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Improved CASA-Based Net Ecosystem Productivity Estimation in China by Incorporating Developmental Factors into Autumn Phenology Model

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Title: Improved CASA-Based Net Ecosystem Productivity Estimation in China by Incorporating Developmental Factors into Autumn Phenology Model
Authors: Shuping Ji, Shilong Ren, Lei Fang, Jinyue Chen, Guoqiang Wang, Qiao Wang
Source: Remote Sensing, Vol 17, Iss 3, p 487 (2025)
Publisher Information: MDPI AG, 2025.
Publication Year: 2025
Collection: LCC:Science
Subject Terms: autumn phenology, carbon sink, light use efficiency, net ecosystem productivity, phenology model, Science
More Details: Accurately assessing the carbon sink intensity of China’s ecosystem is crucial for achieving carbon neutrality. However, existing ecosystem process models have significant uncertainties in net ecosystem productivity (NEP) estimates due to the lack of or insufficient description of phenological regulation. Although plant developmental factors have been proven to significantly influence autumn phenology, they have not been systematically incorporated into autumn phenology models. In this study, we modified the autumn phenology model (cold-degree-day, CDD) by incorporating the growing-season gross primary productivity (GPP) and the start of growing season (SOS) and used it as a constraint to improve the CASA model for quantifying NEP across China from 2003 to 2021. Validation results showed that the CDD model incorporating developmental factors significantly improved the simulation accuracy at the end of the growing season (EOS). More importantly, compared with flux tower observations, the NEP derived from the improved CASA model based on the above phenology model showed a 15.34% reduction in root mean square error and a 74% increase in the coefficient of determination relative to the original model. During the study period, China’s multiyear average total NEP was 489.67 ± 38.27 Tg C/yr, with the highest found in evergreen broadleaf forests and the lowest detected in shrublands. Temporally, China’s NEP demonstrated an overall increasing trend with an average rate of 1.75 g C/m2/yr2. However, the growth rate of NEP remained far below concurrent carbon emissions from fossil fuel combustion totally, especially for eastern China, while the northeastern regions performed relatively better. The improved regional carbon flux estimation framework proposed in this study will provide important support for developing future climate change mitigation strategies.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2072-4292
Relation: https://www.mdpi.com/2072-4292/17/3/487; https://doaj.org/toc/2072-4292
DOI: 10.3390/rs17030487
Access URL: https://doaj.org/article/9c81685369a04806b79c71da262835c7
Accession Number: edsdoj.9c81685369a04806b79c71da262835c7
Database: Directory of Open Access Journals
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  Value: <anid>AN0182983139;[b03x]01feb.25;2025Feb14.05:22;v2.2.500</anid> <title id="AN0182983139-1">Improved CASA-Based Net Ecosystem Productivity Estimation in China by Incorporating Developmental Factors into Autumn Phenology Model </title> <p>Accurately assessing the carbon sink intensity of China's ecosystem is crucial for achieving carbon neutrality. However, existing ecosystem process models have significant uncertainties in net ecosystem productivity (NEP) estimates due to the lack of or insufficient description of phenological regulation. Although plant developmental factors have been proven to significantly influence autumn phenology, they have not been systematically incorporated into autumn phenology models. In this study, we modified the autumn phenology model (cold-degree-day, CDD) by incorporating the growing-season gross primary productivity (GPP) and the start of growing season (SOS) and used it as a constraint to improve the CASA model for quantifying NEP across China from 2003 to 2021. Validation results showed that the CDD model incorporating developmental factors significantly improved the simulation accuracy at the end of the growing season (EOS). More importantly, compared with flux tower observations, the NEP derived from the improved CASA model based on the above phenology model showed a 15.34% reduction in root mean square error and a 74% increase in the coefficient of determination relative to the original model. During the study period, China's multiyear average total NEP was 489.67 ± 38.27 Tg C/yr, with the highest found in evergreen broadleaf forests and the lowest detected in shrublands. Temporally, China's NEP demonstrated an overall increasing trend with an average rate of 1.75 g C/m<sup>2</sup>/yr<sup>2</sup>. However, the growth rate of NEP remained far below concurrent carbon emissions from fossil fuel combustion totally, especially for eastern China, while the northeastern regions performed relatively better. The improved regional carbon flux estimation framework proposed in this study will provide important support for developing future climate change mitigation strategies.</p> <p>Keywords: autumn phenology; carbon sink; light use efficiency; net ecosystem productivity; phenology model</p> <hd id="AN0182983139-2">1. Introduction</hd> <p>Terrestrial ecosystems have absorbed approximately 30% of anthropogenic carbon dioxide emissions over the past decade, effectively mitigating global climate warming [[<reflink idref="bib1" id="ref1">1</reflink>]]. China's terrestrial ecosystems have been recognized as a significant component of the global carbon sink [[<reflink idref="bib2" id="ref2">2</reflink>], [<reflink idref="bib4" id="ref3">4</reflink>]]. However, due to the diversity of China's climate and ecosystems along with the significant spatial heterogeneity of terrestrial ecosystems, there are still considerable uncertainties in estimating the carbon sink capacity [[<reflink idref="bib3" id="ref4">3</reflink>]]. Reducing these uncertainties is crucial for refining the global carbon budget, supporting the formulation of climate policies, and predicting future climate change.</p> <p>Net ecosystem productivity (NEP), calculated as the difference between net primary productivity (NPP) and soil heterotrophic respiration (HR), is a key indicator for assessing carbon fluxes in terrestrial ecosystems [[<reflink idref="bib5" id="ref5">5</reflink>]]. The estimation of regional terrestrial ecosystem carbon fluxes is typically categorized into 'bottom-up' and 'top-down' approaches [[<reflink idref="bib3" id="ref6">3</reflink>]]. Terrestrial ecosystem models, such as the widely used Carnegie–Ames–Stanford Approach (CASA) model, are common 'bottom-up' methods and are important tools for assessing carbon sinks on global and regional scales [[<reflink idref="bib6" id="ref7">6</reflink>]]. These models are usually driven by inputs, such as climate variables and land-use change data, and can predict future changes in ecosystem carbon sinks [[<reflink idref="bib3" id="ref8">3</reflink>]]. However, the factors driving carbon sinks in these models are often not fully comprehensive [[<reflink idref="bib3" id="ref9">3</reflink>]]. Recently, an increasing number of studies have highlighted the significant impact of changes in the growing season length of temperate plants on ecosystem productivity [[<reflink idref="bib8" id="ref10">8</reflink>]]. While warmer autumns may increase carbon loss by extending respiratory activity, the advance in the start of the growing season (SOS) and the delay in the end of the growing season (EOS) caused by climate warming have extended the carbon uptake period and increased the net carbon uptake of Northern Hemisphere forest ecosystems [[<reflink idref="bib8" id="ref11">8</reflink>]]. Therefore, incorporating vegetation phenology information into ecosystem models is crucial for accurately assessing vegetation productivity.</p> <p>Traditional plant phenology models typically use accumulated temperature to determine EOS [[<reflink idref="bib10" id="ref12">10</reflink>], [<reflink idref="bib12" id="ref13">12</reflink>]]. However, the drivers of autumn phenology are more complex, being influenced not only by climatic conditions but also by the developmental status of plants, including the growing season gross primary production (GPP) and SOS [[<reflink idref="bib9" id="ref14">9</reflink>], [<reflink idref="bib12" id="ref15">12</reflink>]]. Developmental factors can influence plant senescence through direct mechanisms, such as carbon sink limitations from photosynthesis and programmed cell death, as well as indirect mechanisms, like intensified summer drought [[<reflink idref="bib13" id="ref16">13</reflink>]]. Studies have shown that warming-induced advances in SOS and increased growing season GPP may lead to earlier plant senescence, partially offsetting the delaying effect of warming on EOS [[<reflink idref="bib9" id="ref17">9</reflink>], [<reflink idref="bib15" id="ref18">15</reflink>]]. Furthermore, if phenology models overlook plant developmental factors, they may overestimate future delays in autumn phenology, thereby inflating projections of future ecosystem productivity [[<reflink idref="bib9" id="ref19">9</reflink>], [<reflink idref="bib12" id="ref20">12</reflink>]]. Integrating phenology algorithms into GPP models has been demonstrated to significantly enhance model accuracy and reduce biases compared to flux tower-measured GPP [[<reflink idref="bib11" id="ref21">11</reflink>]]. Therefore, improving phenology models and integrating their outputs with NPP models is essential for achieving more accurate regional estimates of NEP.</p> <p>In this study, we proposed an improved autumn phenology model and used it to constrain the output of the CASA model, aiming to enhance the prediction accuracy of NEP for China's terrestrial ecosystems. The objectives of this study were: (<reflink idref="bib1" id="ref22">1</reflink>) to construct an autumn phenology model incorporating developmental factors; (<reflink idref="bib2" id="ref23">2</reflink>) to assess the NPP simulation performance of the CASA model modified by incorporating phenology information; and (<reflink idref="bib3" id="ref24">3</reflink>) to analyze the spatial patterns and temporal trends of NEP in China from 2003 to 2021 based on the phenology-modified CASA model. This study will contribute to accounting for ecosystem carbon sinks, thereby helping to formulate carbon reduction policies and achieve carbon neutrality goals.</p> <hd id="AN0182983139-3">2. Materials and Methods</hd> <p></p> <hd id="AN0182983139-4">2.1. Data Sources</hd> <p>To extract phenological metrics, the normalized difference vegetation index (NDVI) dataset from MODIS (MYD13C1) was used. The temperature (Tem) datasets used in the autumn phenology model and NEP simulation were also obtained from MODIS (MOD11C2). GPP data used to improve the phenology model came from the revised EC-LUE (Eddy Covariance Light Use Efficiency) model [[<reflink idref="bib16" id="ref25">16</reflink>]], and GPP estimates based on satellite NIRv (near-infrared reflectance) [[<reflink idref="bib17" id="ref26">17</reflink>]]. Due to the highly consistent interannual variations between the two datasets (Figure S1), we averaged them for model improvement.</p> <p>The fraction of absorbed photosynthetically active radiation (FAPAR) and photosynthetically active radiation (PAR) required for the CASA model were obtained from Global Land Surface Satellite (GLASS) products. Monthly actual evapotranspiration (ET) was sourced from the ECMWF Reanalysis v5 (ERA5) dataset. Potential evapotranspiration (PET) and precipitation (Pre) were obtained from the China 1-km monthly potential evapotranspiration and precipitation datasets [[<reflink idref="bib18" id="ref27">18</reflink>]].</p> <p>For the land cover data, the MODIS Land Cover Climate Modeling Grid (MCD12C1) Version 6 product was used. This study focused on nine major and widely distributed vegetation types in China, including evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF), mixed forests (MF), shrublands (SHB), savannas (SAV), grasslands (GRA), and croplands (CRO), with their distribution shown in Figure 1.</p> <p>We used satellite data products from 2003 to 2021 to estimate NEP and test the feasibility of the new NEP simulation framework. All satellite data were standardized to a spatial resolution of 0.05° using the arithmetic average. The spatiotemporal resolution and data acquisition details of the satellite data used in this study are shown in Table 1.</p> <p>To test the performance of NEP simulation, we obtained 52 site-year NEP flux records across 7 flux tower sites in China (Figure 1), downloaded from the National Science & Technology Infrastructure platform (https://<ulink href="http://www.nesdc.org.cn/">www.nesdc.org.cn/</ulink> (accessed on 29 January 2025)). Finally, to evaluate the synergistic variation trend between fossil fuel carbon emissions and ecosystem carbon sequestration, we obtained apparent carbon emission data for China's four economic regions (eastern, central, western, and northeastern) from 2003 to 2021 from the Carbon Emission Accounts and Datasets (CEADs) [[<reflink idref="bib20" id="ref28">20</reflink>]].</p> <hd id="AN0182983139-5">2.2. Methods</hd> <p>The framework for NEP improvement involved three steps: (<reflink idref="bib1" id="ref29">1</reflink>) improving the EOS simulation by introducing developmental factors (growing-season GPP and SOS) into the traditional autumn phenology model; (<reflink idref="bib2" id="ref30">2</reflink>) incorporating the improved EOS simulation into the CASA model to enhance the accuracy of NPP estimation; and finally, (<reflink idref="bib3" id="ref31">3</reflink>) quantifying the terrestrial NEP by subtracting the soil heterotrophic respiration from the modified NPP. The scheme of the work is illustrated in Figure 2.</p> <hd id="AN0182983139-6">2.2.1. Phenological Extraction Methods</hd> <p>Phenology metrics were retrieved based on MODIS NDVI data. NDVI observations during the non-growing season, defined as the multiyear daily average temperature below 0 °C, were first replaced with the average value in the non-growing season. The Savitzky–Golay filter was then applied to smooth and reconstruct the NDVI curve. A double logistic function was used to fit the reconstructed NDVI time series to extract SOS and EOS. Specific details of the phenology extraction can be found in Ren et al. (2020) [[<reflink idref="bib22" id="ref32">22</reflink>]].</p> <hd id="AN0182983139-7">2.2.2. Improvement of the Autumn Phenology Model</hd> <p>The traditional autumn phenology model is based on low temperature accumulation, known as the cold degree day (CDD) model [[<reflink idref="bib10" id="ref33">10</reflink>]]. Considering the potential impacts of SOS and growing season GPP on EOS [[<reflink idref="bib9" id="ref34">9</reflink>], [<reflink idref="bib15" id="ref35">15</reflink>]], we incorporated SOS, GPP, and their combination into the CDD model, naming the models CDD<subs>S</subs>, CDD<subs>G</subs>, and CDD<subs>SG</subs>, respectively. The CDD model hypothesizes that the cooling degree starts to accumulate when the daily temperature drops below the critical temperature and stops on the day of foliar senescence [[<reflink idref="bib10" id="ref36">10</reflink>]]. The formula was as follows:</p> <p>(<reflink idref="bib1" id="ref37">1</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>d</mi></mrow></msub><mo>=</mo><mrow><munderover><mo stretchy="false">∑</mo><mrow><mi>d</mi><mo>=</mo><msub><mrow><mi>d</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow><mrow><msub><mrow><mi>d</mi></mrow><mrow><mi>y</mi></mrow></msub></mrow></munderover><mrow><mi mathvariant="normal">max</mi><mo /><mo>⁡</mo><mo>(</mo><msub><mrow><mi>T</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub><mo>−</mo><msub><mrow><mi>T</mi></mrow><mrow><mrow><mi>d</mi></mrow></mrow></msub><mo>,</mo><mo /><mn>0</mn><mo>)</mo></mrow></mrow></mrow></semantics></math> </ephtml></p> <p>(<reflink idref="bib2" id="ref38">2</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>E</mi><mi>O</mi><mi>S</mi><mo>=</mo><msub><mrow><mi>d</mi></mrow><mrow><mi>y</mi></mrow></msub><mo>,</mo><mo /><mi>i</mi><mi>f</mi><mo /><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>d</mi></mrow></msub><mo>≥</mo><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub></mrow></semantics></math> </ephtml></p> <p>where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>d</mi></mrow></msub></mrow></semantics></math> </ephtml> represents the accumulated cooling degree from day <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>d</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></semantics></math> </ephtml> to day <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>d</mi></mrow><mrow><mi>y</mi></mrow></msub></mrow></semantics></math> </ephtml> ; <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub></mrow></semantics></math> </ephtml> is the required threshold upon which EOS occurs; <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub></mrow></semantics></math> </ephtml> is the critical temperature; <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mrow><mi>d</mi></mrow></mrow></msub></mrow></semantics></math> </ephtml> is the mean daily temperature on day d; and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>d</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></semantics></math> </ephtml> is set to 1 July [[<reflink idref="bib23" id="ref39">23</reflink>]]. The model has two free parameters, <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub></mrow></semantics></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub></mrow></semantics></math> </ephtml> .</p> <p>For the CDD<subs>S</subs> and CDD<subs>G</subs> models, <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub></mrow></semantics></math> </ephtml> depends on SOS and the growing season GPP (Equations (<reflink idref="bib3" id="ref40">3</reflink>) and (<reflink idref="bib4" id="ref41">4</reflink>)), while the <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub></mrow></semantics></math> </ephtml> for CDD<subs>SG</subs> model is linearly linked to both SOS and the growing season GPP (Equation (<reflink idref="bib5" id="ref42">5</reflink>)). The <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub></mrow></semantics></math> </ephtml> of the modified models are expressed by the following equations:</p> <p>(<reflink idref="bib3" id="ref43">3</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub><mo>=</mo><mi>a</mi><mo>+</mo><mi>b</mi><mo>∗</mo><msub><mrow><mi>S</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></semantics></math> </ephtml></p> <p>(<reflink idref="bib4" id="ref44">4</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub><mo>=</mo><mi>a</mi><mo>+</mo><mi>b</mi><mo>∗</mo><msub><mrow><mi>G</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></semantics></math> </ephtml></p> <p>(<reflink idref="bib5" id="ref45">5</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>C</mi><mi>D</mi><mi>D</mi></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msub><mo>=</mo><mi>a</mi><mo>+</mo><mi>b</mi><mo>∗</mo><msub><mrow><mi>S</mi></mrow><mrow><mi>a</mi></mrow></msub><mo>+</mo><msub><mrow><mi>c</mi><mo>∗</mo><mi>G</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></semantics></math> </ephtml></p> <p>where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>S</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></semantics></math> </ephtml> is the SOS anomaly (difference from the multiyear average SOS); <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>G</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></semantics></math> </ephtml> is the growing season GPP anomaly (difference from the multiyear average GPP); and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>a</mi></mrow></semantics></math> </ephtml> , <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>b</mi></mrow></semantics></math> </ephtml> , and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>c</mi></mrow></semantics></math> </ephtml> are free parameters. To optimize free parameters in models, the simulated annealing algorithm was employed in this study. The optimal parameter combination was determined with the smallest root mean square error (RMSE) between modelled and observed EOS.</p> <hd id="AN0182983139-8">2.2.3. Modification of the CASA Model</hd> <p>In the CASA model, NPP was determined by the absorbed photosynthetically active radiation (APAR) and the light use efficiency ( <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>ε</mi></mrow></semantics></math> </ephtml> ) [[<reflink idref="bib24" id="ref46">24</reflink>]]. Here, APAR was obtained from the PAR and FAPAR.</p> <p>(<reflink idref="bib6" id="ref47">6</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi><mi>P</mi><mi>P</mi><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow><mo>=</mo><mi>A</mi><mi>P</mi><mi>A</mi><mi>R</mi><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow><mo>×</mo><mi>ε</mi><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow></mrow></semantics></math> </ephtml></p> <p>(<reflink idref="bib7" id="ref48">7</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>A</mi><mi>P</mi><mi>A</mi><mi>R</mi><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow><mo>=</mo><mi>P</mi><mi>A</mi><mi>R</mi><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow><mo>×</mo><mi>F</mi><mi>P</mi><mi>A</mi><mi>R</mi><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow></mrow></semantics></math> </ephtml></p> <p>where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>χ</mi></mrow></semantics></math> </ephtml> denotes the location; <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi></mrow></semantics></math> </ephtml> represents time; and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi><mi>P</mi><mi>P</mi><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow></mrow></semantics></math> </ephtml> is the NPP (g C/m<sups>2</sups>) at location <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>χ</mi></mrow></semantics></math> </ephtml> and time <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi></mrow></semantics></math> </ephtml> .</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>ε</mi></mrow></semantics></math> </ephtml> is constrained by both temperature and moisture and is calculated as follows:</p> <p>(<reflink idref="bib8" id="ref49">8</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>ε</mi><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow><mo>=</mo><msub><mrow><mi>T</mi></mrow><mrow><mi>ε</mi><mn>1</mn></mrow></msub><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow><mo>×</mo><msub><mrow><mi>T</mi></mrow><mrow><mi>ε</mi><mn>2</mn></mrow></msub><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow><mo>×</mo><msub><mrow><mi>W</mi></mrow><mrow><mi>ε</mi></mrow></msub><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow><mo>×</mo><msub><mrow><mi>ε</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math> </ephtml></p> <p>where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>ε</mi><mn>1</mn></mrow></msub><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow></mrow></semantics></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>ε</mi><mn>2</mn></mrow></msub><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow></mrow></semantics></math> </ephtml> represent the effects of low and high temperatures on light use efficiency, respectively; <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>W</mi></mrow><mrow><mi>ε</mi></mrow></msub><mrow><mi>χ</mi><mo>,</mo><mi>t</mi></mrow></mrow></semantics></math> </ephtml> is the moisture stress; and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>ε</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math> </ephtml> is the maximum light use efficiency under ideal conditions. Detailed calculation methods and values for <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>ε</mi><mn>1</mn></mrow></msub></mrow></semantics></math> </ephtml> , <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>ε</mi><mn>2</mn></mrow></msub></mrow></semantics></math> </ephtml> , and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>ε</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math> </ephtml> can be found in Liang et al. (2022) and Chen et al. (2024) [[<reflink idref="bib5" id="ref50">5</reflink>], [<reflink idref="bib25" id="ref51">25</reflink>]].</p> <p>The NPP calculated by the CASA model was interpolated to a 1-day resolution using a cubic spline function, and the CASA results were constrained by the actual vegetation phenological dates and temperature [[<reflink idref="bib11" id="ref52">11</reflink>], [<reflink idref="bib26" id="ref53">26</reflink>]].</p> <p>(<reflink idref="bib9" id="ref54">9</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>N</mi><mi>P</mi><mi>P</mi></mrow><mrow><mi>m</mi><mi>o</mi><mi>d</mi></mrow></msub><mo>=</mo><mi>f</mi><mo>×</mo><msub><mrow><mi>N</mi><mi>P</mi><mi>P</mi></mrow><mrow><mi>o</mi><mi>r</mi><mi>g</mi></mrow></msub></mrow></semantics></math> </ephtml></p> <p>(<reflink idref="bib10" id="ref55">10</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>f</mi><mo>=</mo><mrow><mtable><mtr><mtd><mrow><mn>0</mn><mo>,</mo><mo /><mi>i</mi><mi>f</mi><mo /><mi>T</mi><mo>≤</mo><mn>0</mn></mrow></mtd></mtr><mtr><mtd><mrow><mstyle scriptlevel="0" displaystyle="true"><mfrac><mrow><mn>1</mn></mrow><mrow><mrow><mfrac><mrow><mn>5</mn><mo>−</mo><mi>T</mi></mrow><mrow><mn>5</mn></mrow></mfrac></mrow><mo>+</mo><mn>1</mn></mrow></mfrac></mstyle><mo>×</mo><mstyle scriptlevel="0" displaystyle="true"><mfrac><mrow><mn>1</mn></mrow><mrow><mrow><mfrac><mrow><mi>S</mi><mi>O</mi><mi>S</mi><mo>−</mo><mi>d</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>S</mi></mrow></mfrac></mrow><mo>+</mo><mn>1</mn></mrow></mfrac></mstyle><mo>,</mo><mo /><mi>i</mi><mi>f</mi><mo /><mi>T</mi><mo>≤</mo><mn>5</mn><mo /><mi>a</mi><mi>n</mi><mi>d</mi><mo /><mi>d</mi><mo>≤</mo><mi>S</mi><mi>O</mi><mi>S</mi></mrow></mtd></mtr><mtr><mtd><mrow><mstyle scriptlevel="0" displaystyle="true"><mfrac><mrow><mn>1</mn></mrow><mrow><mrow><mfrac><mrow><mi>S</mi><mi>O</mi><mi>S</mi><mo>−</mo><mi>d</mi></mrow><mrow><mi>S</mi><mi>O</mi><mi>S</mi></mrow></mfrac></mrow><mo>+</mo><mn>1</mn></mrow></mfrac></mstyle><mo>,</mo><mo /><mi>i</mi><mi>f</mi><mo /><mi>T</mi><mo>></mo><mn>5</mn><mo /><mi>a</mi><mi>n</mi><mi>d</mi><mo /><mi>d</mi><mo>≤</mo><mi>S</mi><mi>O</mi><mi>S</mi></mrow></mtd></mtr><mtr><mtd><mrow><mstyle scriptlevel="0" displaystyle="true"><mfrac><mrow><mn>1</mn></mrow><mrow><mrow><mfrac><mrow><mi>d</mi><mo>−</mo><mi>E</mi><mi>O</mi><mi>S</mi></mrow><mrow><mi>E</mi><mi>O</mi><mi>S</mi></mrow></mfrac></mrow><mo>+</mo><mn>1</mn></mrow></mfrac></mstyle><mo>,</mo><mo /><mi>i</mi><mi>f</mi><mo /><mi>T</mi><mo>></mo><mn>5</mn><mo /><mi>a</mi><mi>n</mi><mi>d</mi><mo /><mi>d</mi><mo>≥</mo><mi>E</mi><mi>O</mi><mi>S</mi></mrow></mtd></mtr><mtr><mtd><mrow><mstyle scriptlevel="0" displaystyle="true"><mfrac><mrow><mn>1</mn></mrow><mrow><mrow><mfrac><mrow><mi>d</mi><mo>−</mo><mi>E</mi><mi>O</mi><mi>S</mi></mrow><mrow><mi>E</mi><mi>O</mi><mi>S</mi></mrow></mfrac></mrow><mo>+</mo><mn>1</mn></mrow></mfrac></mstyle><mo>×</mo><mstyle scriptlevel="0" displaystyle="true"><mfrac><mrow><mn>1</mn></mrow><mrow><mrow><mfrac><mrow><mn>5</mn><mo>−</mo><mi>T</mi></mrow><mrow><mn>5</mn></mrow></mfrac></mrow><mo>+</mo><mn>1</mn></mrow></mfrac></mstyle><mo>,</mo><mo /><mi>i</mi><mi>f</mi><mo /><mi>T</mi><mo>≤</mo><mn>5</mn><mo /><mi>a</mi><mi>n</mi><mi>d</mi><mo /><mi>d</mi><mo>≥</mo><mi>E</mi><mi>O</mi><mi>S</mi></mrow></mtd></mtr></mtable></mrow></mrow></semantics></math> </ephtml></p> <p>where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>N</mi><mi>P</mi><mi>P</mi></mrow><mrow><mi>o</mi><mi>r</mi><mi>g</mi></mrow></msub></mrow></semantics></math> </ephtml> is the monthly NPP simulated by the original algorithm in the CASA model, and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>N</mi><mi>P</mi><mi>P</mi></mrow><mrow><mi>m</mi><mi>o</mi><mi>d</mi></mrow></msub></mrow></semantics></math> </ephtml> is the result after being constrained with <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>f</mi></mrow></semantics></math> </ephtml> . <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>d</mi></mrow></semantics></math> </ephtml> is the day of year, and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>T</mi></mrow></semantics></math> </ephtml> is daily average temperature. SOS was satellite-derived, while EOS was derived from the CDD<subs>SG</subs> model.</p> <hd id="AN0182983139-9">2.2.4. NEP Estimation</hd> <p>NEP was obtained by subtracting soil heterotrophic respiration (HR) from NPP.</p> <p>(<reflink idref="bib11" id="ref56">11</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="normal">N</mi><mi mathvariant="normal">E</mi><mi mathvariant="normal">P</mi><mo>=</mo><mi mathvariant="normal">N</mi><mi mathvariant="normal">P</mi><mi mathvariant="normal">P</mi><mo>−</mo><mi mathvariant="normal">H</mi><mi mathvariant="normal">R</mi></mrow></semantics></math> </ephtml></p> <p>The geostatistical model of soil respiration (GSMSR), driven by temperature, precipitation, and soil organic carbon density [[<reflink idref="bib27" id="ref57">27</reflink>]], was employed to obtain the monthly soil respiration ( <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>S</mi></mrow></msub></mrow></semantics></math> </ephtml> ).</p> <p>(<reflink idref="bib12" id="ref58">12</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>S</mi></mrow></msub><mo>=</mo><mo stretchy="false">(</mo><msub><mrow><mi>R</mi></mrow><mrow><msub><mrow><mi>D</mi></mrow><mrow><mi>S</mi><mo>=</mo><mn>0</mn></mrow></msub></mrow></msub><mo>+</mo><mi>M</mi><msub><mrow><mi>D</mi></mrow><mrow><mi>S</mi></mrow></msub><mo stretchy="false">)</mo><msup><mrow><mi>e</mi></mrow><mrow><mrow><mrow><mi mathvariant="normal">ln</mi></mrow><mo>⁡</mo><mrow><mi>α</mi><msup><mrow><mi>e</mi></mrow><mrow><mi>β</mi><mi>T</mi></mrow></msup><mi>T</mi><mo>/</mo><mn>10</mn></mrow></mrow></mrow></msup><mo stretchy="false">(</mo><mi>P</mi><mo>+</mo><msub><mrow><mi>P</mi></mrow><mrow><mn>0</mn></mrow></msub><mo stretchy="false">)</mo><mo>/</mo><mo stretchy="false">(</mo><mi>K</mi><mo>+</mo><mi>P</mi><mo stretchy="false">)</mo></mrow></semantics></math> </ephtml></p> <p>where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><msub><mrow><mi>D</mi></mrow><mrow><mi>S</mi><mo>=</mo><mn>0</mn></mrow></msub></mrow></msub></mrow></semantics></math> </ephtml> is set as 0.588, <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>M</mi></mrow></semantics></math> </ephtml> to 0.118, <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>α</mi></mrow></semantics></math> </ephtml> to 1.83, <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>β</mi></mrow></semantics></math> </ephtml> to −0.006, <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></semantics></math> </ephtml> to 2.972, and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>K</mi></mrow></semantics></math> </ephtml> to 5.657 [[<reflink idref="bib27" id="ref59">27</reflink>]]. <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>D</mi></mrow><mrow><mi>S</mi></mrow></msub></mrow></semantics></math> </ephtml> is the soil organic carbon density at 20 cm depth, varies across different vegetation types; for specific values, refer to Chen et al. (2024) [[<reflink idref="bib25" id="ref60">25</reflink>]]. <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>T</mi></mrow></semantics></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>P</mi></mrow></semantics></math> </ephtml> represent the monthly average temperature and precipitation, respectively.</p> <p>HR was calculated based on the empirical relationship between <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>S</mi></mrow></msub></mrow></semantics></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="normal">H</mi><mi mathvariant="normal">R</mi></mrow></semantics></math> </ephtml> [[<reflink idref="bib5" id="ref61">5</reflink>]].</p> <p>(<reflink idref="bib13" id="ref62">13</reflink>) <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="normal">H</mi><mi mathvariant="normal">R</mi><mo>=</mo><mo>−</mo><mn>0.0009</mn><msubsup><mrow><mi>R</mi></mrow><mrow><mi>S</mi></mrow><mrow><mn>2</mn></mrow></msubsup><mo>+</mo><mn>0.6011</mn><msub><mrow><mi>R</mi></mrow><mrow><mi>S</mi></mrow></msub><mo>+</mo><mn>4.8874</mn></mrow></semantics></math> </ephtml></p> <p>where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>S</mi></mrow></msub></mrow></semantics></math> </ephtml> is the monthly soil respiration.</p> <hd id="AN0182983139-10">2.2.5. Evaluation of Model Performances</hd> <p>We assessed the accuracy of the phenology model based on two metrics: RMSE and Pearson correlation coefficient (<emph>r</emph>) [[<reflink idref="bib10" id="ref63">10</reflink>], [<reflink idref="bib28" id="ref64">28</reflink>]]. The original CASA output <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>N</mi><mi>P</mi><mi>P</mi></mrow><mrow><mi>o</mi><mi>r</mi><mi>g</mi></mrow></msub></mrow></semantics></math> </ephtml> and the phenology-modified CASA output <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mrow><mi>N</mi><mi>P</mi><mi>P</mi></mrow><mrow><mi>m</mi><mi>o</mi><mi>d</mi></mrow></msub></mrow></semantics></math> </ephtml> were compared with the MODIS gap-filled yearly NPP dataset (MOD17A3HGF) using the coefficient of determination (R<sups>2</sups>) and RMSE. Additionally, the performance of NEP estimation was also evaluated using R<sups>2</sups> and RMSE by comparing NEP flux data with NEP data produced by either the original CASA or the phenology-modified CASA [[<reflink idref="bib11" id="ref65">11</reflink>]].</p> <hd id="AN0182983139-11">2.2.6. Analysis</hd> <p>We calculated the trend of terrestrial NEP across China for each pixel using the Theil-Sen trend estimator and assessed the significance of the trends at the 0.05 level with the non-parametric Mann–Kendall test [[<reflink idref="bib29" id="ref66">29</reflink>]]. One-way analysis of variance (ANOVA) with Tukey's HSD test was used to examine differences in the performance of phenology models as well as differences in NEP and its trends across different vegetation types and regions. Finally, we assessed the interannual trend of the difference between fossil fuel carbon emissions and ecosystem carbon sequestration in China's four economic regions. All analyses were performed in Python.</p> <hd id="AN0182983139-12">3. Results</hd> <p></p> <hd id="AN0182983139-13">3.1. Performances Assessment of Models</hd> <p>Overall, the models that incorporated developmental factors outperformed the original phenology model (Figure 3a,b). The CDD<subs>SG</subs> model achieved the highest average <emph>r</emph> values (0.10), significantly surpassing the other models, followed by the CDD<subs>S</subs> (0.09), CDD<subs>G</subs> (0.03), and CDD models (0.01). In terms of the prediction accuracy, the EOS simulated by the CDD<subs>G</subs> model exhibited a significantly lower RMSE (14.27 days) compared to other models, followed by CDD<subs>SG</subs> (14.95 days), CDD<subs>S</subs> (15.32 days), and CDD (18.28 days). Based on these results, we introduced the EOS predicted by the CDD<subs>SG</subs> model into the CASA model.</p> <p>The NPP produced by the phenology-modified CASA model was more consistent with the MODIS NPP (Figure 3c,d). Specifically, the NPP simulated by the phenology-modified CASA model displayed a relatively higher R<sups>2</sups> (0.58 vs. 0.53) and lower RMSE (3615.5 vs. 3826.7 g C/m<sups>2</sups>/yr) compared to the counterparts of the original CASA model. Similarly, the following comparison of NEP based on the original CASA model and the phenology-modified CASA model against flux tower NEP observations revealed that the RMSE of the phenology-modified model decreased by 15.34% compared to the original model, while the R<sups>2</sups> increased from 0.27 to 0.47, representing an improvement of approximately 74% (Figure 3e,f).</p> <hd id="AN0182983139-14">3.2. Spatial Patterns of NEP Estimates</hd> <p>From 2003 to 2021, the total annual average NEP over China was 489.67 ± 38.27 Tg C/yr (mean ± standard error), with an average of 68.78 g C/m<sups>2</sups>/yr per unit area (Figure 4a,b). Of the pixels, 52.0% showed a multiyear average NEP greater than 0, serving as carbon sinks (Figure 4c). Spatially, stronger carbon sink capabilities were observed in the southeast, southwest, and the three northeastern provinces, generally exceeding 300 g C/m<sups>2</sups>/yr, while most of the Bohai Sea Rim area, Inner Mongolia, and the central region of the Qinghai–Tibet Plateau exhibited weak carbon sources (Figure 4a).</p> <p>Across diverse vegetation types, the multiyear average NEP of EBF (600.53 g C/m<sups>2</sups>/yr) was significantly higher than that of other vegetation types, followed by DBF (348.64 g C/m<sups>2</sups>/yr) and SAV (236.48 g C/m<sups>2</sups>/yr) (Figure 4d). In contrast, SHB, GRA, and CRO functioned as carbon sources, displaying negative multiyear average NEP values of −113.34, −46.16, and −45.43 g C/m<sups>2</sups>/yr, respectively. From a regional perspective, all four economic regions in China played a role as carbon sinks from 2003 to 2021 (Figure 4e). The multiyear average NEP in the eastern region (174.17 g C/m<sups>2</sups>/yr) was the greatest, likely due to the highest forest coverage in China, followed by the northeastern, central, and western regions, with NEP values of 119.52, 101.59, and 32.49 g C/m<sups>2</sups>/yr, respectively.</p> <hd id="AN0182983139-15">3.3. Temporal Trends of NEP Estimates</hd> <p>Overall, this study found an increasing tendency of NEP in China with an average rate of 1.75 g C/m<sups>2</sups>/yr<sups>2</sups> from 2003 to 2021 (Figure 5a,b). Specifically, this increasing trend was identified in 73.30% of the pixels, with 20.32% passing the significance test (Figure 5c). Spatially, the increase in NEP was primarily concentrated in central regions, such as Guizhou, Chongqing, and Hunan, while most eastern coastal areas exhibited a declining trend. Further investigation revealed an increment of NEP across all vegetation types except for EBF and DNF. Among them, SAV exhibited the highest NEP growth rate of 3.40 g C/m<sups>2</sups>/yr<sups>2</sups>, followed by MF (1.83 g C/m<sups>2</sups>/yr<sups>2</sups>) and DBF (1.81 g C/m<sups>2</sups>/yr<sups>2</sups>). Subregionally, all areas showed positive NEP trends during the study period, except the economically developed eastern region (−0.01 g C/m<sups>2</sups>/yr<sups>2</sups>), with the central region exhibiting the most pronounced increase at 2.21 g C/m<sups>2</sups>/yr<sups>2</sups>.</p> <p>By analyzing the trends in the difference between total apparent carbon emissions and ecosystem NEP, we identified significant regional disparities in carbon neutrality progress across China's regions (Figure 6). The central region exhibited the fastest net carbon emission growth, reaching 40.8 Tg C/yr, suggesting that carbon emissions from fossil fuel consumption substantially outpaced the enhancement in ecosystem carbon sequestration. The western and eastern regions showed comparable net carbon emission growth rates of 36.9 and 36.1 Tg C/yr, respectively. In contrast, the northeastern region performed the most favorably, exhibiting both the lowest total net carbon emissions throughout the study period and the most modest growth rate at 6.8 Tg C/yr.</p> <hd id="AN0182983139-16">4. Discussion</hd> <p>Accurate phenological models are crucial tools for predicting vegetation phenological responses to future climate scenarios and their subsequent impacts on ecosystem functions [[<reflink idref="bib31" id="ref67">31</reflink>]]. Given the significant influence of developmental factors on autumn phenology, this study enhanced the traditional autumn phenology model by incorporating growing season GPP and SOS. The results showed that whether considering GPP or SOS alone or incorporating both factors, the modified autumn phenology models outperformed the original temperature-based model, significantly enhancing the simulation capability of autumn phenology (Figure 3). Traditional temperature-based models predict that under future warming conditions, vegetation productivity will increase due to extended growing seasons [[<reflink idref="bib8" id="ref68">8</reflink>], [<reflink idref="bib33" id="ref69">33</reflink>]]. However, in temperate forests of the Northern Hemisphere, increased growing season productivity and earlier spring phenology triggered by rising CO<subs>2</subs> concentrations, higher temperatures, or changes in light levels may accelerate plant senescence, potentially offsetting the promotional effects of warming on growing season extension [[<reflink idref="bib9" id="ref70">9</reflink>], [<reflink idref="bib15" id="ref71">15</reflink>]]. Therefore, current phenological models that do not consider developmental factors may seriously overestimate future growing season length, leading to significant biases in global carbon cycle estimates [[<reflink idref="bib8" id="ref72">8</reflink>]]. Future research should fully integrate developmental factors into predictions of plant phenology, which may revise our expectations of plant carbon sequestration capacity and avoid overly optimistic estimates of ecosystem carbon sink potential under future climate change.</p> <p>In recent years, numerous studies have employed various methods to estimate the carbon sink capacity of China's terrestrial ecosystems [[<reflink idref="bib4" id="ref73">4</reflink>], [<reflink idref="bib25" id="ref74">25</reflink>]]. While estimates vary across methodologies, there is consensus that China's terrestrial ecosystems act as a significant carbon sink [[<reflink idref="bib2" id="ref75">2</reflink>]]. Piao et al. (2022) [[<reflink idref="bib3" id="ref76">3</reflink>]] systematically compared estimates from different periods and methods, revealing that China's terrestrial ecosystems exhibit a carbon sink capacity ranging from 0.12 to 1.11 Pg C/yr. Our study estimated an average NEP of 0.49 Pg C/yr for the period 2003–2021, which falls within the credible range but is slightly higher than estimates from ecosystem process models. This discrepancy likely stems from variations in temporal coverage, as China's NEP has shown significant increases since the 21st century [[<reflink idref="bib34" id="ref77">34</reflink>]], while other studies include NEP data from before 2000, lowering the overall average in those estimates. On the other hand, the spatial distribution of NEP estimated in our study aligns with previous studies [[<reflink idref="bib2" id="ref78">2</reflink>], [<reflink idref="bib4" id="ref79">4</reflink>], [<reflink idref="bib25" id="ref80">25</reflink>]], with strong carbon sink areas primarily located in the southeastern and southwestern parts of China. Forests serve as the primary carbon sinks, while shrubs, grasslands, and croplands act as weak carbon sources (Figure 4). Forest ecosystems have high productivity and carbon storage capacity, and studies have shown that existing global forests alone can account for the land carbon sink [[<reflink idref="bib35" id="ref81">35</reflink>]]. In contrast, shrubs and grasslands have low biomass and limited carbon storage capacity, with carbon absorption and respiration being generally balanced, resulting in a weak carbon sink or even a carbon source [[<reflink idref="bib36" id="ref82">36</reflink>]].</p> <p>Consistent with other studies, China's terrestrial carbon sink showed a significant upward trend from 2003 to 2021 (Figure 5), likely driven by multiple factors. First, the fertilization effect of increased atmospheric CO<subs>2</subs> concentrations significantly enhanced plant photosynthesis efficiency, thereby promoting carbon uptake [[<reflink idref="bib17" id="ref83">17</reflink>], [<reflink idref="bib38" id="ref84">38</reflink>]]. Second, in both the northern and southern forest regions, climate warming extended the growing season, which may have further strengthened carbon sequestration capacity [[<reflink idref="bib8" id="ref85">8</reflink>]]. Additionally, in southeastern China, increased anthropogenic nitrogen deposition has been shown to have a significant positive impact on plant growth [[<reflink idref="bib39" id="ref86">39</reflink>]], potentially providing crucial support for the enhancement of carbon sinks [[<reflink idref="bib40" id="ref87">40</reflink>]]. Finally, human interventions played a key role, such as large-scale afforestation projects contributing to over 70% of forest area expansion, natural forest protection, and ecological restoration programs, like the conversion of grazing land to grassland, which effectively promoted vegetation growth and enhanced ecosystem carbon sequestration capacity [[<reflink idref="bib41" id="ref88">41</reflink>]]. The combined effects of CO<subs>2</subs> fertilization, climate warming, increased nitrogen deposition, and human interventions collectively drove the continuous growth of China's terrestrial carbon sink.</p> <p>Our analysis of the residual emissions after offsetting industrial carbon emissions with the carbon sink of China's terrestrial ecosystems revealed that, across all economic regions, the growth in ecosystem carbon sinks was smaller than the increase in carbon emissions from fossil fuel combustion during the same period (Figure 6). There were also regional differences in carbon neutrality levels. The economically developed eastern regions, including the Bohai Sea Rim and the Huang–Huai–Hai region, were far from achieving carbon neutrality. These areas were predominantly cropland with inherently weak carbon sink capacity, combined with high levels of industrial development and dense populations, leading to significantly higher apparent carbon emissions and residual emissions compared to other regions. In contrast, the northeastern region, with its relatively slower economic growth, showed a slower increase in residual emissions and exhibited better carbon neutrality levels than other regions. The western and central regions had relatively similar levels of carbon neutrality. Studies have suggested that as China's forests gradually mature and age, the carbon sink capacity might decline, further reducing the ability of terrestrial ecosystems to offset industrial carbon emissions [[<reflink idref="bib3" id="ref89">3</reflink>], [<reflink idref="bib43" id="ref90">43</reflink>]]. Therefore, achieving carbon neutrality requires the coordinated implementation of multiple strategies.</p> <p>The NEP simulation framework we developed also has some uncertainties. First, discrepancies may exist between the actual start and end dates of plant photosynthesis and the SOS and EOS derived from the phenology model. Studies have shown that photosynthesis can continue until chlorophyll depletion in the leaves [[<reflink idref="bib44" id="ref91">44</reflink>]]. Second, ecosystem process models, such as CASA, do not account for human factors, like irrigation, fertilization, and forest management, all of which can significantly influence plant growth [[<reflink idref="bib3" id="ref92">3</reflink>]]. This adds uncertainty to the NEP estimates, and caution is needed when applying these models to agricultural ecosystems. Furthermore, ecosystem process models rely on remote sensing data, and the accuracy of these estimates is highly dependent on the quality of the remote sensing data [[<reflink idref="bib11" id="ref93">11</reflink>]]. The relatively coarse resolution of remote sensing data may also obscure NEP variations in specific plant types [[<reflink idref="bib22" id="ref94">22</reflink>]]. In the future, when extending our NEP accounting framework to larger study areas, special attention should be given to data quality, and multi-source data should be used for cross-validation to improve the reliability of the estimates. Additionally, more factors influencing plant growth should be incorporated into carbon sink accounting models to improve their comprehensiveness and accuracy.</p> <hd id="AN0182983139-17">5. Conclusions</hd> <p>This study enhanced NEP estimation by incorporating developmental factors (growing-season GPP and SOS) into autumn phenology model and using the modified phenology outputs to constrain the CASA model. The integration of developmental factors significantly improved the autumn phenology model's performance, resulting in lower RMSE and higher correlation. The CASA model, constrained by phenological information, exhibited markedly better alignment with MODIS NPP products. Moreover, compared to the original model, the NEP produced by the phenology-modified CASA model achieved a 15.34% reduction in RMSE and a 74% improvement in R<sups>2</sups> against flux tower observations. From 2003 to 2021, the annual average total NEP of China's terrestrial ecosystems was 489.67 ± 38.27 Tg C/yr and significantly increased with an average growth rate of 1.75 g C/m<sups>2</sups>/yr<sups>2</sups>. Among vegetation types, evergreen broadleaf forests maintained the highest NEP, while shrublands exhibited the lowest values. However, the NEP growth rates across all four economic regions remained insufficient to offset the concurrent increases in industrial carbon emissions, with eastern China showing the highest net carbon emissions and facing significant challenges in achieving carbon neutrality. This study proposes a new and more accurate framework for NEP estimation, which provides valuable insights for understanding and predicting China's carbon sink strength and offers scientific support for the formulation of carbon neutrality policies.</p> <hd id="AN0182983139-18">Figures and Table</hd> <p>Graph: Figure 1 Distribution of the landcover types, flux tower sites, and four economic regions across China.</p> <p>Graph: Figure 2 The framework of NEP improvement. SOC refers to soil organic carbon density.</p> <p>Graph: Figure 3 Model performance comparison. (a,b) Pearson's correlation coefficient and RMSE between predicted EOS from different phenology models and observed EOS, respectively. Different letters indicate significant differences among models (p < 0.05). (c,d) Comparison between the NPP modeled by the original CASA and the phenology-modified CASA with MODIS NPP, respectively. (e,f) Accuracy of NEP produced by the original CASA and the phenology-modified CASA compared to the NEP flux observations, respectively.</p> <p>Graph: Figure 4 The multiyear mean of annual NEP in China produced by the phenology-modified CASA model during 2003–2021. (a) The spatial pattern of the multiyear mean NEP during 2003–2021. (b) The multiyear mean NEP over China. (c) The density distribution of the multiyear mean annual NEP. (d,e) Multiyear mean NEP across various vegetation types and economic regions. The yellow dots represent the mean value, and the horizontal lines within the bars denote the median value. Different letters indicate significant differences among models (p < 0.05).</p> <p>Graph: Figure 5 The trends of NEP in China produced by the phenology-modified CASA model during 2003–2021. (a) The spatial pattern of NEP trend from 2003 to 2021. (b) The mean NEP trend across China. (c) The density distribution of the NEP trend. The numbers in parentheses represent the proportion that passed the significance test (p < 0.05). (d,e) The NEP trend across different vegetation types and economic regions. The yellow dots represent the mean value, and the horizontal lines within the bars denote the median value. Different letters indicate significant differences among models (p < 0.05).</p> <p>Graph: Figure 6 The tendency comparison of the difference between apparent carbon emissions and terrestrial NEP in China's four economic regions from 2003 to 2021.</p> <p>Table 1 The spatiotemporal resolution and data accessibility details of the satellite datasets.</p> <p> <ephtml> <table><thead><tr><th align="center" style="border-top:solid thin;border-bottom:solid thin">Dataset</th><th align="center" style="border-top:solid thin;border-bottom:solid thin">Spatial/Temporal Resolution</th><th align="center" style="border-top:solid thin;border-bottom:solid thin">Data Access</th></tr></thead><tbody><tr><td align="center" valign="middle">MODIS NDVI (MYD13C1)</td><td align="center" valign="middle">0.05°/16-day</td><td align="center" valign="middle"><ext-link href="https://ladsweb.modaps.eosdis.nasa.gov" /> (accessed on 29 January 2025)</td></tr><tr><td align="center" valign="middle">MODIS Tem (MOD11C2)</td><td align="center" valign="middle">0.05°/8-day</td><td align="center" valign="middle"><ext-link href="https://ladsweb.modaps.eosdis.nasa.gov" /> (accessed on 29 January 2025)</td></tr><tr><td align="center" valign="middle">Revised EC-LUE GPP</td><td align="center" valign="middle">0.05°/8-day</td><td align="center" valign="middle"><ext-link href="https://doi.org/10.6084/m9.figshare.8942336.v1" /> (accessed on 29 January 2025)</td></tr><tr><td align="center" valign="middle">GPP based on NIRv</td><td align="center" valign="middle">0.05°/monthly</td><td align="center" valign="middle"><ext-link href="https://doi.org/10.6084/m9.figshare.12981977.v2" /> (accessed on 29 January 2025)</td></tr><tr><td align="center" valign="middle">GLASS FAPAR</td><td align="center" valign="middle">250 m/8-day</td><td align="center" valign="middle"><ext-link href="http://glass.umd.edu/" /> (accessed on 29 January 2025)</td></tr><tr><td align="center" valign="middle">GLASS PAR</td><td align="center" valign="middle">0.05°/daily</td><td align="center" valign="middle"><ext-link href="http://glass.umd.edu/" /> (accessed on 29 January 2025)</td></tr><tr><td align="center" valign="middle">ERA5 ET</td><td align="center" valign="middle">0.1°/monthly</td><td align="center" valign="middle"><ext-link href="https://cds.climate.copernicus.eu/datasets" /> (accessed on 29 January 2025)</td></tr><tr><td align="center" valign="middle">China PET</td><td align="center" valign="middle">1 km/monthly</td><td align="center" valign="middle"><ext-link href="http://loess.geodata.cn" /> (accessed on 29 January 2025)</td></tr><tr><td align="center" valign="middle">China Pre</td><td align="center" valign="middle">1 km/monthly</td><td align="center" valign="middle"><ext-link href="https://doi.org/10.5281/zenodo.3114194" /> (accessed on 29 January 2025)</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin">MODIS land use (MCD12C1)</td><td align="center" valign="middle" style="border-bottom:solid thin">0.05°/yearly</td><td align="center" valign="middle" style="border-bottom:solid thin"><ext-link href="https://ladsweb.modaps.eosdis.nasa.gov" /> (accessed on 29 January 2025)</td></tr></tbody></table> </ephtml> </p> <hd id="AN0182983139-19">Author Contributions</hd> <p>Conceptualization, S.J. and S.R.; methodology, S.J. and S.R.; investigation, S.J.; writing—original draft preparation, S.J. and S.R.; writing—review and editing, L.F., J.C., G.W., and Q.W. All authors have read and agreed to the published version of the manuscript.</p> <hd id="AN0182983139-20">Data Availability Statement</hd> <p>All data used in this study could be obtained freely online.</p> <hd id="AN0182983139-21">Conflicts of Interest</hd> <p>The authors declare no conflicts of interest.</p> <hd id="AN0182983139-22">Acknowledgments</hd> <p>We acknowledge the data support from National Earth System Science Data Center, National Science & Technology Infrastructure of China.</p> <hd id="AN0182983139-23">Supplementary Materials</hd> <p>The following supporting information can be downloaded at: https://<ulink href="http://www.mdpi.com/article/10.3390/rs17030487/s1,">www.mdpi.com/article/10.3390/rs17030487/s1,</ulink> Figure S1: Correlation of interannual fluctuations between different GPP datasets.</p> <ref id="AN0182983139-24"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.</bibtext> </blist> </ref> <ref id="AN0182983139-25"> <title> References </title> <blist> <bibtext> Le Quéré C., Moriarty R., Andrew R.M., Canadell J.G., Sitch S., Korsbakken J.I., Friedlingstein P., Peters G.P., Andres R.J., Boden T.A. Global Carbon Budget 2015. Earth Syst. Sci. Data. 2015; 7: 349-396. 10.5194/essd-7-349-2015</bibtext> </blist> <blist> <bibl id="bib2" idref="ref2" type="bt">2</bibl> <bibtext> Jung M., Reichstein M., Margolis H.A., Cescatti A., Richardson A.D., Arain M.A., Arneth A., Bernhofer C., Bonal D., Chen J. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. Biogeosci. 2011; 116G00J07. 10.1029/2010JG001566</bibtext> </blist> <blist> <bibl id="bib3" idref="ref4" type="bt">3</bibl> <bibtext> Piao S., He Y., Wang X., Chen F. Estimation of China's terrestrial ecosystem carbon sink: Methods, progress and prospects. Sci. China Earth Sci. 2022; 65: 641-651. 10.1007/s11430-021-9892-6</bibtext> </blist> <blist> <bibl id="bib4" idref="ref3" type="bt">4</bibl> <bibtext> Yao Y., Li Z., Wang T., Chen A., Wang X., Du M., Jia G., Li Y., Li H., Luo W. A new estimation of China's net ecosystem productivity based on eddy covariance measurements and a model tree ensemble approach. Agric. For. Meteorol. 2018; 253–254: 84-93. 10.1016/j.agrformet.2018.02.007</bibtext> </blist> <blist> <bibl id="bib5" idref="ref5" type="bt">5</bibl> <bibtext> Liang L., Geng D., Yan J., Qiu S., Shi Y., Wang S., Wang L., Zhang L., Kang J. Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China. Remote Sens. 2022; 141902. 10.3390/rs14081902</bibtext> </blist> <blist> <bibl id="bib6" idref="ref7" type="bt">6</bibl> <bibtext> Friedlingstein P., O'Sullivan M., Jones M.W., Andrew R.M., Bakker D.C.E., Hauck J., Landschützer P., Le Quéré C., Luijkx I.T., Peters G.P. Global Carbon Budget 2023. Earth Syst. Sci. Data. 2023; 15: 5301-5369. 10.5194/essd-15-5301-2023</bibtext> </blist> <blist> <bibl id="bib7" idref="ref48" type="bt">7</bibl> <bibtext> Piao S., Liu Z., Wang T., Peng S., Ciais P., Huang M., Ahlstrom A., Burkhart J.F., Chevallier F., Janssens I.A. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nat. Clim. Chang. 2017; 7: 359-363. 10.1038/nclimate3277</bibtext> </blist> <blist> <bibl id="bib8" idref="ref10" type="bt">8</bibl> <bibtext> Keenan T.F., Gray J., Friedl M.A., Toomey M., Bohrer G., Hollinger D.Y., Munger J.W., O'Keefe J., Schmid H.P., Wing I.S. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Chang. 2014; 4: 598-604. 10.1038/nclimate2253</bibtext> </blist> <blist> <bibl id="bib9" idref="ref14" type="bt">9</bibl> <bibtext> Zani D., Crowther T.W., Mo L., Renner S.S., Zohner C.M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science. 2020; 370: 1066-1071. 10.1126/science.abd8911</bibtext> </blist> <blist> <bibtext> Keenan T.F., Richardson A.D. The timing of autumn senescence is affected by the timing of spring phenology: Implications for predictive models. Glob. Change Biol. 2015; 21: 2634-2641. 10.1111/gcb.12890</bibtext> </blist> <blist> <bibtext> Peng J., Wu C., Zhang X., Ju W., Wang X., Lu L., Liu Y. Incorporating water availability into autumn phenological model improved China's terrestrial gross primary productivity (GPP) simulation. Environ. Res. Lett. 2021; 16: 094012. 10.1088/1748-9326/ac1a3b</bibtext> </blist> <blist> <bibtext> Zohner C.M., Mirzagholi L., Renner S.S., Mo L., Rebindaine D., Bucher R., Palouš D., Vitasse Y., Fu Y.H., Stocker B.D. Effect of climate warming on the timing of autumn leaf senescence reverses after the summer solstice. Science. 2023; 381: eadf5098. 10.1126/science.adf5098. 37410847</bibtext> </blist> <blist> <bibtext> Buermann W., Bikash P.R., Jung M., Burn D.H., Reichstein M. Earlier springs decrease peak summer productivity in North American boreal forests. Environ. Res. Lett. 2013; 8: 024027. 10.1088/1748-9326/8/2/024027</bibtext> </blist> <blist> <bibtext> Lam E. Controlled cell death, plant survival and development. Nat. Rev. Mol. Cell Biol. 2004; 5: 305-315. 10.1038/nrm1358</bibtext> </blist> <blist> <bibtext> Fu Y.S.H., Campioli M., Vitasse Y., De Boeck H.J., Van den Berge J., AbdElgawad H., Asard H., Piao S., Deckmyn G., Janssens I.A. Variation in leaf flushing date influences autumnal senescence and next year's flushing date in two temperate tree species. Proc. Natl. Acad. Sci. USA. 2014; 111: 7355-7360. 10.1073/pnas.1321727111</bibtext> </blist> <blist> <bibtext> Zheng Y., Shen R., Wang Y., Li X., Liu S., Liang S., Chen J.M., Ju W., Zhang L., Yuan W. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data. 2020; 12: 2725-2746</bibtext> </blist> <blist> <bibtext> Wang S., Zhang Y., Ju W., Chen J.M., Ciais P., Cescatti A., Sardans J., Janssens I.A., Wu M., Berry J.A. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science. 2020; 370: 1295-1300. 10.1126/science.abb7772</bibtext> </blist> <blist> <bibtext> Peng S., Ding Y., Liu W., Li Z. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data. 2019; 11: 1931-1946. 10.5194/essd-11-1931-2019</bibtext> </blist> <blist> <bibtext> Peng S., Ding Y., Wen Z., Chen Y., Cao Y., Ren J. Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100. Agric. For. Meteorol. 2017; 233: 183-194. 10.1016/j.agrformet.2016.11.129</bibtext> </blist> <blist> <bibtext> Guan Y., Shan Y., Huang Q., Chen H., Wang D., Hubacek K. Assessment to China's Recent Emission Pattern Shifts. Earth's Future. 2021; 9: e2021EF002241. 10.1029/2021EF002241</bibtext> </blist> <blist> <bibtext> Shan Y., Huang Q., Guan D., Hubacek K. China CO2 emission accounts 2016–2017. Sci. Data. 2020; 7: 54. 10.1038/s41597-020-0393-y. 32054849</bibtext> </blist> <blist> <bibtext> Ren S., Li Y., Peichl M. Diverse effects of climate at different times on grassland phenology in mid-latitude of the Northern Hemisphere. Ecol. Indic. 2020; 113: 106260. 10.1016/j.ecolind.2020.106260</bibtext> </blist> <blist> <bibtext> Wu C., Wang J., Ciais P., Peñuelas J., Zhang X., Sonnentag O., Tian F., Wang X., Wang H., Liu R. Widespread decline in winds delayed autumn foliar senescence over high latitudes. Proc. Natl. Acad. Sci. USA. 2021; 118: e2015821118. 10.1073/pnas.2015821118. 33846246</bibtext> </blist> <blist> <bibtext> Potter C.S., Randerson J.T., Field C.B., Matson P.A., Vitousek P.M., Mooney H.A., Klooster S.A. Terrestrial ecosystem production: A process model based on global satellite and surface data. Glob. Biogeochem. Cycles. 1993; 7: 811-841. 10.1029/93GB02725</bibtext> </blist> <blist> <bibtext> Chen Y., Xu Y., Chen T., Zhang F., Zhu S. Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020. Remote Sens. 2024; 1660. 10.3390/rs16010060</bibtext> </blist> <blist> <bibtext> Gonsamo A., Chen J.M., Price D.T., Kurz W.A., Liu J., Boisvenue C., Hember R.A., Wu C., Chang K.-H. Improved assessment of gross and net primary productivity of Canada's landmass. J. Geophys. Res. Biogeosci. 2013; 118: 1546-1560. 10.1002/2013JG002388</bibtext> </blist> <blist> <bibtext> Yu G., Zheng Z., Wang Q., Fu Y., Zhuang J., Sun X., Wang Y. Spatiotemporal Pattern of Soil Respiration of Terrestrial Ecosystems in China: The Development of a Geostatistical Model and Its Simulation. Environ. Sci. Technol. 2010; 44: 6074-6080. 10.1021/es100979s</bibtext> </blist> <blist> <bibtext> Wu C., Peng J., Ciais P., Peñuelas J., Wang H., Beguería S., Andrew Black T., Jassal R.S., Zhang X., Yuan W. Increased drought effects on the phenology of autumn leaf senescence. Nat. Clim. Chang. 2022; 12: 943-949. 10.1038/s41558-022-01464-9</bibtext> </blist> <blist> <bibtext> Mann H.B. Nonparametric Tests Against Trend. Econometrica. 1945; 13: 245-259. 10.2307/1907187</bibtext> </blist> <blist> <bibtext> Kendall M.G. Rank Correlation Methods; Griffin: Oxford, UK. 1948</bibtext> </blist> <blist> <bibtext> Fu Y., Li X., Zhou X., Geng X., Guo Y., Zhang Y. Progress in plant phenology modeling under global climate change. Sci. China Earth Sci. 2020; 63: 1237-1247. 10.1007/s11430-019-9622-2</bibtext> </blist> <blist> <bibtext> Piao S., Liu Q., Chen A., Janssens I.A., Fu Y., Dai J., Liu L., Lian X., Shen M., Zhu X. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol. 2019; 25: 1922-1940. 10.1111/gcb.14619. 30884039</bibtext> </blist> <blist> <bibtext> Menzel A., Fabian P. Growing season extended in Europe. Nature. 1999; 397: 659. 10.1038/17709</bibtext> </blist> <blist> <bibtext> Shi Y., Yang C., Zhu J., Chang J., Zhao X., Sun W., Huang M., Yu Y., Guo K., Gu F. Estimation of national and provincial carbon emissions, terrestrial carbon sinks and their relative contribution to emission reductions during 1980~2020. Sci. Sin. Vitae. 2024; 54: 2459-2478. 10.1360/SSV-2023-0214</bibtext> </blist> <blist> <bibtext> Pan Y., Birdsey R.A., Fang J., Houghton R., Kauppi P.E., Kurz W.A., Phillips O.L., Shvidenko A., Lewis S.L., Canadell J.G. A Large and Persistent Carbon Sink in the World's Forests. Science. 2011; 333: 988-993. 10.1126/science.1201609</bibtext> </blist> <blist> <bibtext> Lal R. Soil Carbon Sequestration Impacts on Global Climate Change and Food Security. Science. 2004; 304: 1623-1627. 10.1126/science.1097396</bibtext> </blist> <blist> <bibtext> Schlesinger W.H., Andrews J.A. Soil respiration and the global carbon cycle. Biogeochemistry. 2000; 48: 7-20. 10.1023/A:1006247623877</bibtext> </blist> <blist> <bibtext> Fang J., Kato T., Guo Z., Yang Y., Hu H., Shen H., Zhao X., Kishimoto-Mo A.W., Tang Y., Houghton R.A. Evidence for environmentally enhanced forest growth. Proc. Natl. Acad. Sci. USA. 2014; 111: 9527-9532. 10.1073/pnas.1402333111</bibtext> </blist> <blist> <bibtext> Liu X., Zhang Y., Han W., Tang A., Shen J., Cui Z., Vitousek P., Erisman J.W., Goulding K., Christie P. Enhanced nitrogen deposition over China. Nature. 2013; 494: 459-462. 10.1038/nature11917</bibtext> </blist> <blist> <bibtext> Piao S., Yin G., Tan J., Cheng L., Huang M., Li Y., Liu R., Mao J., Myneni R.B., Peng S. Detection and attribution of vegetation greening trend in China over the last 30 years. Glob. Change Biol. 2015; 21: 1601-1609. 10.1111/gcb.12795</bibtext> </blist> <blist> <bibtext> Lu F., Hu H., Sun W., Zhu J., Liu G., Zhou W., Zhang Q., Shi P., Liu X., Wu X. Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010. Proc. Natl. Acad. Sci. USA. 2018; 115: 4039-4044. 10.1073/pnas.1700294115</bibtext> </blist> <blist> <bibtext> Yang C., Shi Y., Sun W., Zhu J., Ji C., Feng Y., Ma S., Guo Z., Fang J. Updated estimation of forest biomass carbon pools in China, 1977–2018. Biogeosciences. 2022; 19: 2989-2999. 10.5194/bg-19-2989-2022</bibtext> </blist> <blist> <bibtext> Leng Y., Li W., Ciais P., Sun M., Zhu L., Yue C., Chang J., Yao Y., Zhang Y., Zhou J. Forest aging limits future carbon sink in China. One Earth. 2024; 7: 822-834. 10.1016/j.oneear.2024.04.011</bibtext> </blist> <blist> <bibtext> Keskitalo J., Bergquist G., Gardeström P., Jansson S. A Cellular Timetable of Autumn Senescence. Plant Physiol. 2005; 139: 1635-1648. 10.1104/pp.105.066845. 16299183</bibtext> </blist> </ref> <aug> <p>By Shuping Ji; Shilong Ren; Lei Fang; Jinyue Chen; Guoqiang Wang and Qiao Wang</p> <p>Reported by Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib10" firstref="ref12"></nolink> <nolink nlid="nl2" bibid="bib12" firstref="ref13"></nolink> <nolink nlid="nl3" bibid="bib13" firstref="ref16"></nolink> <nolink nlid="nl4" bibid="bib15" firstref="ref18"></nolink> <nolink nlid="nl5" bibid="bib11" firstref="ref21"></nolink> <nolink nlid="nl6" bibid="bib16" firstref="ref25"></nolink> <nolink nlid="nl7" bibid="bib17" firstref="ref26"></nolink> <nolink nlid="nl8" bibid="bib18" firstref="ref27"></nolink> <nolink nlid="nl9" bibid="bib20" firstref="ref28"></nolink> <nolink nlid="nl10" bibid="bib22" firstref="ref32"></nolink> <nolink nlid="nl11" bibid="bib23" firstref="ref39"></nolink> <nolink nlid="nl12" bibid="bib24" firstref="ref46"></nolink> <nolink nlid="nl13" bibid="bib25" firstref="ref51"></nolink> <nolink nlid="nl14" bibid="bib26" firstref="ref53"></nolink> <nolink nlid="nl15" bibid="bib27" firstref="ref57"></nolink> <nolink nlid="nl16" bibid="bib28" firstref="ref64"></nolink> <nolink nlid="nl17" bibid="bib29" firstref="ref66"></nolink> <nolink nlid="nl18" bibid="bib31" firstref="ref67"></nolink> <nolink nlid="nl19" bibid="bib33" firstref="ref69"></nolink> <nolink nlid="nl20" bibid="bib34" firstref="ref77"></nolink> <nolink nlid="nl21" bibid="bib35" firstref="ref81"></nolink> <nolink nlid="nl22" bibid="bib36" firstref="ref82"></nolink> <nolink nlid="nl23" bibid="bib38" firstref="ref84"></nolink> <nolink nlid="nl24" bibid="bib39" firstref="ref86"></nolink> <nolink nlid="nl25" bibid="bib40" firstref="ref87"></nolink> <nolink nlid="nl26" bibid="bib41" firstref="ref88"></nolink> <nolink nlid="nl27" bibid="bib43" firstref="ref90"></nolink> <nolink nlid="nl28" bibid="bib44" firstref="ref91"></nolink>
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Items – Name: Title
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  Data: Improved CASA-Based Net Ecosystem Productivity Estimation in China by Incorporating Developmental Factors into Autumn Phenology Model
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Shuping+Ji%22">Shuping Ji</searchLink><br /><searchLink fieldCode="AR" term="%22Shilong+Ren%22">Shilong Ren</searchLink><br /><searchLink fieldCode="AR" term="%22Lei+Fang%22">Lei Fang</searchLink><br /><searchLink fieldCode="AR" term="%22Jinyue+Chen%22">Jinyue Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Guoqiang+Wang%22">Guoqiang Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Qiao+Wang%22">Qiao Wang</searchLink>
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  Label: Source
  Group: Src
  Data: Remote Sensing, Vol 17, Iss 3, p 487 (2025)
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  Label: Publisher Information
  Group: PubInfo
  Data: MDPI AG, 2025.
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  Data: 2025
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  Data: LCC:Science
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22autumn+phenology%22">autumn phenology</searchLink><br /><searchLink fieldCode="DE" term="%22carbon+sink%22">carbon sink</searchLink><br /><searchLink fieldCode="DE" term="%22light+use+efficiency%22">light use efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22net+ecosystem+productivity%22">net ecosystem productivity</searchLink><br /><searchLink fieldCode="DE" term="%22phenology+model%22">phenology model</searchLink><br /><searchLink fieldCode="DE" term="%22Science%22">Science</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Accurately assessing the carbon sink intensity of China’s ecosystem is crucial for achieving carbon neutrality. However, existing ecosystem process models have significant uncertainties in net ecosystem productivity (NEP) estimates due to the lack of or insufficient description of phenological regulation. Although plant developmental factors have been proven to significantly influence autumn phenology, they have not been systematically incorporated into autumn phenology models. In this study, we modified the autumn phenology model (cold-degree-day, CDD) by incorporating the growing-season gross primary productivity (GPP) and the start of growing season (SOS) and used it as a constraint to improve the CASA model for quantifying NEP across China from 2003 to 2021. Validation results showed that the CDD model incorporating developmental factors significantly improved the simulation accuracy at the end of the growing season (EOS). More importantly, compared with flux tower observations, the NEP derived from the improved CASA model based on the above phenology model showed a 15.34% reduction in root mean square error and a 74% increase in the coefficient of determination relative to the original model. During the study period, China’s multiyear average total NEP was 489.67 ± 38.27 Tg C/yr, with the highest found in evergreen broadleaf forests and the lowest detected in shrublands. Temporally, China’s NEP demonstrated an overall increasing trend with an average rate of 1.75 g C/m2/yr2. However, the growth rate of NEP remained far below concurrent carbon emissions from fossil fuel combustion totally, especially for eastern China, while the northeastern regions performed relatively better. The improved regional carbon flux estimation framework proposed in this study will provide important support for developing future climate change mitigation strategies.
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  Data: English
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  Data: 2072-4292
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  Data: https://www.mdpi.com/2072-4292/17/3/487; https://doaj.org/toc/2072-4292
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3390/rs17030487
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs17030487
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: 487
    Subjects:
      – SubjectFull: autumn phenology
        Type: general
      – SubjectFull: carbon sink
        Type: general
      – SubjectFull: light use efficiency
        Type: general
      – SubjectFull: net ecosystem productivity
        Type: general
      – SubjectFull: phenology model
        Type: general
      – SubjectFull: Science
        Type: general
    Titles:
      – TitleFull: Improved CASA-Based Net Ecosystem Productivity Estimation in China by Incorporating Developmental Factors into Autumn Phenology Model
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      – PersonEntity:
          Name:
            NameFull: Shuping Ji
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          Name:
            NameFull: Shilong Ren
      – PersonEntity:
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            NameFull: Lei Fang
      – PersonEntity:
          Name:
            NameFull: Jinyue Chen
      – PersonEntity:
          Name:
            NameFull: Guoqiang Wang
      – PersonEntity:
          Name:
            NameFull: Qiao Wang
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
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              Value: 17
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            – TitleFull: Remote Sensing
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