)之间的逐像元估算模型,并对估算结果进行了验证。结果表明,与MODIS积雪产品数据相比,该模型估算的雪盖率精度明显改善,具有较强的精确性和实用性。但是相对于真实雪盖率,也存在总体高估的情况。模型误差的主要来源是积雪边缘区的破碎分布、阴坡和阳坡的太阳辐射差异以及林带积雪的复杂影响。逐像元估算模型能有效的提高巴音布鲁克地区雪盖率制图精度,为该地区水资源合理利用,牧区雪灾的抗灾减灾提供了依据。

Abstract: Snow cover information is important for climate change research, water resource utilization and snow disaster monitoring. Estimating sub-pixel snow cover fraction can reduce the error caused by the mixed information within a pixel, thus improve the accuracy of snow cover mapping. Compared with the MODIS (moderate-resolution imaging spectroradiometer) snow cover fraction (FRA) product, a per-pixel estimating model was developed and validated to achieve a more precise estimation in typical area of Bayanbulak, Xinjiang Province. Using HJ-1B 30-m observations as “ground truth”, the percentage of snow cover was calculated for 500-m cells. Then a regression relationship between 500-m normalized difference snow index (NDSI) observations and FRA was developed for each pixel. The results showed that compared with the MOD10A1 FRA product, the standard error and the mean absolute error of the propose model were reduced. But the per-pixel estimating model tended to overestimate FRA overall. The problem was resulted from solar radiation difference between sunny and shady slope, the fragmented snow in fringe area and the complications of snow in forested area. Overall, the per-pixel estimating model can estimate sub-pixel snow cover fraction more accurately, thus provides support for reasonable water resource utilization and snow disaster mitigation in pastoral areas

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