. Author manuscript; available in PMC: 2020 Apr 30.

Published in final edited form as: Remote Sens (Basel). 2020;12(1):50. doi: 10.3390/rs12010050

Table 6.

Performance of the TSEB model based on GP model estimate of LAI using model scenarios 1, 2, and 3 (S1, S2 and S3) for each energy flux component.

Variable	Scenario	MAE	RMSE	RRMSE
Rn	S1	46	53	10%
	S2	39	47	8%
	S3	39	42	8%
H	S1	87	93	49%
	S2	64	67	35%
	S3	35	40	21%
LE	S1	65	72	26%
	S2	65	69	25%
	S3	35	39	14%
G	S1	46	52	65%
	S2	38	49	61%
	S3	37	41	51%

Variable	Scenario	MAE	RMSE	RRMSE
Rn	S1	46	53	10%
	S2	39	47	8%
	S3	39	42	8%
H	S1	87	93	49%
	S2	64	67	35%
	S3	35	40	21%
LE	S1	65	72	26%
	S2	65	69	25%
	S3	35	39	14%
G	S1	46	52	65%
	S2	38	49	61%
	S3	37	41	51%

Variable	Scenario	MAE	RMSE	RRMSE
Rn	S1	46	53	10%
	S2	39	47	8%
	S3	39	42	8%
H	S1	87	93	49%
	S2	64	67	35%
	S3	35	40	21%
LE	S1	65	72	26%
	S2	65	69	25%
	S3	35	39	14%
G	S1	46	52	65%
	S2	38	49	61%
	S3	37	41	51%

Variable	Scenario	MAE	RMSE	RRMSE
Rn	S1	46	53	10%
	S2	39	47	8%
	S3	39	42	8%
H	S1	87	93	49%
	S2	64	67	35%
	S3	35	40	21%
LE	S1	65	72	26%
	S2	65	69	25%
	S3	35	39	14%
G	S1	46	52	65%
	S2	38	49	61%
	S3	37	41	51%