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leaf area index based nitrogen diagnosis in irrigated lowland rice

Journal of Integrative Agriculture 2018年第1期17卷 111-121页

作者： LIU Xiao-jun CAO Qiang YUAN Zhao-feng LIU Xia WANG Xiao-ling TIAN Yong-chao CAO Wei-xing ZHU Yan National Engineering and Technology Center for Information Agriculture Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center forModern Crop Production/Nanjing Agricultural University Nanjing 210095 P.R. China

leaf area index （LAI） is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen （N） status of crops. This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice. Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014. Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice. LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period. Group and individual plant characters （e.g., tiller number （TN） and plant height （H）） were investigated simultaneously. Two N indicators of plant N accumulation （NA） and N nutrition index （NNI） were measured as well. A calibration equation （LAI=1.7787LAI2o00-0.8816, R2=0.870＂） was developed for LAI-2000. The linear regression analysis showed a significant relationship between NA and actual LAI （R2=0.863^＊＊）. For the NNI, the relative LAI （R2=0.808-） was a relatively unbiased variable in the regression than the LAI （R^2=0.33^＊＊）. The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice （NA=29.778LAI-5.9397; NNI=0.7705RLAI＋0.2764）. Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H （LAI=-0.3375（THxHx0.01）2＋3.665（TH×H×0.01）-1.8249, R2=0.875＊＊）. With these models, the N status of rice can be diagnosed conveniently in the field.

关键词： leaf area index rice LAI-2000 nitrogen diagnosis plant characters

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Assimilation of NDVI data in a land surface-Vegetation model for leaf area index predictions in a tree-grass ecosystem

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International Journal of Digital Earth 2023年第1期16卷 3810-3837页

作者： Nicola Montaldo Andrea Gaspa Roberto Corona Dipartimento di Ingegneria civile ambientale e architetturaUniversita di CagliariCagliariItaly

Periodic observations of vegetation index,such as the normalized difference vegetation index(NDVI),can be used for data assimilation in heterogenous ecosystems.Indeed,the new Sentinel 2 Multispectral instrument and Landsat 8 Operational Land Imager sensor data are available at such high temporal and spatial resolutions that can be used to detect the patches of the main vegetation components(grass and trees)of heterogenous ecosystems,and capture their dynamics.We demonstrate the possibility to merge grass and tree NDVI observations and models,to optimally provide robust predictions of grass and tree leaf area index.The proposed assimilation approach assimilates NDVI data through the Ensemble Kalman filter(EnKF)and dynamically calibrates a key vegetation dynamic model parameter,the maintenance respiration coeffcient(ma).In the presence of large bias of the grass and tree ma base values,only the use of the proposed assimilation approach removes the large bias in the biomass balance,dynamically calibrating maintenance respiration coefficients,and corrects the model.The performance of a land surface-vegetation model was improved by assimilating observations of NDVl.The effective impact of the proposed assimilation approach on the evapotranspiration and CO_(2) uptake predictions in the heterogenous ecosystem is also demonstrated.

关键词： Data assimilation leaf area index heterogenous ecosystem vegetation dynamic model Landsat 8 Sentinel 2

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Conventional digital cameras as a tool for assessing leaf area index and biomass for cereal breeding

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Journal of Integrative Plant Biology 2014年第1期56卷 7-14页

作者： Jaume Casadesús Dolors Villegas Institute of Food and Agricultural Research and Technology

Affordable and easy-to-use methods for assessing biomass and leaf area index （LAI） would be of interest in most breeding programs. Here, we describe the evaluation of a protocol for photographic sampling and image analysis aimed at providing low-labor yet robust indicators of biomass and LAI. In this trial, two genotypes of triticale, two of bread wheat, and four of tritordeum were studied. At six dates during the growing cycle, biomass and LAI were measured destructively, and digital photography was taken on the same dates. Several vegetation indices were calculated from each image. The results showed that repeatable and consistent values of the indices were obtained in consecutive photographic samplings on the same plots. The photographic indices were highly correlated with the destructive measure-ments, though the magnitude of the correlation was lower after anthesis. This work shows that photographic assess-ment of biomass and LAI can be fast, affordable, have good repeatability, and can be used under bright and overcast skies. A practical vegetation index derived from pictures is the fraction of green pixels over the total pixels of the image, and as it shows good correlations with all biomass variables, is the most robust to lighting conditions and has easy interpretation.

关键词： Digital camera image analysis leaf area index plantbreeding vegetation index

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Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots

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Forest Ecosystems 2020年第4期7卷 686-703页

作者： Jie Zou Wei Hou Ling Chen Qianfeng Wang Peihong Zhong Yong Zuo Shezhou Luo Peng Leng The Academy of Digital China(Fujian) Fuzhou UniversityFuzhou 350116China. Key Laboratory of Data Mining and Information Sharing Ministry of EducationFuzhou 350116China. School of Forestry Beijing Forestry UniversityBeijing 100083China. College of Environment and Resources Fuzhou UniversityFuzhou 350116China. College of Resources and Environment Fujian Agriculture and Forestry UniversityFuzhou 350002China.

Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme.However,various sampling schemes involving DHP have been used for the LAI estimation of forest plots.To date,the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated.Methods:In this study,13 commonly used sampling schemes which belong to five sampling types(i.***.dispersed,square,cross,transect and circle)were adopted in the LAI estimation of five Larix principis-rupprechtii plots(25m×25 m).An additional sampling scheme(with a sample size of 89)was generated on the basis of all the sample points of the 13 sampling schemes.Three typical inversion models and four canopy element clumping index(Ωe)algorithms were involved in the LAI estimation.The impacts of the sampling schemes on four variables,including gap fraction,Ωe,effective plant area index(PAIe)and LAI estimation from DHP were analysed.The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements.Results:Large differences were observed for all four variable estimates(i.***.gap fraction,Ωe,PAIe and LAI)under different sampling schemes.The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models,if the fourΩe algorithms,except for the traditional gap-size analysis algorithm were adopted in the estimation.The accuracy of LAI estimation was not always improved with an increase in sample size.Moreover,results indicated that with the appropriate inversion model,Ωe algorithm and sampling scheme,the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%,which is required by the global climate observing system,except in forest plots with extremely large LAI values(~>6.0).However,obtaining an LAI from DHP with an

关键词： Sampling scheme Elementary sampling unit Clumping index leaf area index Digital hemispherical photography Forest Larix

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Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

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Forest Ecosystems 2018年第4期5卷 400-411页

作者： ilker Ercanli Alkan Gunlu Muammer Senyurt Sedat Keles Faculty of Forestry Cankin Karatekin University18200 CankiriTurkey

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.

关键词： leaf area index Multivariate linear regression model Artificial neural network modeling Crimean pine Stand parameters

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Improved Land Use and leaf area index Enhances WRF-3DVAR Satellite Radiance Assimilation： A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

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Advances in Atmospheric Sciences 2018年第6期35卷 628-644页

作者： Junhua YANG Zhenming JI Deliang CHEN Shichang KANG Congshen FU Keqin DUAN Miaogen SHEN State Key Laboratory of Cryospheric Sciences Northwest Institute of Eco-Environment and ResourcesChinese Academy of Sciences (CAS) Lanzhou 730000 China School of Atmospheric Sciences Sun Yat-sen University Guangzhou 510275 China Department of Earth Sciences University of Gothenburg Gothenburg 40530 Sweden Yale School of Forestry and Environmental Studies Yale University New Haven Connecticut 06511 USA College of Tourism and Environment Shaanxi Normal University Xi'an 710000 China Key Laboratory of Alpine Ecology and Biodiversity Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing 100101 China CAS Center for Excellence in Tibetan Plateau Earth Sciences Beijing 100101 China

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level（surface-sensitive）channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets.Here, we used an improved land use and leaf area index（LAI） dataset in the WRF-3 DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels（e.g., channel 3）,the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.

关键词： WRF-3DVAR land use leaf area index radiance assimilation rainfall simulation

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Comparative analysis of GF-1,HJ-1,and Landsat-8 data for estimating the leaf area index of winter wheat

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Journal of Integrative Agriculture 2017年第2期16卷 266-285页

作者： LI He CHEN Zhong-xin JIANG Zhi-wei WU Wen-bin REN Jian-qiang LIU Bin Tuya Hasi Key Laboratory of Agri-informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences National Meteorological Information Center China Meteorological Administration

Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 （GF-1） wide field of view （WFV） camera, environment and disaster monitoring and forecasting satellite （H J-l） charge coupled device （CCD）, and Landsat-8 opera- tional land imager （OLI） data for estimating the leaf area index （LAI） of winter wheat via reflectance and vegetation indices （VIs）. The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration, spectral response functions, and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations：（1） The correlation between reflectance from different sensors is relative good, with the adjusted coefficients of determination （R2） between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors＇ images can all be used for cross calibration of the reflectance and VIs. （2） The four VIs from the three sensors are all demonstrated to be highly correlated with LAI （R2 between 0.703 and 0.849）. The linear models associated with the 2-band enhanced vegetation index （EVI2）, which feature the highest R2 （higher than 0.746） and the lowest root mean square errors （RMSE）（less than 0.21）, were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest, with the relative errors （RE） of 2.18% and an RMSE of 0.13, while the H J-1 was the lowest, with the RE of 2.43% and the RMSE of 0.15. （3） The inversion errors in the different sensors＇ LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the hig

关键词： GF-1 WFV H J-1 CCD Landsat-80LI leaf area index PROSAIL vegetation indices

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Comparison of leaf area index inversion for grassland vegetation through remotely sensed spectra by unmanned aerial vehicle and field-based spectroradiometer

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Journal of Plant Ecology 2019年第3期12卷 395-408页

作者： Zongyao Sha Yuwei Wang Yongfei Bai Yujin Zhao Hua Jin Ya Na Xiaoliang Meng Department of Spatial Information&Digital Engineering School of Remote Sensing and Information EngineeringWuhan UniversityWuhanHubei 430079China Research Center of Spatial Information and Digital Engineering of the State Bureau of Surveying and Mapping WuhanHubei 430079China Institute of Botany the Chinese Academy of SciencesBeijing 100093China Inner Mongolia Institute of Grassland Surveying and Planning Hohhot 010051China Key Laboratory of Forage Cultivation College of GrasslandResources and EnvironmentInner Mongolia Agricultural UniversityHohhot 010018China

Aims Remote sensing technology has been proved useful in mapping grass-land vegetation properties.Spectral features of vegetation cover can be recorded by optical sensors on board of different platforms.With increas-ing popularity of applying unmanned aerial vehicle(UAV)to mapping plant cover,the study aims to investigate the possible applications and potential issues related to mapping leaf area index(LAI)through integra-tion of remote sensing imagery collected by multiple sensors.Methods This paper applied the collected spectral data through field-based(FLD)and UAV-borne spectroradiometer to map LAI in a Sino-German experiment pasture located in the Xilingol grassland,Inner Mongolia,China.Spectroradiometers on FLD and UAV platforms were taken to measure spectral reflectance related to the targeted vegetation proper-ties.Based on eight vegetation indices(VIs)computed from the col-lected hyperspectral data,regression models were used to inverse LAI.The spectral responses between FLD and UAV platforms were com-pared,and the regression models relating LAI with VIs from FLD and UAV were established.The modeled LAIs by UAV and FLD platforms were analyzed in order to evaluate the feasibility of potential integra-tion of spectra data for mapping vegetation from the two platforms.Important Findings Results indicated that the spectral reflectance between FLD and UAV showed critical gaps in the green and near-infrared regions of the spec-trum over densely vegetated areas,while the gaps were small over sparsely vegetated areas.The VI values from FLD spectra were greater than their UAV-based counterparts.Out of all the VIs,broadband gen-eralized soil-adjusted vegetation index(GESAVI)and narrow-band nNDVI2 were found to achieve the best results in terms of the accuracy of the inversed LAIs for both FLD and UAV platforms.We conclude that GESAVI and nNDVI2 are the two promising VIs for both platforms and thus preferred for LAI inversion to carry spectra integration of the two platforms.We suggest that accuracy on the LAI inver

关键词： grassland leaf area index unmanned aerial vehicle vegetation index remote sensing

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Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

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Journal of Integrative Agriculture 2017年第10期16卷 2283-2299页

作者： LI He JIANG Zhi-wei CHEN Zhong-xin REN Jian-qiang LIU Bin Hasituya Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences National Meteorological Information Center China Meteorological Administration

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.

关键词： winter wheat yield estimates crop model data assimilation ensemble Kalman filter uncertainty leaf area index

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Comprehensive Study on the Influence of Evapotranspiration and Albedo on Surface Temperature Related to Changes in the leaf area index

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Advances in Atmospheric Sciences 2015年第7期32卷 935-942页

作者： ZHU Jiawen ZENG Xiaodong International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of Sciences University of Chinese Academy of Sciences Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science & Technology

Many studies have investigated the influence of evapotranspiration and albedo and emphasize their separate effects but ignore their interactive influences by changing vegetation status in large amplitudes. This paper focuses on the comprehensive influence of evapotranspiration and albedo on surface temperature by changing the leaf area index （LAD between 30^-90~N. Two LAI datasets with seasonally different amplitudes of vegetation change between 30^-90~N were used in the simulations. Seasonal differences between the results of the simulations are compared, and the major findings are as follows. （1） The interactive effects of evapotranspiration and albedo on surface temperature were different over different regions during three seasons [March-April-May （MAM）, June-July-August （JJA）, and September-October-November （SON）], i.e., they were always the same over the southeastern United States during these three seasons but were opposite over most regions between 30°-90°N during JJA. （2） Either evapotranspiration or albedo tended to be dominant over different areas and during different seasons. For example, evapotranspiration dominated almost all regions between 30^-90~N during JJA, whereas albedo played a dominant role over northwestern Eurasia during MAM and over central Eurasia during SON. （3） The response of evapotranspiration and albedo to an increase in LAI with different ranges showed different paces and signals. With relatively small amplitudes of increased LAI, the rate of the relative increase in evapotranspiration was quick, and positive changes happened in albedo. But both relative changes in evapotranspiration and albedo tended to be gentle, and the ratio of negative changes of albedo increased with relatively large increased amplitudes of LAI.

关键词： surface temperature evapotranspiration albedo leaf area index comprehensive influence

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