Layered leaf area index （LAIk） is one of the major determinants for rice canopy. The objective of this study is to attain rice LAI k using morphological traits especially leaf traits that affected plant type. A theo...Layered leaf area index （LAIk） is one of the major determinants for rice canopy. The objective of this study is to attain rice LAI k using morphological traits especially leaf traits that affected plant type. A theoretical model based on rice geometrical structure was established to describe LAI k of rice with leaf length （Li）, width （Wi）, angle （Ai）, and space （Si）, and plant pole height （H） at booting and heading stages. In correlation with traditional manual measurement, the model was performed by high R2-values （0.95-0.89, n=24） for four rice hybrids （Liangyoupeijiu, Liangyou E32, Liangyou Y06, and Shanyou 63） with various plant types and four densities （3 750, 2 812, 1 875, and 1 125 plants per 100 m2） of a particular hybrid （Liangyoupeijiu）. The analysis of leaf length, width, angle, and space on LAI k for two hybrids （Liangyoupeijiu and Shanyou 63） showed that leaves length and space exhibited greater effects on the change of rice LAI k . The radiation intensity showed a significantly negative exponential relation to the accumulation of LAI k , which agreed to the coefficient of light extinction （K）. Our results suggest that plant type regulates radiation distribution through changing LAI k . The present model would be helpful to acquire leaf distribution and judge canopy structure of rice field by computer system after a simple and less-invasive measurement of leaf length, width, angle （by photo）, and space at field with non-dilapidation of plants.展开更多
Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predic...Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.展开更多
This study was aimed at establishing allometric models for estimating LA (Leaf Area) of eight Coffea arabica genotypes in Mana district of Jimma Zone Oromia Regional State, South Western Ethiopia (7°46'N, ...This study was aimed at establishing allometric models for estimating LA (Leaf Area) of eight Coffea arabica genotypes in Mana district of Jimma Zone Oromia Regional State, South Western Ethiopia (7°46'N, 36°0'E). Many Methodologies and instruments have been devised to facilitate measurement of leaf area. However, these methods are destructive, laborious and expensive. For modeling leaf area, leaf width, leaf length and leaf area of 1200 leaves (50 leaves for each genotype) was measured for model calibration and the respective measurements on 960 leaves were used for model validation. Linear measurement was taken from leaves and branch diameters of eight genotypes of C. arabica, cultivated in field following a randomized complete blocks design at three altitudes (High, Medium and Low) were evaluated to identify best option for input in the models, and to validate the method to estimate the leaf area. Linear and non-linear models were tested for their accuracy to predict leaf area of the eight C. arabica genotypes. The use of linear model resulted in high accuracy for all of the eight C. arabica genotypes. No significant effect of growing altitude and genotype was obtained among the slopes of the models. Therefore, one single model was fitted to the combined data of all genotypes at all altitudes (LA = 0.6434LW). Comparison between observed and predicted leaf area was made using this model in another independent dataset, conducted for model validation, exhibited a high degree of correlation (r = 0.98 - 0.99, P < 0. 01). The over or under estimation of the leaf area using this model ranges between 0.02% to 1.7% and this model is adequate to estimate the leaf area for the eight C. arabica genotypes. Hence, this model can be proposed to be reliably used and with this developed model, researchers can estimate the leaf area of newly released eight genotypes of C. arabica at different altitudes accurately.展开更多
To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） v...To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.展开更多
The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were ...The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of TM3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.展开更多
Logistic and exponential approaches have been used to simulate plant growth and leaf area index (LAI) in different growing conditions. The objective of the present study was to develop and evaluate an approach to simu...Logistic and exponential approaches have been used to simulate plant growth and leaf area index (LAI) in different growing conditions. The objective of the present study was to develop and evaluate an approach to simulate maize LAI that expresses key physiological and phonological processes using a minimum entry requirement for Quality Protein maize (QPM) varieties grown in the southwestern region of the DR-Congo. Data for the development and testing of the model were collected manually in experimental plots using a non-destructive method. Simulation results revealed measurable variations between crop seasons (long season A and short season B) and between the two varieties (Mudishi-1 and Mudishi-3) for height, number of visible leaves, and LAI. For both seasons, Mudishi-3, a short stature variety was associated with expected stable yield based on simulation data. In general, the model simulated reliably all the parameters including the LAI. The LAI value for mudishi-1 was higher than that of Mudishi-3. There were significant differences among the model parameters (K, Ti, a, b, Tf) and between the two varieties. In all crop conditions studied and for the two varieties, the senescence rate (a) was higher, while the growth rate (b) was lower compared to the estimates based on the STICS model.展开更多
叶面积指数(leaf area index,LAI)是反映植物冠层结构和光能利用的重要指标.随着遥感技术的不断发展,利用遥感数据获取大面积LAI已经成为监测作物生长和估产的重要手段.基于物理模型的LAI遥感反演方法经常假设作物冠层结构是均匀分布,然...叶面积指数(leaf area index,LAI)是反映植物冠层结构和光能利用的重要指标.随着遥感技术的不断发展,利用遥感数据获取大面积LAI已经成为监测作物生长和估产的重要手段.基于物理模型的LAI遥感反演方法经常假设作物冠层结构是均匀分布,然而,作为典型的垄行结构,作物冠层被公认为是介于连续植被与离散植被之间的一种过渡形式,而简单的均匀假设必然会给反演带来偏差.本文以农作物玉米为研究对象,首先重建了玉米三维冠层结构,并定量对比分析了一维辐射传输模型PROSAIL和三维辐射传输模型LESS在玉米冠层不同生长期的反射率差异,确定了玉米冠层的非均匀分布特征是引起PROSAIL模型模拟和反演误差的主要因素;然后,考虑到玉米冠层生长过程中聚集指数的变化特征,利用LESS模型定量计算了不同生育期玉米冠层结构对应的聚集指数,建立了聚集指数和有效叶面积指数(LAI_(e))之间的关系;进而,利用该关系对基于PROSAIL模型反演得到的LAI进行修正.结果表明,修正后的LAI精度有明显提高,R^(2)从0.27提高到了0.55.该方法有望提高中高分辨率遥感数据在农作物LAI反演精度.展开更多
以尼洋河上游流域为研究对象,率定新安江-ET模型,探究气候变化和植被变化对径流变化的影响,为流域水资源规划管理提供理论依据。采用Pettitt检验和非参数Mann-Kendall检验分析年叶面积指数(leaf area index,I_(LA))系列和年径流深系列...以尼洋河上游流域为研究对象,率定新安江-ET模型,探究气候变化和植被变化对径流变化的影响,为流域水资源规划管理提供理论依据。采用Pettitt检验和非参数Mann-Kendall检验分析年叶面积指数(leaf area index,I_(LA))系列和年径流深系列的变点和趋势性特征。在此基础上,构建基于遥感I_(LA)的新安江-ET模型,定量估算气候变化影响量与植被变化影响量。结果表明:新安江-ET模型在该流域径流模拟中具有很好的适用性,率定期和验证期模拟径流与实测径流的纳什效率系数(Nash-Sutcliffe efficiency,E_(NS))分别为0.81和0.88;在气候变化影响下,径流的变化量仅为2 mm,占径流变化量的3.2%;相比之下,植被变化的影响更为显著,林地面积减少与草地面积增加使得变化期径流量与基准期相比增加了61 mm,占径流变化量的比例高达96.8%。由此可知导致尼洋河上游流域径流量变化的主要因素是流域植被覆盖的变化,为该流域的生态平衡及经济社会的可持续发展提供了理论支持。展开更多
叶面积指数(leaf area index,LAI)作为衡量作物生长状况的关键参数,对其进行精准高效的反演对于作物监测、产量预测等活动至关重要。然而,传统经验模型在估算LAI时常存在计算负荷重、泛化能力弱等问题。为实现青贮玉米多时序LAI精准、...叶面积指数(leaf area index,LAI)作为衡量作物生长状况的关键参数,对其进行精准高效的反演对于作物监测、产量预测等活动至关重要。然而,传统经验模型在估算LAI时常存在计算负荷重、泛化能力弱等问题。为实现青贮玉米多时序LAI精准、高效估算,该研究以甘肃省民乐县的大田青贮玉米LAI为研究对象,结合Landsat-8多光谱影像与同期实地采集的LAI数据,提出了4种基于EFAST全局敏感性分析方法的机器学习混合反演模型(MLP-PROSAIL、SVR-PROSAIL、RF-PROSAIL和GBM-PROSAIL)。通过对PROSAIL模型的输入参数进行敏感性分析,以便确定参数敏感度并准确模拟输出冠层反射率光谱。进一步对Landsat-8多光谱数据进行预处理和波段变换,并采用地理配准工具结合反距离加权插值的策略减少其尺度差异。同时利用贝叶斯超参数寻优和正则化技术优化模型不同的参数类型和激活函数,得到4种改进模型用于训练LAI与光谱数据,通过5折交叉验证法和留一验证法对4种模型的反演性能进行验证并选出最优模型。优化后的模型性能均有明显提升,其中,GBM-PROSAIL模型反演性能最优,拟合精度R^(2)为0.93、均方根误差(RMSE)为0.42。MLP-PROSAIL、SVR-PROSAIL和RF-PROSAIL模型的拟合精度R^(2)依次为0.85、0.88、0.90,RMSE依次为0.80、0.69、0.51。根据GBM-PROSAIL模型绘制的研究区多时序LAI反演空间分布结果表明:不同生长期青贮玉米LAI值存在明显差异,能较好反映其生长过程。该研究提出的混合反演模型具有较高的性能及较强的鲁棒性,可为多时序、大尺度作物监测、产量预测相关研究提供方法与思路。展开更多
中国东南丘陵地区茶园的快速扩张对地区碳循环产生显著影响。Biome-BGC模型常被用于碳通量定量研究,但其对人工管理过程刻画不足。本研究结合实测与遥感叶面积指数(LAI)数据,改进了Biome-BGC模型,以增强其对茶园人工管理过程的模拟能力...中国东南丘陵地区茶园的快速扩张对地区碳循环产生显著影响。Biome-BGC模型常被用于碳通量定量研究,但其对人工管理过程刻画不足。本研究结合实测与遥感叶面积指数(LAI)数据,改进了Biome-BGC模型,以增强其对茶园人工管理过程的模拟能力。结果表明:LAI是Biome-BGC模型中关键的中间变量,对LAI的准确模拟是提升模型对茶园碳通量模拟精度的关键。改进后的模型显著提升了对总初级生产力(GPP)和生态系统呼吸(RE)的模拟精度,5年平均GPP和RE值分别为1.26、1.19 kg C·m^(-2),日尺度R^(2)分别达到0.55和0.80,较改进前分别提升44.5%和降低0.9%,均方根误差(RMSE)分别为0.887和1.030 g C·m^(-2)·d^(-1),较改进前分别降低50.3%和68.4%,月尺度的模拟效果更佳,显著改善了原始模型因未充分刻画人工修剪导致的碳通量高估问题。改进后的模型能够动态刻画修剪引起的LAI波动对碳循环的影响,并验证了其在不同时间尺度下的适用性,为存在高强度人工管理的茶园生态系统碳循环定量研究提供了技术支撑。展开更多
获取作物叶面积指数(leaf area index, LAI)及其动态变化信息对作物长势监测和产量估测等具有重要意义。基于辐射传输模型的物理模型反演是LAI遥感反演最常用的方法,但该方法存在反演值不唯一的问题。此外,现有研究通常只针对单一作物类...获取作物叶面积指数(leaf area index, LAI)及其动态变化信息对作物长势监测和产量估测等具有重要意义。基于辐射传输模型的物理模型反演是LAI遥感反演最常用的方法,但该方法存在反演值不唯一的问题。此外,现有研究通常只针对单一作物类型,缺乏针对多类型作物的精度较高的LAI反演算法。该研究以玉米和水稻为主要作物的农田为例,基于PROSAIL模型模拟数据集,通过分析不同类型作物的缨帽三角-植被等值线分布模式,将植被覆盖度作为先验知识,构建用于反演多类型作物的LAI反演查找表,将其用于多时相GF-1 WFV(wide-field view)影像,反演获得整个生长季不同生长时期的LAI,并利用地面实测数据进行验证。研究结果显示:将植被覆盖度作为先验知识构建的查找表反演的LAI和实测值相关性较显著(R^(2)=0.60),均方根误差(RMSE)为0.75,反演的整个生长期LAI的变化趋势与实测LAI的变化趋势一致。而由未加入先验知识的查找表反演的LAI值和实测值的R^(2)为0.47,RMSE为0.85。该研究表明,基于缨帽三角-植被等值线分布模式,在构建涉及多类型作物的农田LAI反演的查找表中引入先验知识,能够显著提高LAI反演的精度,有效获得作物的LAI信息。展开更多
亚洲中高纬度地区是受全球变暖影响最严重的地区之一,其生态系统对气候变化高度敏感,该地区植被的未来变化存在很大不确定性。本研究使用第3版全球陆表卫星遥感数据集(Global Land Surface Satellite Product Version 3,GLOBMAP)、第3...亚洲中高纬度地区是受全球变暖影响最严重的地区之一,其生态系统对气候变化高度敏感,该地区植被的未来变化存在很大不确定性。本研究使用第3版全球陆表卫星遥感数据集(Global Land Surface Satellite Product Version 3,GLOBMAP)、第3代全球植被指数数据集(Global Inventory Modeling and Mapping Studies 3rd generation,GIMMS 3g)、全球陆表参数产品(Global Land Surface Satellite Products,GLASS)3套独立的遥感数据集中的叶面积指数(leaf area index,LAI)变量与耦合模式比较计划第5阶段(Coupled Model Intercomparison Project Phase 5,CMIP5)的15个模型、第6阶段(Coupled Model Intercomparison Project Phase 6,CMIP6)的19个模型模拟的叶面积指数及气候因子数据,基于多模型集合均值的方法对亚洲中高纬地区植被的历史及未来特征进行了系统评估。研究结果表明,CMIP6较CMIP5在模拟叶面积指数及其关键气候影响因子(包括地表气温、降水量和地表下行短波辐射)时的不确定性均有所降低。预计在中等排放情景(RCP4.5和SSP2-4.5)和高排放情景(RCP8.5和SSP5-8.5)下亚洲高纬度地区未来的LAI都将增加,且高排放情景下的增长率比中等排放情景更快。未来LAI的增加在暖季比冷季更为显著,表明植被的季节性周期和振幅都将得到增强。在LAI的高值区域,其年度均值与年际标准差增幅相较于LAI的低值区域将更加明显。展开更多
青藏高原自然资源丰富、生态系统多样,是我国重要的生态安全屏障。叶面积指数(Leaf Area Index,LAI)是表征植被冠层结构的重要参数,高效准确地获取青藏高原LAI数据对于青藏高原植被生长状况动态监测及生态环境变化等研究具有重要的意义...青藏高原自然资源丰富、生态系统多样,是我国重要的生态安全屏障。叶面积指数(Leaf Area Index,LAI)是表征植被冠层结构的重要参数,高效准确地获取青藏高原LAI数据对于青藏高原植被生长状况动态监测及生态环境变化等研究具有重要的意义。本研究以青藏高原为研究区,采用PROSAIL物理机理模型和机器学习(随机森林方法)结合的LAI反演方法,生产了1990-2023年青藏高原长时间序列30米分辨率年度最大有效LAI产品。Google Earth Engine云平台存档的近40年Landsat系列卫星历史影像为LAI产品生产提供了数据保障。数据产品质量评估结果表明,青藏高原30 m分辨率LAI产品在直接验证和交叉验证中均有较好的精度,产品质量可靠,可以为青藏高原植被资源调查、生态环境保护与恢复等研究提供数据产品支撑。展开更多
In the past several decades, dynamic global vegetation models（DGVMs） have been the most widely used and appropriate tool at the global scale to investigate vegetation-climate interactions. At the Institute of Atmosp...In the past several decades, dynamic global vegetation models（DGVMs） have been the most widely used and appropriate tool at the global scale to investigate vegetation-climate interactions. At the Institute of Atmospheric Physics, a new version of DGVM（IAP-DGVM） has been developed and coupled to the Common Land Model（CoLM） within the framework of the Chinese Academy of Sciences＇ Earth System Model（CAS-ESM）. This work reports the performance of IAP-DGVM through comparisons with that of the default DGVM of CoLM（CoLM-DGVM） and observations. With respect to CoLMDGVM, IAP-DGVM simulated fewer tropical trees, more ＂needleleaf evergreen boreal tree＂ and ＂broadleaf deciduous boreal shrub＂, and a better representation of grasses. These contributed to a more realistic vegetation distribution in IAP-DGVM,including spatial patterns, total areas, and compositions. Moreover, IAP-DGVM also produced more accurate carbon fluxes than CoLM-DGVM when compared with observational estimates. Gross primary productivity and net primary production in IAP-DGVM were in better agreement with observations than those of CoLM-DGVM, and the tropical pattern of fire carbon emissions in IAP-DGVM was much more consistent with the observation than that in CoLM-DGVM. The leaf area index simulated by IAP-DGVM was closer to the observation than that of CoLM-DGVM; however, both simulated values about twice as large as in the observation. This evaluation provides valuable information for the application of CAS-ESM, as well as for other model communities in terms of a comparative benchmark.展开更多
Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only desi...Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm （EnKF） assimilation by combing the crop growth model （CERES-Wheat） with remote sensing data, but also optimized and updated the key parameters （LAI） of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.展开更多
基金supported by the National Natural Science Foundation of China (NSFC,30871479)
文摘Layered leaf area index （LAIk） is one of the major determinants for rice canopy. The objective of this study is to attain rice LAI k using morphological traits especially leaf traits that affected plant type. A theoretical model based on rice geometrical structure was established to describe LAI k of rice with leaf length （Li）, width （Wi）, angle （Ai）, and space （Si）, and plant pole height （H） at booting and heading stages. In correlation with traditional manual measurement, the model was performed by high R2-values （0.95-0.89, n=24） for four rice hybrids （Liangyoupeijiu, Liangyou E32, Liangyou Y06, and Shanyou 63） with various plant types and four densities （3 750, 2 812, 1 875, and 1 125 plants per 100 m2） of a particular hybrid （Liangyoupeijiu）. The analysis of leaf length, width, angle, and space on LAI k for two hybrids （Liangyoupeijiu and Shanyou 63） showed that leaves length and space exhibited greater effects on the change of rice LAI k . The radiation intensity showed a significantly negative exponential relation to the accumulation of LAI k , which agreed to the coefficient of light extinction （K）. Our results suggest that plant type regulates radiation distribution through changing LAI k . The present model would be helpful to acquire leaf distribution and judge canopy structure of rice field by computer system after a simple and less-invasive measurement of leaf length, width, angle （by photo）, and space at field with non-dilapidation of plants.
基金Funding from The Scientific and Technological Research Council of Turkey(Project No:2130026)is gratefully acknowledged
文摘Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.
文摘This study was aimed at establishing allometric models for estimating LA (Leaf Area) of eight Coffea arabica genotypes in Mana district of Jimma Zone Oromia Regional State, South Western Ethiopia (7°46'N, 36°0'E). Many Methodologies and instruments have been devised to facilitate measurement of leaf area. However, these methods are destructive, laborious and expensive. For modeling leaf area, leaf width, leaf length and leaf area of 1200 leaves (50 leaves for each genotype) was measured for model calibration and the respective measurements on 960 leaves were used for model validation. Linear measurement was taken from leaves and branch diameters of eight genotypes of C. arabica, cultivated in field following a randomized complete blocks design at three altitudes (High, Medium and Low) were evaluated to identify best option for input in the models, and to validate the method to estimate the leaf area. Linear and non-linear models were tested for their accuracy to predict leaf area of the eight C. arabica genotypes. The use of linear model resulted in high accuracy for all of the eight C. arabica genotypes. No significant effect of growing altitude and genotype was obtained among the slopes of the models. Therefore, one single model was fitted to the combined data of all genotypes at all altitudes (LA = 0.6434LW). Comparison between observed and predicted leaf area was made using this model in another independent dataset, conducted for model validation, exhibited a high degree of correlation (r = 0.98 - 0.99, P < 0. 01). The over or under estimation of the leaf area using this model ranges between 0.02% to 1.7% and this model is adequate to estimate the leaf area for the eight C. arabica genotypes. Hence, this model can be proposed to be reliably used and with this developed model, researchers can estimate the leaf area of newly released eight genotypes of C. arabica at different altitudes accurately.
基金supported by the National Natural Science Foundation of China (41401491,41371396,41301457,41471364)the Introduction of International Advanced Agricultural Science and Technology,Ministry of Agriculture,China (948 Program,2016-X38)+1 种基金the Agricultural Scientific Research Fund of Outstanding Talentsthe Open Fund for the Key Laboratory of Agri-informatics,Ministry of Agriculture,China (2013009)
文摘To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.
基金European Com mission Project, No.ICA 4-CT-2002-10004 N ational Natural Science Foundation of China, N o. 40371081 K now ledge Innovation ProjectofCA S,N o.K ZCX 3-SW -146
文摘The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of TM3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.
文摘Logistic and exponential approaches have been used to simulate plant growth and leaf area index (LAI) in different growing conditions. The objective of the present study was to develop and evaluate an approach to simulate maize LAI that expresses key physiological and phonological processes using a minimum entry requirement for Quality Protein maize (QPM) varieties grown in the southwestern region of the DR-Congo. Data for the development and testing of the model were collected manually in experimental plots using a non-destructive method. Simulation results revealed measurable variations between crop seasons (long season A and short season B) and between the two varieties (Mudishi-1 and Mudishi-3) for height, number of visible leaves, and LAI. For both seasons, Mudishi-3, a short stature variety was associated with expected stable yield based on simulation data. In general, the model simulated reliably all the parameters including the LAI. The LAI value for mudishi-1 was higher than that of Mudishi-3. There were significant differences among the model parameters (K, Ti, a, b, Tf) and between the two varieties. In all crop conditions studied and for the two varieties, the senescence rate (a) was higher, while the growth rate (b) was lower compared to the estimates based on the STICS model.
文摘叶面积指数(leaf area index,LAI)是反映植物冠层结构和光能利用的重要指标.随着遥感技术的不断发展,利用遥感数据获取大面积LAI已经成为监测作物生长和估产的重要手段.基于物理模型的LAI遥感反演方法经常假设作物冠层结构是均匀分布,然而,作为典型的垄行结构,作物冠层被公认为是介于连续植被与离散植被之间的一种过渡形式,而简单的均匀假设必然会给反演带来偏差.本文以农作物玉米为研究对象,首先重建了玉米三维冠层结构,并定量对比分析了一维辐射传输模型PROSAIL和三维辐射传输模型LESS在玉米冠层不同生长期的反射率差异,确定了玉米冠层的非均匀分布特征是引起PROSAIL模型模拟和反演误差的主要因素;然后,考虑到玉米冠层生长过程中聚集指数的变化特征,利用LESS模型定量计算了不同生育期玉米冠层结构对应的聚集指数,建立了聚集指数和有效叶面积指数(LAI_(e))之间的关系;进而,利用该关系对基于PROSAIL模型反演得到的LAI进行修正.结果表明,修正后的LAI精度有明显提高,R^(2)从0.27提高到了0.55.该方法有望提高中高分辨率遥感数据在农作物LAI反演精度.
文摘叶面积指数(leaf area index,LAI)作为衡量作物生长状况的关键参数,对其进行精准高效的反演对于作物监测、产量预测等活动至关重要。然而,传统经验模型在估算LAI时常存在计算负荷重、泛化能力弱等问题。为实现青贮玉米多时序LAI精准、高效估算,该研究以甘肃省民乐县的大田青贮玉米LAI为研究对象,结合Landsat-8多光谱影像与同期实地采集的LAI数据,提出了4种基于EFAST全局敏感性分析方法的机器学习混合反演模型(MLP-PROSAIL、SVR-PROSAIL、RF-PROSAIL和GBM-PROSAIL)。通过对PROSAIL模型的输入参数进行敏感性分析,以便确定参数敏感度并准确模拟输出冠层反射率光谱。进一步对Landsat-8多光谱数据进行预处理和波段变换,并采用地理配准工具结合反距离加权插值的策略减少其尺度差异。同时利用贝叶斯超参数寻优和正则化技术优化模型不同的参数类型和激活函数,得到4种改进模型用于训练LAI与光谱数据,通过5折交叉验证法和留一验证法对4种模型的反演性能进行验证并选出最优模型。优化后的模型性能均有明显提升,其中,GBM-PROSAIL模型反演性能最优,拟合精度R^(2)为0.93、均方根误差(RMSE)为0.42。MLP-PROSAIL、SVR-PROSAIL和RF-PROSAIL模型的拟合精度R^(2)依次为0.85、0.88、0.90,RMSE依次为0.80、0.69、0.51。根据GBM-PROSAIL模型绘制的研究区多时序LAI反演空间分布结果表明:不同生长期青贮玉米LAI值存在明显差异,能较好反映其生长过程。该研究提出的混合反演模型具有较高的性能及较强的鲁棒性,可为多时序、大尺度作物监测、产量预测相关研究提供方法与思路。
文摘中国东南丘陵地区茶园的快速扩张对地区碳循环产生显著影响。Biome-BGC模型常被用于碳通量定量研究,但其对人工管理过程刻画不足。本研究结合实测与遥感叶面积指数(LAI)数据,改进了Biome-BGC模型,以增强其对茶园人工管理过程的模拟能力。结果表明:LAI是Biome-BGC模型中关键的中间变量,对LAI的准确模拟是提升模型对茶园碳通量模拟精度的关键。改进后的模型显著提升了对总初级生产力(GPP)和生态系统呼吸(RE)的模拟精度,5年平均GPP和RE值分别为1.26、1.19 kg C·m^(-2),日尺度R^(2)分别达到0.55和0.80,较改进前分别提升44.5%和降低0.9%,均方根误差(RMSE)分别为0.887和1.030 g C·m^(-2)·d^(-1),较改进前分别降低50.3%和68.4%,月尺度的模拟效果更佳,显著改善了原始模型因未充分刻画人工修剪导致的碳通量高估问题。改进后的模型能够动态刻画修剪引起的LAI波动对碳循环的影响,并验证了其在不同时间尺度下的适用性,为存在高强度人工管理的茶园生态系统碳循环定量研究提供了技术支撑。
文摘亚洲中高纬度地区是受全球变暖影响最严重的地区之一,其生态系统对气候变化高度敏感,该地区植被的未来变化存在很大不确定性。本研究使用第3版全球陆表卫星遥感数据集(Global Land Surface Satellite Product Version 3,GLOBMAP)、第3代全球植被指数数据集(Global Inventory Modeling and Mapping Studies 3rd generation,GIMMS 3g)、全球陆表参数产品(Global Land Surface Satellite Products,GLASS)3套独立的遥感数据集中的叶面积指数(leaf area index,LAI)变量与耦合模式比较计划第5阶段(Coupled Model Intercomparison Project Phase 5,CMIP5)的15个模型、第6阶段(Coupled Model Intercomparison Project Phase 6,CMIP6)的19个模型模拟的叶面积指数及气候因子数据,基于多模型集合均值的方法对亚洲中高纬地区植被的历史及未来特征进行了系统评估。研究结果表明,CMIP6较CMIP5在模拟叶面积指数及其关键气候影响因子(包括地表气温、降水量和地表下行短波辐射)时的不确定性均有所降低。预计在中等排放情景(RCP4.5和SSP2-4.5)和高排放情景(RCP8.5和SSP5-8.5)下亚洲高纬度地区未来的LAI都将增加,且高排放情景下的增长率比中等排放情景更快。未来LAI的增加在暖季比冷季更为显著,表明植被的季节性周期和振幅都将得到增强。在LAI的高值区域,其年度均值与年际标准差增幅相较于LAI的低值区域将更加明显。
基金supported by the National Major Research High Performance Computing Program of China(Grant No.2016YFB02008)the National Natural Science Foundation of China(Grant Number 41705070)supported by the National Natural Science Foundation of China(Grant Numbers 41475099 and 41305096)
文摘In the past several decades, dynamic global vegetation models（DGVMs） have been the most widely used and appropriate tool at the global scale to investigate vegetation-climate interactions. At the Institute of Atmospheric Physics, a new version of DGVM（IAP-DGVM） has been developed and coupled to the Common Land Model（CoLM） within the framework of the Chinese Academy of Sciences＇ Earth System Model（CAS-ESM）. This work reports the performance of IAP-DGVM through comparisons with that of the default DGVM of CoLM（CoLM-DGVM） and observations. With respect to CoLMDGVM, IAP-DGVM simulated fewer tropical trees, more ＂needleleaf evergreen boreal tree＂ and ＂broadleaf deciduous boreal shrub＂, and a better representation of grasses. These contributed to a more realistic vegetation distribution in IAP-DGVM,including spatial patterns, total areas, and compositions. Moreover, IAP-DGVM also produced more accurate carbon fluxes than CoLM-DGVM when compared with observational estimates. Gross primary productivity and net primary production in IAP-DGVM were in better agreement with observations than those of CoLM-DGVM, and the tropical pattern of fire carbon emissions in IAP-DGVM was much more consistent with the observation than that in CoLM-DGVM. The leaf area index simulated by IAP-DGVM was closer to the observation than that of CoLM-DGVM; however, both simulated values about twice as large as in the observation. This evaluation provides valuable information for the application of CAS-ESM, as well as for other model communities in terms of a comparative benchmark.
基金supported by the National Natural Science Foundation of China (40701120)the Beijing Natural Science Foundation, China (4092016)the Beijing Nova, China (2008B33)
文摘Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm （EnKF） assimilation by combing the crop growth model （CERES-Wheat） with remote sensing data, but also optimized and updated the key parameters （LAI） of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.
