Project CAMS41
Optimization of the relationship between CHTESSEL GPP and SIF data [per REGION and per SEASON]

All input data (SIF, GPP Jung and GPP CTESSEL) are smoothed with the 3-month running averaging window

PFT1 (Crops, mixed farming) PFT2 (Short grass) PFT3 (Evergreen needleleaf) PFT4 (Deciduous needleleaf) PFT5 (Deciduous broadleaf) PFT6 (Evergreen broadleaf) PFT7 (Tall grass) PFT9 (Tundra) PFT10 (Irrigated crops) PFT11 (Semidesert) PFT13 (Bogs and marshes) PFT16 (Evergreen shrubs) PFT17 (Deciduous shrubs) PFT18 (Mixed forest - Wood) PFT19 (Interrupted forest) Show Plots

Optimize \( (a,b) \) per CTESSEL PFT from the relationship \( SIF_\mathsf{SAT} = a \cdot GPP_\mathsf{Jung} + b \) Calculate inverse coefficients \( (\alpha,\beta) \) \( \alpha = \frac{1}{a} \) \( \beta = -\frac{b}{a} \) Compute CTESSEL SIF \( SIF_\mathsf{CTESSEL} = a \cdot GPP_\mathsf{CTESSEL} + b\)

Compare SIF for CTESSEL vs SAT \( \Delta SIF = SIF_\mathsf{SAT} - SIF_\mathsf{CTESSEL} \) Convert difference from SIF to GPP \( \Delta GPP_\mathsf{SAT} = \alpha \cdot \Delta SIF \) Compare with GPP difference \( \Delta GPP = GPP_\mathsf{Jung} - GPP_\mathsf{CTESSEL} \)

PFT maps

SIF data

Posterior data for GOSAT
a_post REGION YEAR WINTER SPRING SUMMER AUTUMN 01 0.114 0.132 0.116 0.107 0.127 01_N 0.108 0.158 0.108 0.106 0.132 01_T 0.108 0.112 0.11 0.118 0.114 01_S 0.122 0.133 0.135 0.14 0.116	b_post REGION YEAR WINTER SPRING SUMMER AUTUMN 01 0.0377 0.0308 0.029 0.0392 0.0457 01_N 0.0326 0.0218 0.0211 0.0321 0.0415 01_T 0.0698 0.0831 0.0548 0.0413 0.0712 01_S 0.0483 0.00859 0.0573 0.0375 0.058	n_points REGION YEAR WINTER SPRING SUMMER AUTUMN 01 36322 8750 9232 9552 8788 01_N 25082 5936 6293 6770 6083 01_T 6377 1657 1689 1492 1539 01_S 4863 1157 1250 1290 1166	RMSD (GOSAT vs Jung) REGION MODE YEAR WINTER SPRING SUMMER AUTUMN 01 all data 0.162 0.171 0.16 0.161 0.156 seasonal 0.162 0.171 0.16 0.161 0.155 01_N all data 0.155 0.168 0.154 0.151 0.144 seasonal 0.154 0.168 0.154 0.151 0.143 01_T all data 0.182 0.171 0.173 0.208 0.178 seasonal 0.182 0.17 0.172 0.208 0.177 01_S all data 0.167 0.181 0.165 0.143 0.181 seasonal 0.167 0.179 0.163 0.143 0.181

Posterior data for GOME2-Joiner
a_post REGION YEAR WINTER SPRING SUMMER AUTUMN 01 0.156 0.149 0.153 0.147 0.154 01_N 0.185 0.247 0.188 0.159 0.205 01_T 0.115 0.0947 0.103 0.125 0.121 01_S 0.146 0.15 0.164 0.149 0.131	b_post REGION YEAR WINTER SPRING SUMMER AUTUMN 01 0.07 0.0529 0.0586 0.102 0.0833 01_N 0.0526 0.0292 0.0357 0.102 0.0606 01_T 0.109 0.119 0.1 0.0949 0.129 01_S 0.084 0.0643 0.0599 0.0831 0.124	n_points REGION YEAR WINTER SPRING SUMMER AUTUMN 01 257615 56294 67002 67163 67156 01_N 161581 32294 42990 43152 43145 01_T 54083 13512 13524 13524 13523 01_S 41951 10488 10488 10487 10488	RMSD (GOME2-Joiner vs Jung) REGION MODE YEAR WINTER SPRING SUMMER AUTUMN 01 all data 0.102 0.116 0.0943 0.104 0.0941 seasonal 0.1 0.114 0.0933 0.101 0.0933 01_N all data 0.0928 0.114 0.0861 0.095 0.078 seasonal 0.0908 0.112 0.0848 0.0921 0.076 01_T all data 0.109 0.108 0.103 0.116 0.111 seasonal 0.107 0.106 0.0995 0.116 0.107 01_S all data 0.104 0.109 0.0917 0.092 0.12 seasonal 0.102 0.108 0.0904 0.092 0.117

GPP

SIF (all data optimization)

SIF (seasonal optimization)

Delta GPP