مورد إلكتروني
Estimating local-scale forest GPP in Northern Europe using Sentinel-2:Model comparisons with LUE, APAR, the plant phenology index, and a light response function
| العنوان: | Estimating local-scale forest GPP in Northern Europe using Sentinel-2:Model comparisons with LUE, APAR, the plant phenology index, and a light response function |
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| المصدر: | Junttila , S , Ardö , J , Cai , Z , Jin , H , Kljun , N , Klemedtsson , L , Krasnova , A , Lange , H , Lindroth , A , Mölder , M , Noe , S M , Tagesson , T , Vestin , P , Weslien , P & Eklundh , L 2023 , ' Estimating local-scale forest GPP in Northern Europe using Sentinel-2 : Model comparisons with LUE, APAR, the plant phenology index, and a light response function ' , Science of Remote Sensing , vol. 7 , 100075 . |
| بيانات النشر: | 2023 |
| تفاصيل مُضافة: | Junttila, Sofia Ardö, Jonas Cai, Zhanzhang Jin, Hongxiao Kljun, Natascha Klemedtsson, Leif Krasnova, Alisa Lange, Holger Lindroth, Anders Mölder, Meelis Noe, Steffen M. Tagesson, Torbern Vestin, Patrik Weslien, Per Eklundh, Lars |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Northern forest ecosystems make up an important part of the global carbon cycle. Hence, monitoring local-scale gross primary production (GPP) of northern forest is essential for understanding climatic change impacts on terrestrial carbon sequestration and for assessing and planning management practices. Here we evaluate and compare four methods for estimating GPP using Sentinel-2 data in order to improve current available GPP estimates: four empirical regression models based on either the 2-band Enhanced Vegetation Index (EVI2) or the plant phenology index (PPI), an asymptotic light response function (LRF) model, and a light-use efficiency (LUE) model using the MOD17 algorithm. These approaches were based on remote sensing vegetation indices, air temperature (Tair), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). The models were parametrized and evaluated using in-situ data from eleven forest sites in North Europe, covering two common forest types, evergreen needleleaf forest and deciduous broadleaf forest. Most of the models gave good agreement with eddy covariance-derived GPP. The VI-based regression models performed well in evergreen needleleaf forest (R2=0.69–0.78, RMSE=1.97–2.28 g C m−2 d−1, and NRMSE=9–11.0%, eight sites), whereas the LRF and MOD17 performed slightly worse (R2=0.65 and 0.57, RMSE=2.49 and 2.72 g C m−2 d−1, NRMSE=12 and 13.0%, respectively). In deciduous broadleaf forest all models, except the LRF, showed close agreements with the observed GPP (R2=0.75–0.80, RMSE=2.23–2.46 g C m−2 d−1, NRMSE=11–12%, three sites). For the LRF model, R2=0.57, RMSE=3.21 g C m−2 d−1, NRMSE=16%. The results highlighted the necessity of improved models in evergreen needleleaf forest where the LUE approach gave poorer results., The simplest regression model using only PPI performed well beside more |
| مصطلحات الفهرس: | Enhanced vegetation index 2, Gross primary production, Light response function, Plant phenology index, Sentinel-2, dk/atira/pure/sustainabledevelopmentgoals/life_on_land, SDG 15 - Life on Land, article |
| URL: | |
| الإتاحة: | Open access content. Open access content info:eu-repo/semantics/openAccess |
| ملاحظة: | application/pdf English |
| أرقام أخرى: | EDN oai:pure.atira.dk:publications/4817d803-8d70-4c78-a33e-7a00ec7bbb77 1397137059 |
| المصدر المساهم: | TECHNICAL KNOWLEDGE CTR DENMARK From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1397137059 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |