{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T13:37:22Z","timestamp":1786714642139,"version":"build-2736575974"},"reference-count":89,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Seasonal canopy changes in subtropical evergreen broad-leaved forests are subtle, making it challenging to quantify phenological dynamics and their climatic drivers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Using phenology camera (\n                      <jats:italic>PhenoCam<\/jats:italic>\n                      ) imagery from evergreen broad-leaved trees in Jiangxi Province, China, collected in 2024, together with environmental variables spanning 2023 and 2024, four vegetation indices - the Normalized Difference Vegetation Index (\n                      <jats:italic>NDVI<\/jats:italic>\n                      ) and RGB-based chromatic indices, including Green Chromatic Coordinate (\n                      <jats:italic>\n                        G\n                        <jats:sub>cc<\/jats:sub>\n                      <\/jats:italic>\n                      ), Red Chromatic Coordinate (\n                      <jats:italic>\n                        R\n                        <jats:sub>cc<\/jats:sub>\n                      <\/jats:italic>\n                      ), and the derived Red-Green Vegetation Index (\n                      <jats:italic>RGVI<\/jats:italic>\n                      ) - were extracted to evaluate their performances in resolving phenophases and to identify environmental controls. Multiple meteorological variables were reduced to a small set of independent climatic variables using principal component analysis, and related to latent phenological indicators via Pearson correlation analysis. Phenological transition dates were estimated by fitting a double Logistic model to the time series data, after which the effects of lagged environmental variables on each transition were quantified.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The analysis shows that\n                      <jats:italic>\n                        R\n                        <jats:sub>cc<\/jats:sub>\n                      <\/jats:italic>\n                      and\n                      <jats:italic>RGVI<\/jats:italic>\n                      most closely tracked seasonal climate variation and outperformed\n                      <jats:italic>NDVI<\/jats:italic>\n                      and\n                      <jats:italic>\n                        G\n                        <jats:sub>cc<\/jats:sub>\n                      <\/jats:italic>\n                      for phenophases detection. Canopy dynamics were primarily associated with radiation, air temperature, moisture availability and atmospheric pressure. Based on\n                      <jats:italic>RGVI<\/jats:italic>\n                      double logistic fitting, the growing season commenced in early April (day-of-year (\n                      <jats:italic>DOY<\/jats:italic>\n                      ) 98), peaked by late April (\n                      <jats:italic>DOY<\/jats:italic>\n                      116) and commenced a significant decline in early December (\n                      <jats:italic>DOY<\/jats:italic>\n                      340), spanning a growing-season length of 242 days. The timing of phenological events showed clear carry-over effects: prior-season environmental anomalies exerted lagged influences on subsequent canopy development, after certain threshold conditions were exceeded.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>\n                      Overall, our findings clarify that near-ground\n                      <jats:italic>PhenoCam<\/jats:italic>\n                      s provide sensitive, scalable indicators of evergreen canopy dynamics in the subtropics. Red-Green Vegetation Index offers reliable phenophase detection, and incorporating cross-season lag effects will improve the understanding of phenological mechanisms in evergreen ecosystems.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1830218","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:38:11Z","timestamp":1783316291000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Unveiling cryptic phenology and environmental responses in subtropical evergreen broad-leaved tree canopies using phenology camera observations"],"prefix":"10.3389","volume":"17","author":[{"given":"Lu","family":"Yang","sequence":"first","affiliation":[{"name":"College of Forestry, Shenyang Agricultural University","place":["Shenyang, China"]},{"name":"Ecology and Nature Conservation Institute, Chinese Academy of Forestry","place":["Beijing, China"]},{"name":"Dagangshan National Key Field Observation and Research Station for Forest Ecosystem","place":["Xinyu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Niu","sequence":"additional","affiliation":[{"name":"Ecology and Nature Conservation Institute, Chinese Academy of Forestry","place":["Beijing, China"]},{"name":"Dagangshan National Key Field Observation and Research Station for Forest Ecosystem","place":["Xinyu, China"]},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Conservation and Restoration","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Wang","sequence":"additional","affiliation":[{"name":"Ecology and Nature Conservation Institute, Chinese Academy of Forestry","place":["Beijing, China"]},{"name":"Dagangshan National Key Field Observation and Research Station for Forest Ecosystem","place":["Xinyu, China"]},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Conservation and Restoration","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Ecology and Nature Conservation Institute, Chinese Academy of Forestry","place":["Beijing, China"]},{"name":"Dagangshan National Key Field Observation and Research Station for Forest Ecosystem","place":["Xinyu, China"]},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Conservation and Restoration","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingfeng","family":"Song","sequence":"additional","affiliation":[{"name":"Ecology and Nature Conservation Institute, Chinese Academy of Forestry","place":["Beijing, China"]},{"name":"Dagangshan National Key Field Observation and Research Station for Forest Ecosystem","place":["Xinyu, China"]},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Conservation and Restoration","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengshi","family":"Pan","sequence":"additional","affiliation":[{"name":"Ecology and Nature Conservation Institute, Chinese Academy of Forestry","place":["Beijing, China"]},{"name":"Dagangshan National Key Field Observation and Research Station for Forest Ecosystem","place":["Xinyu, China"]},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Conservation and Restoration","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keda","family":"Wang","sequence":"additional","affiliation":[{"name":"Ecology and Nature Conservation Institute, Chinese Academy of Forestry","place":["Beijing, China"]},{"name":"Dagangshan National Key Field Observation and Research Station for Forest Ecosystem","place":["Xinyu, China"]},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Conservation and Restoration","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucheng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Forestry, Shenyang Agricultural University","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Ma","sequence":"additional","affiliation":[{"name":"SINTEF Industry","place":["Oslo, Norway"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2020.00593","article-title":"Phenocams for field phenotyping: Using very high temporal resolution digital repeated photography to investigate interactions of growth, phenology, and harvest traits","volume":"11","author":"Aasen","year":"2020","journal-title":"Front. 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