Impact of Sensor Zenith Angle on MOD10A1 Data Reliability and Modification of Snow Cover Data for the Tarim River Basin

Snow in the mountainous watersheds of the Tarim River Basin is the primary source of water for western China. The Snow Cover Daily L3 Global 500-m Grid (MOD10A1) remote sensing dataset has proven extremely valuable for monitoring the changing snow cover patterns over large spatial areas; however, inherent uncertainty associated with large sensor zenith angles (SZAs) has called its reliability into question. Comparative analysis that utilized a paired-date difference method for parameters such as snow cover frequency, snow cover percentage, and normalized difference snow index (NDSI) has shown that overestimation of snow cover in the Tarim River Basin correlates with high values of SZA. Hence, such overestimation was associated with an increase in the NDSI, attributable to the change in reflectance between Band 4 and Band 6 imagery. A maximum threshold value of SZA of 22.37° was used alongside a multiday refilling method to modify the MOD10A1 dataset to produce a new daily snow cover map of the Tarim River Basin, spanning a 10-year period. A comparison of benchmark results of snow cover classification produced by the HJ-1A/B satellite revealed an increase in the overall accuracy of up to 4%, confirming the usefulness of our modified MOD10A1 data.

[1]  Günter Blöschl,et al.  Spatio‐temporal combination of MODIS images – potential for snow cover mapping , 2008 .

[2]  Anne W. Nolin,et al.  Towards retrieval of forest cover density over snow from the Multi‐angle Imaging SpectroRadiometer (MISR) , 2004 .

[3]  Alexei Lyapustin,et al.  Assessment of biases in MODIS surface reflectance due to Lambertian approximation , 2010 .

[4]  G. Henebry,et al.  Exploring the middle infrared region for urban remote sensing: seasonal and view angle effects , 2013 .

[5]  Amit Angal,et al.  Time-Dependent Response Versus Scan Angle for MODIS Reflective Solar Bands , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Thomas H. Painter,et al.  Time-space continuity of daily maps of fractional snow cover and albedo from MODIS , 2008 .

[7]  Yan Zhou,et al.  Mapping snow cover using HJ-1B infrared remote sensing(IRS) spectroradiometer images , 2015, 2015 8th International Congress on Image and Signal Processing (CISP).

[8]  Tiyip Tashpolat,et al.  Long‐term change of seasonal snow cover and its effects on river runoff in the Tarim River basin, northwestern China , 2009 .

[9]  T. Painter,et al.  Automated mapping of Earth's annual minimum exposed snow and ice with MODIS , 2012 .

[10]  D. Robinson,et al.  Global Snow Cover Monitoring: An Update , 1993 .

[11]  S. Sandmeier,et al.  Sensitivity analysis and quality assessment of laboratory BRDF data , 1998 .

[12]  M. Schaepman,et al.  Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data , 2008 .

[13]  Qing Li,et al.  Chinese HJ-1A/B satellites and data characteristics , 2010 .

[14]  Thomas H. Painter,et al.  Retrieval of subpixel snow covered area, grain size, and albedo from MODIS , 2009 .

[15]  Ranga B. Myneni,et al.  The impact of gridding artifacts on the local spatial properties of MODIS data : Implications for validation, compositing, and band-to-band registration across resolutions , 2006 .

[16]  Anil V. Kulkarni,et al.  Field-based spectral reflectance measurements of seasonal snow cover in the Indian Himalaya , 2010 .

[17]  E. Vermote,et al.  Seasonal and inter-annual variation in view angle effects on MODIS vegetation indices at three forest sites , 2011 .

[18]  R. Wolfe,et al.  Trends in MODIS geolocation error analysis , 2009, Optical Engineering + Applications.

[19]  E. Isaksson,et al.  Measuring snow and glacier ice properties from satellite , 2001 .

[20]  C. L. Wiegand,et al.  View azimuth and zenith, and solar angle effects on wheat canopy reflectance , 1985 .

[21]  Dorothy K. Hall,et al.  Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product , 2010 .

[22]  R. Fensholt,et al.  Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data , 2010 .

[23]  Guobin Fu,et al.  Trends of major hydroclimatic variables in the Tarim River basin during the past 50 years , 2010 .

[24]  Sartajvir Singh,et al.  Impact of topographic correction on high spectral resolution MODIS sensor satellite imagery of Himalayan region , 2015, 2015 International Conference on Signal Processing and Communication (ICSC).

[25]  Steven Platnick,et al.  Viewing Geometry Dependencies in MODIS Cloud Products , 2010 .

[26]  Liang Tian-gang.  Snow Cover Mapping Algorithm Based on HJ-1B Satellite Data , 2013 .

[27]  Liu Wei,et al.  Environmental effects of water resource development and use in the Tarim River basin of northwestern China , 2005 .

[28]  Xingong Li,et al.  Snowmelt runoff modelling in an arid mountain watershed, Tarim Basin, China , 2008 .

[29]  Dorothy K. Hall,et al.  Normalized-Difference Snow Index (NDSI) , 2010 .

[30]  D. Lettenmaier,et al.  Assimilating remotely sensed snow observations into a macroscale hydrology model , 2006 .

[31]  A. Gruen,et al.  Operational snow mapping using multitemporal Meteosat SEVIRI imagery , 2007 .

[32]  D. Roy,et al.  Achieving sub-pixel geolocation accuracy in support of MODIS land science , 2002 .

[33]  Marco Tedesco.  1 Algorithm Theoretical Basis Document Snow Algorithm , 2013 .

[34]  Bin Tan,et al.  View Angle Effects on MODIS Snow Mapping in Forests , 2012 .