Land surface temperature (RS-LST) derived from remotely sensed data is a good alternative because traditional LST data from meteorological stations have limitations in terms of locality, accessbility and cost. Yet MODIS standard LST products from NASA may suffer from noises from various sources including‘cloud contamination’, which greatly degrade the LST quality and hamper its efficient applications. The paper presents a novel algorithm which can reconstruct complete LST image based on regression analysis of LST with elevation in each sliding window of the original image, after filtering low-quality and unreliable pixels with Quality Asessment (QA) information and Histogram Outliers Removing method. Terra/Aqua MODIS-LST with overall four temperature channels in the northeast Qinghai-Tibet Plateau in 2008 are reconstructed using this method. Comparison of reconstructed 8-day composite LST data with corresponding landsurface temperature (T) observations from eleven meteorological stations in the study region indicates that LST is significantly correlated with T with an average correlation coefficient of 0.96 and a mean abosolute difference (MAE) of 2.02℃. When aggregated to monthly serials and yearly serials, LSTs and Ts show no significant differences in Paired-T-Test (with MAE of 1.55℃ and 0.60℃, respectively). The differences between LSTs and Ts have certain correlations with their different spatial and temporal definations; however some residual noises existing in the reconstructed LSTs indicate more meticulous algorithem needed to work out more accurate RS-LST data.
KE Linghong, WANG Zhengxing, SONG Chunqiao, LU Zhenquan
. Reconstruction of MODIS LST Time Series and Comparison with Land Surface Temperature (T) among Observation Stations in the Northeast Qinghai-Tibet Plateau[J]. PROGRESS IN GEOGRAPHY, 2011
, 30(7)
: 819
-826
.
DOI: 10.11820/dlkxjz.2011.07.006
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