Land Surface Temperature (LST) Retrieval from GOES Satellite Observations - Satellite-based Applications on Climate Change - page 316

a b

45
40
35
30
25
20
15
10
5
0
-5

45
40
35
30
25
20
15
10
5
0
-5
-10

Corr.=0.972

Corr.=0.985

Bias=-0.862

Bias=-0.630

STD=2.536

STD=1.839

RMS=3.049

RMS=2.282

1:1 Line

1:1 Line

Linear Fit Line

Linear Fit Line

-10

-10 -5 0 5 10 15 20 25

LST InSltu (Unit:°C)

30 35 40 45 -10 -5 0 5 10 15 20 25 30 35 40 45

LST InSltu (Unit:°C)

Fig. 19.18 Scatter plots of LST derived from the GOES vs. in situ observations for ( a ) split-
window algorithm and ( b ) dual-window algorithm

50

FPK

45

PSU

TBL

BON

40

ARM
SGP

DRA

35

GWN

30

25

-125 -120 -115 -110 -105 -100 -95 -90 -85 -80 -75 -70

Longitude

Fig. 19.19 Distribution of the SURFRAD and ARM sites

the dual-window algorithm, as compared to 1.83 K from the split-window
algorithm.
However, in general, all of accuracy comparisons show that using the dual-
window to derive LST is fairly feasible, though its total accuracy is slightly worse
than those from the split-window algorithm. These results show that the lack of
split-window channels on the GOES M-Q series may degrade the performance of
GOES-LST retrieval.