README.md
In kongdd/PhenoAsync: Extract Remote Sensing Vegetation Phenology
PhenoAsync
Extract vegetation phenology by phenofit (v0.2.7) at 95 flux sites.
Vegetation Phenology Data at 95 flux sites
There are two data files:
pheno_flux95_prim ((GPP_DT) v0.2.6.9000).rda: vegetation carbon
phenology from GPP_DTpheno_flux95_prim ((GPP_NT) v0.2.6.9000).rda: vegetation carbon
phenology from GPP_NT
For each file, two variables are in there:
df_gpp_prim: vegetation carbon phenological metrics extracted from
flux sites GPP observations df_VI_prim : vegetation greenness phenological metrics extracted
from MODIS remote sensing vegetation indexes
load("INPUT/pheno_flux95_prim ((GPP_NT) v0.2.6.9000).rda")
# load("INPUT/pheno_flux95_prim ((GPP_DT) v0.2.6.9000).rda")
print(df_gpp_prim)
# site meth flag origin TRS1.sos TRS1.eos TRS2.sos TRS2.eos
# 1: DE-Seh AG 2008_1 2008-01-01 69 203 82 199
# 2: DE-Seh AG 2009_1 2009-01-01 76 203 86 191
# 3: DE-Seh AG 2010_1 2010-01-01 148 301 154 294
# 4: DE-Seh Beck 2008_1 2008-01-01 70 203 83 199
# 5: DE-Seh Beck 2009_1 2009-01-01 75 201 86 191
# ---
# 2538: CZ-BK2 Elmore 2012_1 2012-01-01 114 214 118 212
# 2539: CZ-BK2 Zhang 2009_1 2009-01-01 105 215 115 213
# 2540: CZ-BK2 Zhang 2010_1 2010-01-01 114 206 123 202
# 2541: CZ-BK2 Zhang 2011_1 2011-01-01 101 201 108 197
# 2542: CZ-BK2 Zhang 2012_1 2012-01-01 112 213 119 210
# TRS5.sos TRS5.eos TRS6.sos TRS6.eos TRS8.sos TRS8.eos TRS9.sos TRS9.eos
# 1: 105 191 111 189 125 185 136 181
# 2: 103 174 108 169 118 159 125 153
# 3: 163 278 166 273 171 261 174 250
# 4: 105 191 110 189 124 185 135 181
# 5: 101 177 105 173 115 163 121 156
# ---
# 2538: 122 206 124 202 126 190 130 176
# 2539: 131 205 135 201 147 191 157 187
# 2540: 138 192 142 188 151 180 158 174
# 2541: 120 187 123 183 131 173 139 166
# 2542: 130 202 133 198 141 188 148 180
# DER.sos DER.pop DER.eos UD SD DD RD Greenup Maturity Senescence
# 1: 105 169 191 72 136 180 204 58 157 174
# 2: 107 140 169 80 127 151 198 69 133 141
# 3: 166 182 281 151 176 257 302 144 181 246
# 4: 105 166 192 75 133 181 203 70 139 176
# 5: 102 139 175 80 121 157 197 75 127 150
# ---
# 2538: 122 146 NA 115 129 199 NA 109 135 154
# 2539: 131 184 NA 108 154 196 NA 103 159 188
# 2540: 139 163 197 118 157 178 NA 113 160 174
# 2541: 120 162 195 103 136 173 NA 99 142 165
# 2542: 130 172 NA 115 146 191 NA 110 150 181
# Dormancy GSL
# 1: 209 86
# 2: 208 71
# 3: 312 115
# 4: 207 86
# 5: 202 76
# ---
# 2538: NA 84
# 2539: NA 74
# 2540: NA 54
# 2541: NA 67
# 2542: NA 72
print(df_VI_prim)
# sate type_VI site group meth flag origin TRS1.sos TRS1.eos
# 1: combined EVI AT-Neu 1 AG 2003_1 2003-01-01 97 357
# 2: combined EVI AT-Neu 1 AG 2004_1 2004-01-01 108 366
# 3: combined EVI AT-Neu 1 AG 2005_1 2005-01-01 98 346
# 4: combined EVI AT-Neu 1 AG 2006_1 2006-01-01 117 347
# 5: combined EVI AT-Neu 1 AG 2007_1 2007-01-01 100 362
# ---
# 643422: Terra NDVI_pc ZM-Mon 9 Zhang 2014_1 2014-01-01 267 530
# 643423: Terra NDVI_pc ZM-Mon 9 Zhang 2015_1 2015-01-01 265 524
# 643424: Terra NDVI_pc ZM-Mon 9 Zhang 2016_1 2016-01-01 270 533
# 643425: Terra NDVI_pc ZM-Mon 9 Zhang 2017_1 2017-01-01 264 510
# 643426: Terra NDVI_pc ZM-Mon 9 Zhang 2018_1 2018-01-01 317 527
# TRS2.sos TRS2.eos TRS5.sos TRS5.eos TRS6.sos TRS6.eos TRS8.sos TRS8.eos
# 1: 107 331 125 285 130 273 143 247
# 2: 122 339 149 292 156 279 172 252
# 3: 108 326 126 290 130 279 140 253
# 4: 132 321 156 280 163 269 176 246
# 5: 104 336 115 288 118 274 124 242
# ---
# 643422: 279 511 299 479 305 470 318 447
# 643423: 280 506 305 476 312 467 332 446
# 643424: 287 513 316 481 325 471 345 449
# 643425: 277 493 301 465 309 457 327 439
# 643426: 326 508 341 476 346 466 356 443
# TRS9.sos TRS9.eos DER.sos DER.pop DER.eos UD SD DD RD Greenup
# 1: 154 231 123 199 272 97 151 225 346 86
# 2: 181 234 156 200 279 113 185 231 355 98
# 3: 146 234 130 158 292 103 148 243 344 93
# 4: 185 231 163 202 268 124 188 228 334 111
# 5: 129 218 116 138 287 NA 130 228 358 NA
# ---
# 643422: 331 427 299 393 479 272 326 439 524 267
# 643423: 340 426 304 342 476 268 342 440 519 261
# 643424: 363 429 316 377 480 274 357 439 527 268
# 643425: 344 421 299 390 466 260 338 430 505 253
# 643426: 364 422 341 400 476 320 362 436 524 316
# Maturity Senescence Dormancy GSL
# 1: 165 200 370 160
# 2: 199 216 384 143
# 3: 157 174 368 164
# 4: 201 218 356 124
# 5: 137 154 182 173
# ---
# 643422: 331 395 428 180
# 643423: 341 427 526 171
# 643424: 375 427 534 165
# 643425: 345 420 512 164
# 643426: 366 405 530 135
column variables:
sate : MODIS satellites, one of “combined” “Aqua”, and “Terra”type_VI: vegetation indexes, one of “EVI”, “NDVI”, “EVI_pc” and
“NDVI_pc”group : 3*3 grids id, 5 is the central gridmeth : curve fitting methods, one offlag : growing season idorigin : first date of this yearothers : vegetation phenological metrics extracted by phenofit
kongdd/PhenoAsync documentation built on April 21, 2024, 2:36 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
PhenoAsync
Extract vegetation phenology by phenofit (v0.2.7) at 95 flux sites.
Vegetation Phenology Data at 95 flux sites
There are two data files:
pheno_flux95_prim ((GPP_DT) v0.2.6.9000).rda: vegetation carbon phenology from GPP_DTpheno_flux95_prim ((GPP_NT) v0.2.6.9000).rda: vegetation carbon phenology from GPP_NT
For each file, two variables are in there:
df_gpp_prim: vegetation carbon phenological metrics extracted from flux sites GPP observationsdf_VI_prim: vegetation greenness phenological metrics extracted from MODIS remote sensing vegetation indexes
load("INPUT/pheno_flux95_prim ((GPP_NT) v0.2.6.9000).rda")
# load("INPUT/pheno_flux95_prim ((GPP_DT) v0.2.6.9000).rda")
print(df_gpp_prim)
# site meth flag origin TRS1.sos TRS1.eos TRS2.sos TRS2.eos
# 1: DE-Seh AG 2008_1 2008-01-01 69 203 82 199
# 2: DE-Seh AG 2009_1 2009-01-01 76 203 86 191
# 3: DE-Seh AG 2010_1 2010-01-01 148 301 154 294
# 4: DE-Seh Beck 2008_1 2008-01-01 70 203 83 199
# 5: DE-Seh Beck 2009_1 2009-01-01 75 201 86 191
# ---
# 2538: CZ-BK2 Elmore 2012_1 2012-01-01 114 214 118 212
# 2539: CZ-BK2 Zhang 2009_1 2009-01-01 105 215 115 213
# 2540: CZ-BK2 Zhang 2010_1 2010-01-01 114 206 123 202
# 2541: CZ-BK2 Zhang 2011_1 2011-01-01 101 201 108 197
# 2542: CZ-BK2 Zhang 2012_1 2012-01-01 112 213 119 210
# TRS5.sos TRS5.eos TRS6.sos TRS6.eos TRS8.sos TRS8.eos TRS9.sos TRS9.eos
# 1: 105 191 111 189 125 185 136 181
# 2: 103 174 108 169 118 159 125 153
# 3: 163 278 166 273 171 261 174 250
# 4: 105 191 110 189 124 185 135 181
# 5: 101 177 105 173 115 163 121 156
# ---
# 2538: 122 206 124 202 126 190 130 176
# 2539: 131 205 135 201 147 191 157 187
# 2540: 138 192 142 188 151 180 158 174
# 2541: 120 187 123 183 131 173 139 166
# 2542: 130 202 133 198 141 188 148 180
# DER.sos DER.pop DER.eos UD SD DD RD Greenup Maturity Senescence
# 1: 105 169 191 72 136 180 204 58 157 174
# 2: 107 140 169 80 127 151 198 69 133 141
# 3: 166 182 281 151 176 257 302 144 181 246
# 4: 105 166 192 75 133 181 203 70 139 176
# 5: 102 139 175 80 121 157 197 75 127 150
# ---
# 2538: 122 146 NA 115 129 199 NA 109 135 154
# 2539: 131 184 NA 108 154 196 NA 103 159 188
# 2540: 139 163 197 118 157 178 NA 113 160 174
# 2541: 120 162 195 103 136 173 NA 99 142 165
# 2542: 130 172 NA 115 146 191 NA 110 150 181
# Dormancy GSL
# 1: 209 86
# 2: 208 71
# 3: 312 115
# 4: 207 86
# 5: 202 76
# ---
# 2538: NA 84
# 2539: NA 74
# 2540: NA 54
# 2541: NA 67
# 2542: NA 72
print(df_VI_prim)
# sate type_VI site group meth flag origin TRS1.sos TRS1.eos
# 1: combined EVI AT-Neu 1 AG 2003_1 2003-01-01 97 357
# 2: combined EVI AT-Neu 1 AG 2004_1 2004-01-01 108 366
# 3: combined EVI AT-Neu 1 AG 2005_1 2005-01-01 98 346
# 4: combined EVI AT-Neu 1 AG 2006_1 2006-01-01 117 347
# 5: combined EVI AT-Neu 1 AG 2007_1 2007-01-01 100 362
# ---
# 643422: Terra NDVI_pc ZM-Mon 9 Zhang 2014_1 2014-01-01 267 530
# 643423: Terra NDVI_pc ZM-Mon 9 Zhang 2015_1 2015-01-01 265 524
# 643424: Terra NDVI_pc ZM-Mon 9 Zhang 2016_1 2016-01-01 270 533
# 643425: Terra NDVI_pc ZM-Mon 9 Zhang 2017_1 2017-01-01 264 510
# 643426: Terra NDVI_pc ZM-Mon 9 Zhang 2018_1 2018-01-01 317 527
# TRS2.sos TRS2.eos TRS5.sos TRS5.eos TRS6.sos TRS6.eos TRS8.sos TRS8.eos
# 1: 107 331 125 285 130 273 143 247
# 2: 122 339 149 292 156 279 172 252
# 3: 108 326 126 290 130 279 140 253
# 4: 132 321 156 280 163 269 176 246
# 5: 104 336 115 288 118 274 124 242
# ---
# 643422: 279 511 299 479 305 470 318 447
# 643423: 280 506 305 476 312 467 332 446
# 643424: 287 513 316 481 325 471 345 449
# 643425: 277 493 301 465 309 457 327 439
# 643426: 326 508 341 476 346 466 356 443
# TRS9.sos TRS9.eos DER.sos DER.pop DER.eos UD SD DD RD Greenup
# 1: 154 231 123 199 272 97 151 225 346 86
# 2: 181 234 156 200 279 113 185 231 355 98
# 3: 146 234 130 158 292 103 148 243 344 93
# 4: 185 231 163 202 268 124 188 228 334 111
# 5: 129 218 116 138 287 NA 130 228 358 NA
# ---
# 643422: 331 427 299 393 479 272 326 439 524 267
# 643423: 340 426 304 342 476 268 342 440 519 261
# 643424: 363 429 316 377 480 274 357 439 527 268
# 643425: 344 421 299 390 466 260 338 430 505 253
# 643426: 364 422 341 400 476 320 362 436 524 316
# Maturity Senescence Dormancy GSL
# 1: 165 200 370 160
# 2: 199 216 384 143
# 3: 157 174 368 164
# 4: 201 218 356 124
# 5: 137 154 182 173
# ---
# 643422: 331 395 428 180
# 643423: 341 427 526 171
# 643424: 375 427 534 165
# 643425: 345 420 512 164
# 643426: 366 405 530 135
column variables:
sate: MODIS satellites, one of “combined” “Aqua”, and “Terra”type_VI: vegetation indexes, one of “EVI”, “NDVI”, “EVI_pc” and “NDVI_pc”group: 3*3 grids id,5is the central gridmeth: curve fitting methods, one offlag: growing season idorigin: first date of this yearothers: vegetation phenological metrics extracted by phenofit
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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