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纸质出版日期： 2021-08-07 ，
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刘向阳，唐伯惠，李召良.2021.耦合双时相与邻近像元信息的极轨卫星地表组分温度反演.遥感学报，25（8）： 1700-1709
Liu X Y，Tang B H and Li Z L. 2021. Separating land surface component temperatures from Low Earth Orbit （LEO） satellite data by coupling of dual-time and multi-pixel data. National Remote Sensing Bulletin， 25（8）：1700-1709
与混合像元的地表温度相比，植被和土壤的组分温度具有更明确的物理意义。因此，本文提出了一种从具有广泛应用的极轨卫星地表温度产品中分离出植被和土壤组分温度的算法。该算法使用温度日变化模型作为桥梁连接极轨卫星一日内的两次观测，利用多像元数据进行模型求解，从而得到过境时刻的地表植被和土壤组分温度。论文针对MODIS数据开展了地表组分温度的反演，并利用实测站点数据和高分辨率卫星数据对反演结果进行了验证。结果表明，该算法可以提供合理的植被和土壤组分温度信息，反演温度的误差变化范围为1.4 K到2.5 K。此外，对观测时刻组合方式的分析表明该算法只需要一次白天观测和一次夜晚观测就可以得到精度较好的分离结果，并且两次观测可以来自于不同传感器，进一步表明了算法具有良好的可操作性。
The component temperature encapsulates more physical meaning than Land Surface Temperature (LST) and better meets the requirements of estimating evapotranspiration
monitoring drought and other studies. The polar-orbit satellites can observe the entire globe with a high spatial resolution and a modest temporal resolution from 1980 to present
and therefore have more wide applications than geostationary satellites. For these reasons
the study focuses on the methodology for estimating vegetation and soil component temperatures from polar-orbit satellite data.
To meet operational and accurate requirements
the study proposed to use multi-temporal and multi-pixel data to separate the vegetation and soil component temperature. Specifically
a well-studied Diurnal Temperature Cycles (DTC) model was applied to link the two observations on one day
and then the moving-window technology was used to add available observations for solving the retrieval model. In addition
a spatial weighting matrix was adopted to improve the limitation of using multi-pixel data.
The proposed algorithm was implemented by using Moderate Resolution Imaging Spectroradiometer (MODIS) data
and was evaluated by using in-situ measurements on Skukuza site and high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data
respectively. In the case of the validation of field data
the separation accuracy of component temperatures is about 2 K
and RMSEs of daytime vegetation
nighttime vegetation
daytime soil
and nighttime soil are 2.3 K
2.5 K
1.5 K and 1.9 K
respectively. The better performance at daytime is resulted from the fact that DTC model cannot describe the temperature decrease at night well. Regarding with the validation of ASTER data
the separation accuracies of the vegetation and soil component are 1.4 K and 1.7 K
respectively. The vegetation component is slightly overestimated (bias = 0.3 K) while the soil component is slightly underestimated (bias = -0.7 K)
which is because of the systematic error between MODIS LST and ASTER LST. Moreover
this study also analyzed the influence of different time groups. Firstly
the combinations of one daytime moment and one nighttime moment can provide same estimation with high accuracy while the performance of the combination of two daytime moments is worse. The result is expected because two daytime moments are close to the maximum temperature moment
and therefore more sensitivity to temperature variation. Secondly
the performance of the time group from two sensors or one sensor is basically same
indicating that the time group is not limited by the sensor.
This study proposed an algorithm for separating vegetation and soil component temperatures from polar-orbit satellite land surface temperatures. The practical method need only two observations from single or different sensors
i.e.
one in daytime and the other one in nighttime
which makes it available for almost all sensors. The validation of field data and high-resolution data indicated that the separation accuracy is about 2 K and the best up to 1.4 K. Considering its accuracy
operationality and robustness
the proposed method would be an effective tool for separating component temperatures.
遥感组分温度极轨卫星地表温度多时相多像元
remote sensingcomponent temperaturepolar-orbit satelliteland surface temperaturemulti-temporalmulti-pixel
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