@article{
 title = {Soil Drought Anomalies in MODIS GPP of a Mediterranean Broadleaved Evergreen Forest},
 type = {article},
 year = {2015},
 keywords = {FR_PUE},
 pages = {1154-1180},
 id = {b853e0e6-5f23-3b1d-8a6e-52d5e96a05a9},
 created = {2016-03-08T11:01:32.000Z},
 file_attached = {false},
 profile_id = {5c1040db-25e3-36ea-a919-0994a44709e7},
 group_id = {c4af41cc-7e3c-3fd3-9982-bdb923596eee},
 last_modified = {2017-03-14T17:16:18.928Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Liu2015},
 private_publication = {false},
 abstract = {The Moderate Resolution Imaging Spectroradiometer (MODIS) yields global operational estimates of terrestrial gross primary production (GPP). In this study, we compared MOD17A2 GPP with tower eddy flux-based estimates of GPP from 2001 to 2010 over an evergreen broad-leaf Mediterranean forest in Southern France with a significant summer drought period. The MOD17A2 GPP shows seasonal variations that are inconsistent with the tower GPP, with close-to-accurate winter estimates and significant discrepancies for summer estimates which are the least accurate. The analysis indicated that the MOD17A2 GPP has high bias relative to tower GPP during severe summer drought which we hypothesized caused by soil water limitation. Our investigation showed that there was a significant correlation (R2 = 0.77, p < 0.0001) between the relative soil water content and the relative error of MOD17A2 GPP. Therefore, the relationship between the error and the measured relative soil water content could explain anomalies in MOD17A2 GPP. The results of this study indicate that careful consideration of the water conditions input to the MOD17A2 GPP algorithm on remote sensing is required in order to provide accurate predictions of GPP. Still, continued efforts are necessary to ascertain the most appropriate index, which characterizes soil water limitation in water-limited environments using remote sensing.},
 bibtype = {article},
 author = {Liu, Jia and Rambal, Serge and Mouillot, Florent},
 doi = {10.3390/rs70101154},
 journal = {Remote Sensing}
}

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The results of this study indicate that careful consideration of the water conditions input to the MOD17A2 GPP algorithm on remote sensing is required in order to provide accurate predictions of GPP. 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