{"doi":"10.3390/rs14102405","title":"Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface Model","abstract":"<jats:p>Soil moisture impacts the biosphere–atmosphere exchange of CO2 and CH4 and plays an important role in the terrestrial carbon cycle. A better representation of soil moisture would improve coupled carbon–water dynamics in terrestrial ecosystem models and could potentially improve model estimates of large-scale carbon fluxes and climate feedbacks. Here, we investigate using soil moisture observations from the Soil Moisture Active Passive (SMAP) satellite mission to inform simulated carbon fluxes in the global terrestrial ecosystem model LPJ-wsl. Results suggest that the direct insertion of SMAP reduces the bias in simulated soil moisture at in situ measurement sites by 40%, with a greater improvement at temperate sites. A wavelet analysis between the model and measurements from 26 FLUXNET sites suggests that the assimilated run modestly reduces the bias of simulated carbon fluxes for boreal and subtropical sites at 1–2-month time scales. At regional scales, SMAP soil moisture can improve the estimated responses of CO2 and CH4 fluxes to extreme events such as the 2018 European drought and the 2019 rainfall event in the Sudd (Southern Sudan) wetlands. The simulated improvements to land–surface carbon fluxes using the direct insertion of SMAP are shown across a variety of timescales, which suggests the potential of SMAP soil moisture in improving the model representation of carbon–water coupling.</jats:p>","journal":"Remote Sensing","year":2022,"id":22473,"datarank":0.6208479496632973,"base_score":2.833213344056216,"endowment":2.833213344056216,"self_citation_contribution":0.42498200160843247,"citation_network_contribution":0.19586594805486482,"self_endowment_contribution":0.42498200160843247,"citer_contribution":0.19586594805486482,"corpus_percentile":null,"corpus_rank":null,"citation_count":16,"citer_count":14,"citers_with_citation_signal":8,"citers_with_endowment":8,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":117143,"name":"Abhishek Chatterjee","orcid":"0000-0002-6231-5302","position":1,"is_corresponding":false},{"id":141175,"name":"Lesley Ott","orcid":null,"position":2,"is_corresponding":false},{"id":65468,"name":"Rolf H. Reichle","orcid":"0000-0001-5513-0150","position":3,"is_corresponding":false},{"id":141176,"name":"Andrew F. Feldman","orcid":null,"position":4,"is_corresponding":false},{"id":141177,"name":"Benjamin Poulter","orcid":null,"position":5,"is_corresponding":false},{"id":5433,"name":"Zhen Zhang","orcid":"0000-0001-8446-1450","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":2.833213344056216,"endowment":2.833213344056216,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21071399","pmcid":null,"openalex_id":"https://openalex.org/W4280521631","authors":[],"funders":[{"funder_name":"NASA SUSMAP","grant_id":"NNX16AQ22G","title":null},{"funder_name":"NASA SUSMAP","grant_id":"80NSSC20K0731","title":null}],"total_grants":2,"fwci":1.5852,"citation_percentile":0.81827264,"influential_citations":0,"citation_trend":[{"year":2022,"count":2},{"year":2023,"count":4},{"year":2024,"count":3},{"year":2025,"count":5},{"year":2026,"count":2}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/2072-4292/14/10/2405/pdf?version=1652792507","host_type":"journal"},{"url":"https://www.mdpi.com/2072-4292/14/10/2405/pdf?version=1652792507","host_type":"GOLD"},{"url":"https://www.mdpi.com/2072-4292/14/10/2405/pdf?version=1652792507","host_type":"publisher"},{"url":"https://www.mdpi.com/2072-4292/14/10/2405/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/rs14102405","host_type":"journal"},{"url":"http://hdl.handle.net/1903/31117","host_type":"repository"},{"url":"https://doaj.org/article/5462b847b776442295414d62e5f3e0ce","host_type":"repository"},{"url":"https://dx.doi.org/10.3390/rs14102405","host_type":"repository"},{"url":"https://doi.org/10.13016/dspace/mkec-0toi","host_type":"repository"},{"url":"https://drum.lib.umd.edu/bitstreams/4a1277f4-5767-4f9e-a958-9d9f1c04f332/download","host_type":"repository"}],"fields_of_study":["Atmospheric and Environmental Gas Dynamics","Climate variability and models","Geophysics and Gravity Measurements","Computer Science","Environmental Science"],"mesh_terms":[],"keywords":["Environmental science","Water content","Carbon cycle","Soil carbon","Terrestrial ecosystem","Atmospheric sciences","Boreal","Biosphere","Water cycle","Ecosystem","Soil water","Soil science","Geology","Ecology"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Clean water and sanitation"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-07T06:08:29.190933Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}