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Published： 07 May 2023 ，
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黄森文，何连，何溪怡，惠凤鸣.2023.北极区域SMAP与SMOS亮度温度数据交叉对比与定标.遥感学报，27（5）： 1216-1227
Huang S W，He L，He X Y and Hui F M. 2023. Inter-comparison and calibration of SMAP and SMOS brightness temperature data in the Arctic. National Remote Sensing Bulletin， 27（5）：1216-1227
SMAP和SMOS卫星均能提供L波段被动微波亮度温度观测数据，能够获取海冰厚度、海冰密集度和冰面积雪厚度等参数，在北极海冰监测中具有重要作用。本文以北极海洋区域为研究区域，选取2015年—2020年SMAP L1B和SMOS L1C大气层顶表观亮度温度数据，研究SMAP固定入射角为40°的亮度温度和SMOS入射角为37.5°—42.5°亮度温度均值之间的一致性，并分析了极化、海冰类型、季节对一致性的影响，以SMAP亮度温度为基准，采用线性公式逐月对SMOS数据进行定标并得到各月份交叉定标的斜率和截距，并对定标精度进行评估。研究结果表明：（1）SMAP亮度温度整体上低于SMOS亮度温度，水平和垂直极化分别低2.0—3.0 K和3.0— 4.5 K，均方根误差分别为4.5—6.0 K和5.0—6.0 K；在冬季，多年冰区域的偏差和均方根误差最小，一年冰次之，开阔水域最大；在夏季，海冰的偏差和均方根误差与开阔水域相近。（2）交叉定标的斜率和截距具有明显的季节特征，且不同月份定标系数的年际变化较小，可以根据月份获取稳定的定标系数。（3）定标后SMOS的亮度温度与SMAP的亮度温度具有较好的一致性，水平和垂直极化均方根误差分别为4.4 K和3.8 K，较定标前显著下降；不同海冰类型的定标精度差异较大，多年冰定标精度最高，水平和垂直极化均方根误差分别为2.4 K和1.9 K，一年冰定标精度相对较低，水平和垂直极化均方根误差分别为4.5 K和3.9 K，并且海冰生长末期（1—4月）定标精度高于海冰生长初期（10—12月）。本文的结果可以用于SMAP和SMOS数据的交叉定标，获取长时间序列、辐射性能一致的亮度温度数据，用于北极海冰参数及其变化趋势的监测。
Soil Moisture Active Passive (SMAP) Satellite and Soil Moisture and Ocean Salinity (SMOS) Satellite are passive microwave radiometers that operate at the L band. They can be used to estimate sea ice parameters
such as sea ice thickness
sea ice concentration
and snow depth on sea ice
so they play an important role in monitoring sea ice parameters and their changes in polar regions. A comprehensive comparison of SMAP and SMOS brightness temperatures (TBs) is necessary because it can help identify possible deficiencies in TB products and construct a highly consistent and reliable TB dataset for sea ice monitoring.
In this study
the sea region north of 55°N in the Arctic was selected as the study area. Top Of Atmosphere (TOA) TB observations derived from SMAP L1B and SMOS L1C products were compared for the period of October 2015 to October 2020. Given that SMAP measures at a fixed incidence angle of 40°
SMOS observations at incidence angles between 37.5° and 42.5° were averaged and compared with SMAP TBs. The discrepancy between SMAP and SMOS TBs was evaluated by computing the Pearson correlation coefficient (
r
)
bias (SMOS minus SMAP)
and root mean squared deviation (RMSD). The dependence of the discrepancy parameters on polarization
sea ice type
and season was also investigated. Given the higher radiometric accuracy of SMAP compared with that of SMOS
the SMAP observations were used as the reference data
and the SMOS TBs were calibrated using a linear regression method
with the slope and intercept values provided for each month. The RMSD values between the calibrated SMOS and SMAP were evaluated at both polarizations for different sea ice types.
Results indicated that the brightness temperatures of SMAP were generally lower than those of SMOS
with bias being 2.0—3.0 K and 3.0—4.5 K for H and V polarizations
respectively
and RMSD being 4.5—6.0 K for H and 5.0—6.0 K for V. During wintertime (October to April)
multi-year ice (MYI) had the lowest bias and RMSD values
followed by first-year ice (FYI). Open water (OW) had the highest bias and RMSD. During summertime (May to September)
the bias and RMSD values for sea ice were similar to those for OW. The slope and intercept values for calibrating the SMOS TBs showed strong seasonal variation. However
their inter-annual variabilities for each month were small
so averaged calibration coefficients could be achieved for each month. The obtained slope and intercept values were used to calibrate the SMOS TBs
and the results showed that the calibrated SMOS agreed well with SMAP
with the overall RMSD being 4.4 K and 3.8 K for H and V polarization
respectively. However
the different sea ice types had different RMSD values. Compared with MYI that had the lowest RMSD values
i.e.
2.4 K for H and 1.9 K for V
FYI had much higher RMSD values of 4.5 K and 3.9 K for H and V
respectively. Moreover
the calibration accuracy at the end of the sea ice growth stage (January to April) was higher than that at the beginning of the sea ice growth stage (October to December).
This study helps understand the discrepancy between SMAP and SMOS TB observations. The obtained calibration coefficients can be used to calibrate SMOS and contribute to the construction of a long-time-series
L-band
consistent-brightness temperature dataset for Arctic sea ice monitoring.
遥感SMAPSMOS亮度温度一致性交叉定标
remote seningSMAPSMOSbrightness temperatureconsistencyinter-calibration
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