Agricultural and Forest Meteorology最新文献

{"title":"Rice yield stability and its determinants across different rice-cropping systems in China","authors":"Siyuan Wang ,&nbsp;Yijiang Liu ,&nbsp;Senthold Asseng ,&nbsp;Matthew Tom Harrison ,&nbsp;Liang Tang ,&nbsp;Bing Liu ,&nbsp;Ke Liu ,&nbsp;Zhongkui Luo ,&nbsp;Enli Wang ,&nbsp;Jinfeng Chang ,&nbsp;Xiaolei Qiu ,&nbsp;Leilei Liu ,&nbsp;Xiaohu Zhang ,&nbsp;Weixing Cao ,&nbsp;Yan Zhu ,&nbsp;Liujun Xiao","doi":"10.1016/j.agrformet.2025.110452","DOIUrl":"10.1016/j.agrformet.2025.110452","url":null,"abstract":"<div><div>Rice production faces increasing challenges from climate change and soil degradation. The conversion from double to single-cropping rice over the past decades has further threatened rice self-sufficiency in China. Understanding the spatial and temporal variations of rice yield across different rice-cropping systems is crucial for creating adaptation strategies. Here we used a process-based modelling approach combined with a nationwide field dataset from 1981 to 2020 to evaluate rice yield gaps and temporal yield variabilities for single and double rice-cropping systems, and further assessed their underlying determinants in China. We showed that single rice had the largest yield gap and the greatest temporal variability in yield, followed by late rice and early rice. The coefficient of variation (CV) for actual yield ranged from 6 % to 64 %, 4 % to 36 %, and 5 % to 28 % for single rice, late rice, and early rice, respectively. Regions with unstable yields were primarily located in southwestern (for single rice) and southern China (for late rice), and determinants of yield stability varied across subregions. Overall, the combined effects of climate and soil factors generally reduce yield stability. Improved management, such as appropriate sowing dates, precise fertilization, and cultivars with favorable traits, significantly enhanced the stability. Socio-economic factors including sufficient labor and advanced agricultural mechanization also contributed to closing yield gaps and stabilizing yield. This study provides spatial insights for developing region-specific strategies to ensure a sufficient and stable rice supply.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110452"},"PeriodicalIF":5.6,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust filling of extra-long gaps in eddy covariance CO2 flux measurements from a temperate deciduous forest using eXtreme Gradient Boosting","authors":"Yujie Liu ,&nbsp;Benjamin Lucas ,&nbsp;Darby D. Bergl ,&nbsp;Andrew D. Richardson","doi":"10.1016/j.agrformet.2025.110438","DOIUrl":"10.1016/j.agrformet.2025.110438","url":null,"abstract":"<div><div>Eddy Covariance measurements are often subject to missing values, or gaps in the data record. Methods to fill short gaps are well-established, but robustly filling gaps longer than a few weeks remains a challenge. Marginal Distribution Sampling (MDS) is a standard gap-filling method, but its effectiveness for long gaps (&gt; 30 days) is limited. We compared the performance of a machine learning algorithm, eXtreme Gradient Boosting (XGB) against MDS, using various artificial scenarios of gap lengths and locations. We gapfilled half hourly CO₂ flux from a temperate deciduous forest, Bartlett Experimental Forest, from 2010 to 2022. Whereas the standard implementation of MDS uses a narrowly-prescribed set of predictor variables, with XGB we were able to include additional variables. The Green Chromatic Coordinate (GCC), derived from PhenoCam imagery, and diffuse photosynthetic photon flux density, emerged as two of the three most important predictor variables. Compared to MDS, the root mean square error (RMSE) of XGB decreased by 9.5 %, and the R² increased by 2.7 % in a randomized 10-fold cross validation test. XGB outperformed MDS for both day and night times across different seasons. But annual NEE integrals varied across methods, with weaker annual net carbon uptake, by -110 ± 74 g C m^-2 y^-1 for XGB compared to MDS (214 ± 11 g C m^-2 yr^-1). In artificial gap experiments, when trained using the 13-year data record, XGB reliably filled gaps, showing little change in RMSE for gaps up to 240 days. In contrast, the performance of MDS steadily decreased as gap lengths increased. MDS was unable to fill gaps longer than 2 months. In summary, XGB demonstrates excellent performance as an alternative method to MDS, providing reliable predictions for temperate deciduous forest carbon fluxes under different gap lengths and location scenarios. Implementation of XGB is facilitated by easy-to-use packages.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110438"},"PeriodicalIF":5.6,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the effects of aerosols and cloud radiative effect on rice growth and yield","authors":"Yuting Liu ,&nbsp;Lunche Wang ,&nbsp;Xinxin Chen ,&nbsp;Zigeng Niu ,&nbsp;Ming Zhang ,&nbsp;Jia Sun ,&nbsp;Junfang Zhao","doi":"10.1016/j.agrformet.2025.110453","DOIUrl":"10.1016/j.agrformet.2025.110453","url":null,"abstract":"<div><div>Radiative Effect (RE) caused by aerosols and clouds significantly impacts crop growth by altering both the spectral distribution and the amount of diffuse light reaching the crop canopy. Traditional crop models often fail to account for these variations in photosynthetically active radiation (PAR), leading to biases in crop growth simulations. To address this, we modified the ORYZA2000 crop model to improve the accuracy of radiation assessment. Using the RTM LibRadtran, we evaluated the effects of aerosol and cloud RE on rice growth and yield, utilizing data from Jiangxi Province, China (2011–2016). The results indicated that aerosols scatter PAR more effectively, with a scatter intensity 1.42 times greater than that of Shortwave Radiation (SW). Clouds increased the ratio of PAR in SW (FPAR) from 0.437 ± 0.01 to 0.448 ± 0.01. Ignoring PAR assessment in the crop model led to a 25.55 % overestimation of rice yield. When accounting for the diffuse fraction of PAR (PARDF) and FPAR, aerosol direct radiative effect (ADRE) increased rice yield by 18.58 %, cloud radiative effect (CRE) decreased yields by 16.13 %, and combined aerosol and cloud radiative effect (ACRE) resulted in a 31.71 % decrease in yields. Although aerosols alone increased yield by enhancing diffuse PAR, the combined effect of clouds and aerosols resulted in a lower overall PAR and caused a greater reduction in yield than clouds alone. Early rice exhibited more sensitivity to RE, allocating more biomass to the panicle in the early stages, while late rice increased leaf biomass during late stages under RE. This study underscores the importance of accurate PAR estimation in crop modeling and highlights the need to integrate the diverse impacts of RE into future crop yield predictions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110453"},"PeriodicalIF":5.6,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building spring development indices for woody species in the conterminous United States","authors":"Joshua J. Hatzis ,&nbsp;Mark D. Schwartz ,&nbsp;Toby R. Ault ,&nbsp;Alison Donnelly ,&nbsp;Amanda Gallinat ,&nbsp;Xiaolu Li ,&nbsp;Theresa M. Crimmins","doi":"10.1016/j.agrformet.2025.110443","DOIUrl":"10.1016/j.agrformet.2025.110443","url":null,"abstract":"<div><div>Phenological indices are an effective approach for assessing spatial and temporal patterns and variability in plant development. The Spring Indices (SI-x), two widely adopted phenological indices, have been used in recent decades to predict development of woody plants, and document changes in spring growth timing, especially in North America. However, these two indices (Leaf and Bloom) capture only two “moments” in the continuum of spring when quantities of thermal or photo/thermal energy, associated with seasonal events in plants, are accumulated, limiting their utility to characterize the remainder of the spring season. Further, the Spring Indices do not account for intraspecific variation, limiting their ability to reflect non-cloned plant development. To address these shortcomings, we developed a novel suite of phenological indices that encompass a broader span of the spring season. These indices were constructed using observations contributed to the USA National Phenology Network's <em>Nature's Notebook</em> platform across many non-cloned tree and shrub species’ ranges, thereby incorporating differing regional responses within species due to genetic variations.</div><div>Individual species model predictions of leaf or bloom timing exhibited an average mean absolute error of 8.55 days; most were improved by the inclusion of site-specific latitude, elevation, or 30-year average temperature. Leaf and bloom model outputs for individual species across the spring season were temporally aggregated into four leaf and bloom groups to produce a suite of Spring Development Indices (SDI). Accuracy of the SDI predictions was 0.89 days lower, on average, than the species models, but 2.65 days better than SI-x. Generally, all SDIs were highly correlated. The SDIs exhibiting the most difference from the others were Early leaf, Very Early bloom, and Late bloom. As such, these SDIs provide novel insights, beyond SI-x, into the relative timing of spring-season “moments” across species in space and time.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110443"},"PeriodicalIF":5.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vapor pressure deficit dominates dryness stress on forest biomass carbon in China under global warming","authors":"Yunfeng Cen ,&nbsp;Mei Tang ,&nbsp;Qingyuan Wang ,&nbsp;Guanfang Sun ,&nbsp;Zhiming Han ,&nbsp;Yonghong Li ,&nbsp;Zhaoliang Gao","doi":"10.1016/j.agrformet.2025.110440","DOIUrl":"10.1016/j.agrformet.2025.110440","url":null,"abstract":"<div><div>Soil moisture (SM) and vapor pressure deficit (VPD) are key factors affecting forest carbon stock. However, their effects on forest biomass carbon under hotter and drier climate trends are unclear. These knowledge gaps limit forest management practices and the implementation of climate change mitigation programs. In this study, satellite observations and meteorological data were combined to analyze the asymmetric response of forest biomass carbon to wet and dry changes in China from 2002 to 2020 and identify the relative contributions and influence pathways of SM and VPD on forest biomass carbon under global warming. The results showed that drought did not lead to a decrease in forest biomass carbon but slowed its rate of increase. Excluding the interaction effects of SM and VPD with temperature (Tmp), the dominant effects of SM and VPD on forest biomass carbon differed between dry and wet regions, but the effects of VPD on forest biomass carbon were broader and larger. Notably, the interaction of Tmp and VPD not only amplifies the positive effects of warming on humid regions but also amplifies the negative effects of warming on semi-arid regions, and to some extent offsets the positive effects of warming on sub-humid regions. Additionally, in warming environments, VPD exerts the greatest stress on forest biomass carbon in areas where precipitation (Pre) is 400–700 mm yr⁻¹ and potential evapotranspiration (Pet) is 650–900 mm yr⁻¹. Our results reconcile the contradictions regarding the relative importance of SM and VPD on forest carbon storage and the direction of the influence of VPD on forest carbon sequestration, thereby enhancing our understanding of forest ecosystem carbon cycling in response to climate change.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110440"},"PeriodicalIF":5.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lightweight SIF-based crop yield estimation model: A case study of Australian wheat","authors":"Jinru Xue ,&nbsp;Alfredo Huete ,&nbsp;Zhunqiao Liu ,&nbsp;Sicong Gao ,&nbsp;Xiaoliang Lu","doi":"10.1016/j.agrformet.2025.110439","DOIUrl":"10.1016/j.agrformet.2025.110439","url":null,"abstract":"<div><div>As Australia's primary staple and export crop, wheat necessitates reliable yield mapping to ensure timely alerts about food insecurity. Conventional crop yields are estimated using either process-based or statistical models, but both face challenges in large-scale application due to the extensive data required. Recent studies have shown that the gross primary production (GPP) of plants can be mechanistically estimated from the fraction of open PSII reaction centers (<em>q</em>_L), solar-induced chlorophyll fluorescence (SIF), and readily accessible meteorological datasets including air temperature (<em>T</em>_air), dew-point temperature, and soil water content. <em>q</em>_L can be modeled as a function of SIF and <em>T</em>_air. Along with these theoretical advances, the resolution of satellite SIF has greatly improved, boosting the potential for accurate large-scale crop yield estimation. In this study, we develop a SIF-based lightweight crop model which uses <em>q</em>_L and SIF to track crop GPP. This approach allows for a direct mechanistic estimation of GPP without the need to explicitly account for numerous complex agro-climatic processes. We apply this model to estimate Australian wheat yields from 2019 to 2022. The model exhibits strong predictive power, explaining 86 % of wheat production variance at the regional level (RMSE: 91 kilotons, rRMSE: 7.24 %) and 91 % at the state level (RMSE: 1509 kilotons, rRMSE: 14.13 %). Australian wheat yields exhibit a positive correlation with soil water content and vapor pressure deficit (VPD) when VPD remains below 0.80 kPa. However, the correlation turns negative once VPD exceeds this threshold. We also identify the main sources of error in estimating wheat production as: (1) inaccuracies in estimating the harvested area of wheat, and (2) the relatively low spatial resolution of current satellite SIF data. Our model, with its lightweight design and its ability to mechanistically estimate crop photosynthetic CO₂ assimilation, offers a promising, novel framework for practical, large-scale crop yield mapping.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110439"},"PeriodicalIF":5.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drying-rewetting cycles decrease temperature sensitivity of soil organic matter decomposition","authors":"Junjie Lin ,&nbsp;Wenling Zhang ,&nbsp;Amit Kumar ,&nbsp;Dafeng Hui ,&nbsp;Changai Zhang ,&nbsp;Shengdao Shan ,&nbsp;Zhiguo Yu ,&nbsp;Biao Zhu ,&nbsp;Yakov Kuzyakov","doi":"10.1016/j.agrformet.2025.110442","DOIUrl":"10.1016/j.agrformet.2025.110442","url":null,"abstract":"<div><div>Soil organic carbon (SOC) decomposition is crucial in the global carbon cycle. Its sensitivity to warming significantly impacts climate change. However, the effect of soil drying-rewetting, a consequence of climate change-induced water cycling shifts, on SOC decomposition sensitivity remains poorly understood. This study investigated how drying-rewetting cycles affect the temperature sensitivity (Q₁₀) of SOC decomposition and its underlying mechanisms. We collected soils from two farmlands with 23- and 33-year C₃<img>C₄ vegetation switches The soils were incubated at 20 °C or 30 °C for 180 days under alternate drying-rewetting cycles (100 %−20 % water holding capacity, WHC) or constant moisture (60 % WHC). Using ¹³C natural abundance, we differentiated CO₂ sources from recent SOC (C₄, &lt;23 or &lt;33 years) and old SOC (C₃, &gt;23 or &gt;33 years). Results showed that warming and drying-rewetting enhanced total SOC decomposition. Across moisture conditions, the Q₁₀ of old SOC was 0.25−0.40 units higher than that of recent SOC. Six drying-rewetting cycles decreased the Q₁₀ of total, recent, and old SOC by 0.30−0.44 units compared to constant moisture, as warming became less dominant during the drying-rewetting process. This indicates that the commonly used Q₁₀ might be overestimated under constant moisture, suggesting that the feedback of SOC pools to climate warming might be weaker than previously expected under real soil moisture fluctuations.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110442"},"PeriodicalIF":5.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Snow depth and spring temperature dominate the spring phenological shifts and control growing season dynamics on the Tibetan Plateau","authors":"Qianqian Ma ,&nbsp;Ji Liu ,&nbsp;Xiangyi Li ,&nbsp;Yanyan Li ,&nbsp;Fanjiang Zeng ,&nbsp;Xiaowei Guo ,&nbsp;Maierdang Keyimu","doi":"10.1016/j.agrformet.2025.110435","DOIUrl":"10.1016/j.agrformet.2025.110435","url":null,"abstract":"<div><div>To explore the spatio-temporal variability of vegetation phenology and its drivers under rapid climate change on the Tibetan Plateau (TP) over the past four decades, a monthly normalized vegetation index (NDVI) dataset was constructed for the TP from 1982 to 2020 using pixel-level univariate linear regression models based on GIMMS NDVI and MODIS NDVI. The extended NDVI dataset passed a consistency check (R² = 0.99, <em>P</em> &lt; 0.001). From here, the optimal thresholds for retrieving vegetation phenology were determined based on phenological observation data. Spatial differences among the pathways of influence of how climate change affected vegetation phenology were analyzed using lagging correlation analysis and structural equation modeling. Based on the extended dataset, the optimal thresholds for the start of the growing season (SOS) and the end of growing season (EOS) were 0.30 and 0.80, respectively. The SOS had a three-month lag in response to snow depth and a one-month lag in response to temperature. The variation in SOS was mainly influenced by a negative effect of snow depth in the central-western TP and a negative effect of spring temperatures in the south-eastern TP, while the variation in EOS was mainly influenced by a positive effect of fall temperature in the central-western TP and a positive effect of SOS in the south-eastern TP. Additionally, phenological changes displayed altitude dependence in response to climate change, with the reduction in snow depth delaying the SOS more at higher altitudes than at lower altitudes. This can be attributed to elevation-dependent warming, where snow depth is reduced more quickly at higher altitudes. Thus, alpine ecosystems at higher elevations on the TP may be particularly sensitive to snow cover changes under future warming scenarios.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110435"},"PeriodicalIF":5.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of spatial and temporal variability in Sentinel-2 surface reflectance on a rice paddy landscape","authors":"Wonseok Choi ,&nbsp;Youngryel Ryu ,&nbsp;Juwon Kong ,&nbsp;Sungchan Jeong ,&nbsp;Kyungdo Lee","doi":"10.1016/j.agrformet.2025.110401","DOIUrl":"10.1016/j.agrformet.2025.110401","url":null,"abstract":"<div><div>High spatial resolution spaceborne remote sensing systems provide a new data source for agricultural applications. As a key deliverable, surface reflectance (SR) enables immediate and non-destructive estimation of crop status, thus the demand for reliable pixelwise SR is increasing. However, the evaluations are typically conducted on pseudo-invariant areas and the reliability of pixelwise SR has not been thoroughly examined over heterogenous, dynamic surfaces. In this study, we evaluated pixelwise Sentinel-2 (S2) SR on a rice paddy landscape across seasons using drone-based hyperspectral images and tower-based continuous hyperspectral observations as the ground references. We also examined the impact of spatial and atmospheric properties on S2 SR. Overall, S2 SR showed strong linear relationships with the ground references (the overall R² &gt; 0.8). The residual errors were influenced by sub-pixel geolocation errors (0.01–0.017 (2.1–11.8 %)), a widen PSF (0.007 (7.6 %) for red) and underestimated AOT retrievals (0.027 (40.7 %) for blue). Notably, atmospheric adjacency effects broadened the PSF, causing the consistency of S2 with the ground reference image to depend on the landscape's heterogeneity. Our findings outlined the key factors contributing to uncertainties in S2 SR, which could affect downstream products like vegetation indices and leaf area index. Considering these factors would enhance remote sensing of landscapes with high contrast in reflectance and elevated aerosol loadings, such as urban, savanna, wetland and dry agricultural land.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110401"},"PeriodicalIF":5.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data assimilation of forest status using Sentinel-2 data and a process-based model","authors":"Francesco Minunno ,&nbsp;Jukka Miettinen ,&nbsp;Xianglin Tian ,&nbsp;Tuomas Häme ,&nbsp;Jonathan Holder ,&nbsp;Kristiina Koivu ,&nbsp;Annikki Mäkelä","doi":"10.1016/j.agrformet.2025.110436","DOIUrl":"10.1016/j.agrformet.2025.110436","url":null,"abstract":"<div><div>Spatially explicit information of forest status is important for obtaining more accurate predictions of C balance. Spatially explicit predictions of forest characteristics at high resolution can be obtained by Earth Observations (EO), but the accuracy of satellite-based predictions may vary significantly. Modern computational techniques, such as data assimilation (DA), allow us to improve the accuracy of predictions considering measurement uncertainties. The main objective of this work was to develop two DA frameworks that combine repeated satellite measurements (Sentinel-2) and process-based forest model predictions. For the study three tiles of 100 × 100 km² were considered, in boreal forests. One framework was used to predict forest structural variables and tree species, while the other was used to quantify the site fertility class. The reliability of the frameworks was tested using field measurements. By means of DA we combined model and satellite-based predictions improving the reliability and robustness of forest monitoring. The DA frameworks reduced the uncertainty associated with forest structural variables and mitigated the effects of biased Earth Observation predictions when errors occurred. For one tile, Sentinel-2 prediction for 2019 (s2019) of stem diameter (D) and height (H) was biased, but the errors were reduced by the DA estimation (DA2019). The root mean squared errors were reduced from 5.8 cm (s2019) to 4.5 cm (DA2019) and from 5.1 m (s2019) to 3.3 m (DA2019) for D (sd = 4.33 cm) and H (sd = 3.43 m), respectively. For the site fertility class estimation DA was less effective, because forest growth rate is low in boreal environments; long term analysis might be more informative. We showed here the potential of the DA framework implemented using medium resolution remote sensing data and a process-based forest model. Further testing of the frameworks using more RS-data acquisitions is desirable and the DA process would benefit if the error of satellite-based predictions were reduced.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110436"},"PeriodicalIF":5.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}