TY - JOUR
T1 - Improved snow depth retrieval by integrating microwave brightness temperature and visible/infrared reflectance
AU - Liang, Jiayong
AU - Liu, Xiaoping
AU - Huang, Kangning
AU - Li, Xia
AU - Shi, Xun
AU - Chen, Yaning
AU - Li, Jun
N1 - Publisher Copyright:
© 2014 Elsevier Inc.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - The accuracy of snow depth retrieval by remote sensing depends heavily on the characteristics of the snow, and both passive microwave and visible/infrared sensors can contribute to the acquisition of this information. A method integrating these two remotely sensed data sets is presented in this study. Snow depth retrieval is performed using microwave brightness temperature at 19 and 37GHz from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Image/Sounder (SSMI/S), and visible/infrared surface reflectance from Moderate Resolution Imaging Spectroadiometer (MODIS) products. Microwave brightness temperature provides information about the volume of snow pack, and visible/infrared surface reflectance can indicate snow presence and surface grain size. With these two remote sensing data sets, snow depth is retrieved by a nonlinear data mining technique, the modified sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression. The proposed method is tested by using 16,329 records of dry snow measured at 54 meteorological stations in Xinjiang, China over an area of 1.6million km2 from 2000 to 2009. The root mean square error (RMSE), relative RMSE and the correlation coefficient of our method are 6.21cm, 0.64 and 0.87, respectively. These results are better than those obtained using only brightness temperature data (8.80cm, 0.90 and 0.73), the traditional spectral polarization difference (SPD) algorithm (15.07cm, 1.54 and 0.58), a modified Chang algorithm in WESTDC (9.80cm, 1.00 and 0.62), or the multilayer perceptron classifier of artificial neural networks (ANN) (9.23cm, 0.94 and 0.72). The daily snow water equivalent (SWE) retrieved by this method has an RMSE of 8.05mm and a correlation of 0.84, which are better than those of NASA NSIDC (32.87mm and 0.47) or Globsnow (19.07mm and 0.59). This study demonstrates that the combination of visible/infrared surface reflectance and microwave brightness temperature via an SVM regression can provide a more accurate retrieval of snow depth.
AB - The accuracy of snow depth retrieval by remote sensing depends heavily on the characteristics of the snow, and both passive microwave and visible/infrared sensors can contribute to the acquisition of this information. A method integrating these two remotely sensed data sets is presented in this study. Snow depth retrieval is performed using microwave brightness temperature at 19 and 37GHz from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Image/Sounder (SSMI/S), and visible/infrared surface reflectance from Moderate Resolution Imaging Spectroadiometer (MODIS) products. Microwave brightness temperature provides information about the volume of snow pack, and visible/infrared surface reflectance can indicate snow presence and surface grain size. With these two remote sensing data sets, snow depth is retrieved by a nonlinear data mining technique, the modified sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression. The proposed method is tested by using 16,329 records of dry snow measured at 54 meteorological stations in Xinjiang, China over an area of 1.6million km2 from 2000 to 2009. The root mean square error (RMSE), relative RMSE and the correlation coefficient of our method are 6.21cm, 0.64 and 0.87, respectively. These results are better than those obtained using only brightness temperature data (8.80cm, 0.90 and 0.73), the traditional spectral polarization difference (SPD) algorithm (15.07cm, 1.54 and 0.58), a modified Chang algorithm in WESTDC (9.80cm, 1.00 and 0.62), or the multilayer perceptron classifier of artificial neural networks (ANN) (9.23cm, 0.94 and 0.72). The daily snow water equivalent (SWE) retrieved by this method has an RMSE of 8.05mm and a correlation of 0.84, which are better than those of NASA NSIDC (32.87mm and 0.47) or Globsnow (19.07mm and 0.59). This study demonstrates that the combination of visible/infrared surface reflectance and microwave brightness temperature via an SVM regression can provide a more accurate retrieval of snow depth.
KW - Integration of multi-sensor data
KW - Snow depth retrieval
KW - Support vector machine
UR - https://www.scopus.com/pages/publications/84922234110
U2 - 10.1016/j.rse.2014.10.016
DO - 10.1016/j.rse.2014.10.016
M3 - 文章
AN - SCOPUS:84922234110
SN - 0034-4257
VL - 156
SP - 500
EP - 509
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
ER -