{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:55:37Z","timestamp":1775760937430,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,22]],"date-time":"2019-03-22T00:00:00Z","timestamp":1553212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wenquan Zhu","award":["No. 41771047"],"award-info":[{"award-number":["No. 41771047"]}]},{"name":"Wenquan Zhu","award":["No. 2017-FX-01(1)"],"award-info":[{"award-number":["No. 2017-FX-01(1)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The leaf area index (LAI) is not only an important parameter used to describe the geometry of vegetation canopy but also a key input variable for ecological models. One of the most commonly used methods for LAI estimation is to establish an empirical relationship between the LAI and the vegetation index (VI). However, the LAI-VI relationships had high seasonal variability, and they differed among phenophases and VIs. In this study, the LAI-VI relationships in different phenophases and for different VIs (i.e., the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv)) were investigated based on 82 site-years of LAI observed data and the Moderate Resolution Imaging Spectroradiometer (MODIS) VI products. Significant LAI-VI relationships were observed during the vegetation growing and declining periods. There were weak LAI-VI relationships (p &gt; 0.05) during the flourishing period. The accuracies for the LAIs estimated with the piecewise LAI-VI relationships based on different phenophases were significantly higher than those estimated based on a single LAI-VI relationship for the entire vegetation active period. The average root mean square error (RMSE) \u00b1 standard deviation (SD) value for the LAIs estimated with the piecewise LAI-VI relationships was 0.38 \u00b1 0.13 (based on the NDVI), 0.41 \u00b1 0.13 (based on the EVI) and 0.41 \u00b1 0.14 (based on the NIRv), respectively. In comparison, it was 0.46 \u00b1 0.13 (based on the NDVI), 0.55 \u00b1 0.15 (based on the EVI) and 0.55 \u00b1 0.15 (based on the NIRv) for those estimated with a single LAI-VI relationship. The performance of the three VIs in estimating the LAI also varied among phenophases. During the growing period, the mean RMSE \u00b1 SD value for the estimated LAIs was 0.30 \u00b1 0.11 (LAI-NDVI relationships), 0.37 \u00b1 0.11 (LAI-EVI relationships) and 0.36 \u00b1 0.13 (LAI-NIRv relationships), respectively, indicating the NDVI produced significantly better LAI estimations than those from the other two VIs. In contrast, the EVI produced slightly better LAI estimations than those from the other two VIs during the declining period (p &gt; 0.05), and the mean RMSE \u00b1 SD value for the estimated LAIs was 0.45 \u00b1 0.16 (LAI-NDVI relationships), 0.43 \u00b1 0.23 (LAI-EVI relationships) and 0.45 \u00b1 0.25 (LAI-NIRv relationships), respectively. Hence, the piecewise LAI-VI relationships based on different phenophases were recommended for the estimations of the LAI instead of a single LAI-VI relationship for the entire vegetation active period. Furthermore, the optimal VI in each phenophase should be selected for the estimations of the LAI according to the characteristics of vegetation growth.<\/jats:p>","DOI":"10.3390\/rs11060689","type":"journal-article","created":{"date-parts":[[2019,3,25]],"date-time":"2019-03-25T06:56:52Z","timestamp":1553497012000},"page":"689","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Estimating the Seasonal Dynamics of the Leaf Area Index Using Piecewise LAI-VI Relationships Based on Phenophases"],"prefix":"10.3390","volume":"11","author":[{"given":"Kun","family":"Qiao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Wenquan","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Zhiying","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Peixian","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1093\/jxb\/erg263","article-title":"Ground-based measurements of leaf area index: A review of methods, instruments and current controversies","volume":"54","author":"Breda","year":"2003","journal-title":"J. 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