Although the distributions of foliage and light play major roles in various forest functions,accurate,nondestructive measurement of these distributions is difficult due to the complexity of the canopy structure.To eva...Although the distributions of foliage and light play major roles in various forest functions,accurate,nondestructive measurement of these distributions is difficult due to the complexity of the canopy structure.To evaluate the foliage and light distributions directly and nondestructively in a mature oak stand,we used the cube method by dividing the forest canopy into small cubes(50 cm per side)and directly measured leaf area density(LAD,the total one-sided leaf area per unit volume,i.e.,cube)and relative irradiance(RI)within each cube.The distribution of LAD and of RI was highly heterogeneous,even at the same canopy height.This heterogeneity reflected the presence of foliage clusters associated with multiple forking branches.The relationship between cumulative LAD at the canopy surface and average RI followed the Beer-Lambert law.The mean light extinction coefficient(K)was 0.32.However,K was overestimated by more than double(0.80)when calculated based on the classical method using RI at the forest floor.This overestimation was caused by the lower RI due to light absorption by nonleaf plant parts below the canopy.Our findings on the complex foliage and light distributions in canopy layers should help improve the accuracy of RI and K measurements and thus more accurate predictions of environmental responses and forest functions.展开更多
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 fiel...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 highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the H J-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26. (4) The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.展开更多
Leaf area index (LAI) is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of...Leaf area index (LAI) is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs (normalized difference vegetation indices), which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance (VIS)〉0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI (Green NDVI) and GBNDVI (Green-Blue NDVI) had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI.展开更多
Leaf area index (LAI) is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal...Leaf area index (LAI) is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices (SVIs) were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index (DVI) and LAI, with the adjusted R2 (0.82) and the RMSE (0.77). The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value (30 m) for winter wheat of the study area increased from 1.29 (March 7, 2004) to 3.43 (April 8, 2004), with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.展开更多
The method for simulating the temporal and spatial distribution patterns of leaf area index (LAI) and biomass at landscape scale using remote sensing images and surface data was discussed in this paper. The procedure...The method for simulating the temporal and spatial distribution patterns of leaf area index (LAI) and biomass at landscape scale using remote sensing images and surface data was discussed in this paper. The procedure was: (1) annual maximum normalized difference vegetation index (NDVI) over the landscape was calculated from TM images; (2) the relationship model between NDVI and LAI was built and annual maximum LAI over the landscape was simulated; (3) the relationship models between LAI and biomass were built and annual branch, stem, root and maximum leaf biomass over the landscape were simulated; (4) spatial distribution patterns of leaf biomass and LAI in different periods all the year round were obtained. The simulation was based on spatial analysis module GRID in ArcInfo software. The method is also a kind of scaling method from patch scale to landscape scale. A case study of Changbai Mountain Nature Reserve was dissertated. Analysis and primary validation were carried out to the simulated LAI and biomass for the major vegetation types in the Changbai Mountain in 1995.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
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 ...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.展开更多
An inversion of bidirectional reflection distribution fiJnedon (BRDF) wastested using NK Model and NOAA AVHRR datu. The test involVed sensitiveanalysis, optimum inversion selecting, ground simulated expenment, calibra...An inversion of bidirectional reflection distribution fiJnedon (BRDF) wastested using NK Model and NOAA AVHRR datu. The test involVed sensitiveanalysis, optimum inversion selecting, ground simulated expenment, calibrahngmeasuremed with satellite and computer image processmg. Results of comparisonwith NDVI indicatal that inversion of BRDF will have brigh developing prospect inthe next decade.展开更多
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
Short basal internodes are important for lodging resistance of rice(Oryza sativa L.).Several canopy indices affect the elongation of basal internodes,but uncertainty as to the key factors determining elongation of bas...Short basal internodes are important for lodging resistance of rice(Oryza sativa L.).Several canopy indices affect the elongation of basal internodes,but uncertainty as to the key factors determining elongation of basal internodes persists.The objectives of this study were(1)to identify key factors affecting the elongation of basal internodes and(2)to establish a quantitative relationship between basal internode length and canopy indices.An inbred rice cultivar,Yinjingruanzhan,was grown in two split-plot field experiments with three N rates(0,75,and 150 kg N ha−1 in early season and 0,90,and 180 kg N ha−1 in late season)as main plots,three seedling densities(16.7,75.0,and 187.5 seedlings m−2)as subplots,and three replications in the 2015 early and late seasons in Guangzhou,China.Light intensity at base of canopy(Lb),light quality as determined from red/far-red light ratio(R/FR),light transmission ratio(LTR),leaf area index(LAI),leaf N concentration(NLV)and final length of second internode(counted from soil surface upward)(FIL)were recorded.Higher N rate and seedling density resulted in significantly longer FIL.FIL was negatively correlated with Lb,LTR,and R/FR(P<0.01)and positively correlated with LAI(P<0.01),but not correlated with NLV(P>0.05).Stepwise linear regression analysis showed that FIL was strongly associated with Lb and LAI(R2=0.82).Heavy N application to pot-grown rice at the beginning of first internode elongation did not change FIL.We conclude that FIL is determined mainly by Lb and LAI at jointing stage.NLV has no direct effect on the elongation of basal internodes.N application indirectly affects FIL by changing LAI and light conditions in the rice canopy.Reducing LAI and improving canopy light transmission at jointing stage can shorten the basal internodes and increase the lodging resistance of rice.展开更多
Leaf area index (LAI) of natural vegetation is recognized as the most important variable for measuring vegetation structure over large areas, and for relating it to energy and mass exchange, which has been successfull...Leaf area index (LAI) of natural vegetation is recognized as the most important variable for measuring vegetation structure over large areas, and for relating it to energy and mass exchange, which has been successfully estimated from satellite resolution sensors. In this paper, according to the statistical analysis based on a lot of forest plots, the mathematical models of LAI distribution patterns in the hydro thermal spaces for five coniferous forest types in China were established. For the cold temperate larch forests growing in the dry and cold climate, their LAI increases with the increasing of warm index and precipitation in the way of hyperbolic quadratic surface. For the cold temperate spruce fir forests and temperate Pinus tabulaeformis forests, their LAI is negatively related to the annual mean air temperature in the way of the natural exponential curve, in order to adapt to the water oppressed environments. For the subtropical Pinus massoniana forests and Cunninghamia lanceolata forests growing in the warm and moist climate, their LAI is related to the annual mean air temperature in the way of the parabolic quadratic curve.展开更多
The leaf area index(LAI) is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop cano...The leaf area index(LAI) is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies.During the last two decades,hyperspectral remote sensing has been employed increasingly for crop LAI estimation,which requires unique technical procedures compared with conventional multispectral data,such as denoising and dimension reduction.Thus,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques.First,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation.Second,we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types:approaches based on statistical models,physical models(i.e.,canopy reflectance models),and hybrid inversions.We summarize and evaluate the theoretical basis and different methods employed by these approaches(e.g.,the characteristic parameters of LAI,regression methods for constructing statistical predictive models,commonly applied physical models,and inversion strategies for physical models).Thus,numerous models and inversion strategies are organized in a clear conceptual framework.Moreover,we highlight the technical difficulties that may hinder crop LAI estimation,such as the "curse of dimensionality" and the ill-posed problem.Finally,we discuss the prospects for future research based on the previous studies described in this review.展开更多
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...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.展开更多
In this study, we developed a computer program for automatic prediction of watering time point by considering the environmental factors such as solar radiation, air temperature and relative humidity based on the multi...In this study, we developed a computer program for automatic prediction of watering time point by considering the environmental factors such as solar radiation, air temperature and relative humidity based on the multiple linear regression equation of leaf area and Penman Method. The experiments were carried out for a year in two watering experimental plots, one of which was controlled by pF value, and the other by the computer program. After comparing the results of the two plots, the following findings were obtained. In the computer program plot, the observed and predicted values of both leaf area and evapotranspiration indicated significant correlation at the 1% level, which suggested that the computer program had high prediction accuracy. In addition, no significant difference was observed between the two experimental plots with respects to the plant height, plant diameter, leaf area, leaf number, fresh weight, and dry weight, which demonstrated that the plants in the computer program plot had normal growth. On the other hand, although the number of flower buds and flowering shoots showed higher values at the end of certain cultivations in the computer program plot than those in pF value plot, we proposed that it was due to the effect of cumulative daily solar radiation in the greenhouse, rather than the watering. Thus, we have reached the conclusion that the computer program for automatic prediction of watering time point developed by this study has high applicability in miniature pot rose production.展开更多
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 l...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.展开更多
Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize (Zea mays L.). Thus, investigating the genetic basis of leaf area will a...Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize (Zea mays L.). Thus, investigating the genetic basis of leaf area will aid efforts to breed maize with high yield. In this study, a total of 150 F7 recombinant inbred lines (RILs) derived from a cross between the maize lines Xu 178 and K12 were used to evaluate three ear-leaves area (TELA) under multi-environments. Inclusive composite interval map- ping (ICIM) was used to identify quantitative trait loci (QTLs) for TELA under a single environment and estimated breeding value (EBV). A total of eight QTLs were detected under a single environmental condition, and four QTLs were identified for EBV which also can be detected in single environment. This indicated that the EBV-detected QTLs have high genetic stability. A major QTL (qTELA_2-9) located in chromosome bin 2.04/2.05 could be detected in four environments and has a high phenotypic contribution rate (ranging from 10.79 to 16.51%) that making it a good target for molecular breeding. In addition, joint analysis was used to reveal the genetic basis of leaf area in six environments. In total, six QTLxenvironment interactions and nine epistatic interactions were identified. Our results reveal that the genetic basis of the leaf area is not only mainly determined by additive effects, but also affected by epistatic effects environmental interaction effects.展开更多
After water stress at various levels and durations at different growth stages, rewatering could greatly stimulate the leaf area development of winter wheat. The results showed that the stimulation effect changed with ...After water stress at various levels and durations at different growth stages, rewatering could greatly stimulate the leaf area development of winter wheat. The results showed that the stimulation effect changed with water stress time, degree and duration. Rewatering under earlier stress had greater stimulation effect on leaf area than that under later stress. Higher stimulation effect was observed under severe water stress than that under moderate stress. Longer duration of stress resulted in low stimulation effect. In spite of the greater stimulation effect under severe and longer stress, the final leaf area in these situations was lower than that under moderate stress and shorter duration. Whenever the stress occurred, the stimulating effect was due to the increase of the leaf area of the tillers. Once the leaf on the main stem emerged during stress period, rewatering had no effect on its size, and consequently its leaf area. The stimulation of rewateirng on leaf area contributed to the final grain yield by 45% under moderate stress, and 67% under severe stress. Although the stimulation partly compensated for the loss during stress, the final leaf area and the grain yield could not reach the level without water stress.展开更多
The amount of photosynthetic radiation inter- cepted by a crop is a function of the incident solar radiation on the plants, the leaf area index (LAI), and the light extinction coefficient (k). We quantified LAI an...The amount of photosynthetic radiation inter- cepted by a crop is a function of the incident solar radiation on the plants, the leaf area index (LAI), and the light extinction coefficient (k). We quantified LAI and k in stands of black wattle (Acacia mearnsii De Wild.) over a 7-year growth cycle at two locations in the state of Rio Grande do Sul, Brazil. Our study was conducted in commercial stands in agroecological regions with high densities of black wattle plantations. LAI was calculated as the ratio between the leaf area of a tree and its planting space, and k was derived from Beer's law. LAI depends on the planting site and stand age. Between the two sites, the LAI was similar over time, the amount of variation differed. Values of k depended only on stand age, with the highest average observed for stands up to 5 years old. The trend of k during the plantation cycle was inversely proportional to LAI and was correlated with LAI, leaf area, leaf dry mass, canopy volume, height, branches dry mass, total dry mass, and crown diameter.展开更多
基金partly supported by Grant-in-Aid for scientific research(No.17658070,22580173)from the Ministry of Education,Science and Culture,Japan“Evaluation of genetic resources for strengthening productivity and adaptability of tropical forests”from the Japan International Research Centre for Agricultural Sciences。
文摘Although the distributions of foliage and light play major roles in various forest functions,accurate,nondestructive measurement of these distributions is difficult due to the complexity of the canopy structure.To evaluate the foliage and light distributions directly and nondestructively in a mature oak stand,we used the cube method by dividing the forest canopy into small cubes(50 cm per side)and directly measured leaf area density(LAD,the total one-sided leaf area per unit volume,i.e.,cube)and relative irradiance(RI)within each cube.The distribution of LAD and of RI was highly heterogeneous,even at the same canopy height.This heterogeneity reflected the presence of foliage clusters associated with multiple forking branches.The relationship between cumulative LAD at the canopy surface and average RI followed the Beer-Lambert law.The mean light extinction coefficient(K)was 0.32.However,K was overestimated by more than double(0.80)when calculated based on the classical method using RI at the forest floor.This overestimation was caused by the lower RI due to light absorption by nonleaf plant parts below the canopy.Our findings on the complex foliage and light distributions in canopy layers should help improve the accuracy of RI and K measurements and thus more accurate predictions of environmental responses and forest functions.
基金supported by the National Natural Science Foundation of China (41371396,41401491 and 41471364)the Introduction of International Advanced Agricultural Science and Technology,Ministry of Agriculture,China (948 Program,2011-G6)the Agricultural Scientific Research Fund of Outstanding Talents and the Open Fund for the Key Laboratory of Agri-informatics,Ministry of Agriculture,China (2013009)
文摘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 highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the H J-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26. (4) The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.
文摘Leaf area index (LAI) is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs (normalized difference vegetation indices), which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance (VIS)〉0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI (Green NDVI) and GBNDVI (Green-Blue NDVI) had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI.
文摘Leaf area index (LAI) is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices (SVIs) were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index (DVI) and LAI, with the adjusted R2 (0.82) and the RMSE (0.77). The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value (30 m) for winter wheat of the study area increased from 1.29 (March 7, 2004) to 3.43 (April 8, 2004), with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.
基金One Hundred Talents Program of CAS No.CXIOG-C00-01+1 种基金 National Natural Science Foundation of China No.39970613
文摘The method for simulating the temporal and spatial distribution patterns of leaf area index (LAI) and biomass at landscape scale using remote sensing images and surface data was discussed in this paper. The procedure was: (1) annual maximum normalized difference vegetation index (NDVI) over the landscape was calculated from TM images; (2) the relationship model between NDVI and LAI was built and annual maximum LAI over the landscape was simulated; (3) the relationship models between LAI and biomass were built and annual branch, stem, root and maximum leaf biomass over the landscape were simulated; (4) spatial distribution patterns of leaf biomass and LAI in different periods all the year round were obtained. The simulation was based on spatial analysis module GRID in ArcInfo software. The method is also a kind of scaling method from patch scale to landscape scale. A case study of Changbai Mountain Nature Reserve was dissertated. Analysis and primary validation were carried out to the simulated LAI and biomass for the major vegetation types in the Changbai Mountain in 1995.
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.
基金supported by the Chi-nese Academy of Sciences Strategic Priority Research Program (Grant No. XDA05110103)the National High Technology Research and Development Program of China (863 Program, Grant No. 2009AA122100)
文摘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.
文摘An inversion of bidirectional reflection distribution fiJnedon (BRDF) wastested using NK Model and NOAA AVHRR datu. The test involVed sensitiveanalysis, optimum inversion selecting, ground simulated expenment, calibrahngmeasuremed with satellite and computer image processmg. Results of comparisonwith NDVI indicatal that inversion of BRDF will have brigh developing prospect inthe next decade.
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.
基金supported by the Natural Science Foundation of Guangdong Province,China(S2012020011043)the National High Technology Research and Development Program of China(2014AA10A605)+2 种基金the Special Fund for Agro-scientific Research in the Public Interest(201503106)Modern Agriculture Industry Technology System for Rice in Guangdong Province(2016LM1066,2017LM1066,2018LM1066)the Swiss Agency for Development and Cooperation through its funding of “Closing Rice Yield Gaps in Asia” Project(CORIGAP)
文摘Short basal internodes are important for lodging resistance of rice(Oryza sativa L.).Several canopy indices affect the elongation of basal internodes,but uncertainty as to the key factors determining elongation of basal internodes persists.The objectives of this study were(1)to identify key factors affecting the elongation of basal internodes and(2)to establish a quantitative relationship between basal internode length and canopy indices.An inbred rice cultivar,Yinjingruanzhan,was grown in two split-plot field experiments with three N rates(0,75,and 150 kg N ha−1 in early season and 0,90,and 180 kg N ha−1 in late season)as main plots,three seedling densities(16.7,75.0,and 187.5 seedlings m−2)as subplots,and three replications in the 2015 early and late seasons in Guangzhou,China.Light intensity at base of canopy(Lb),light quality as determined from red/far-red light ratio(R/FR),light transmission ratio(LTR),leaf area index(LAI),leaf N concentration(NLV)and final length of second internode(counted from soil surface upward)(FIL)were recorded.Higher N rate and seedling density resulted in significantly longer FIL.FIL was negatively correlated with Lb,LTR,and R/FR(P<0.01)and positively correlated with LAI(P<0.01),but not correlated with NLV(P>0.05).Stepwise linear regression analysis showed that FIL was strongly associated with Lb and LAI(R2=0.82).Heavy N application to pot-grown rice at the beginning of first internode elongation did not change FIL.We conclude that FIL is determined mainly by Lb and LAI at jointing stage.NLV has no direct effect on the elongation of basal internodes.N application indirectly affects FIL by changing LAI and light conditions in the rice canopy.Reducing LAI and improving canopy light transmission at jointing stage can shorten the basal internodes and increase the lodging resistance of rice.
文摘Leaf area index (LAI) of natural vegetation is recognized as the most important variable for measuring vegetation structure over large areas, and for relating it to energy and mass exchange, which has been successfully estimated from satellite resolution sensors. In this paper, according to the statistical analysis based on a lot of forest plots, the mathematical models of LAI distribution patterns in the hydro thermal spaces for five coniferous forest types in China were established. For the cold temperate larch forests growing in the dry and cold climate, their LAI increases with the increasing of warm index and precipitation in the way of hyperbolic quadratic surface. For the cold temperate spruce fir forests and temperate Pinus tabulaeformis forests, their LAI is negatively related to the annual mean air temperature in the way of the natural exponential curve, in order to adapt to the water oppressed environments. For the subtropical Pinus massoniana forests and Cunninghamia lanceolata forests growing in the warm and moist climate, their LAI is related to the annual mean air temperature in the way of the parabolic quadratic curve.
基金financed by the National High-Tech R&D Program of China(2012AA12A304)the National Natural Science Foundation of China(41271112 and 41201089)
文摘The leaf area index(LAI) is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies.During the last two decades,hyperspectral remote sensing has been employed increasingly for crop LAI estimation,which requires unique technical procedures compared with conventional multispectral data,such as denoising and dimension reduction.Thus,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques.First,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation.Second,we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types:approaches based on statistical models,physical models(i.e.,canopy reflectance models),and hybrid inversions.We summarize and evaluate the theoretical basis and different methods employed by these approaches(e.g.,the characteristic parameters of LAI,regression methods for constructing statistical predictive models,commonly applied physical models,and inversion strategies for physical models).Thus,numerous models and inversion strategies are organized in a clear conceptual framework.Moreover,we highlight the technical difficulties that may hinder crop LAI estimation,such as the "curse of dimensionality" and the ill-posed problem.Finally,we discuss the prospects for future research based on the previous studies described in this review.
基金supported by the Special Program for Agriculture Science and Technology from the Ministry of Agriculture of China (201303109)the National Key Research & Development Program of China (2016YFD0300604+3 种基金 2016YFD0200602)the Fundamental Research Funds for the Central Universities,China (262201602)the Priority Academic Program Development of Jiangsu Higher Education Institutions of China (PAPD)the 111 Project of China (B16026)
文摘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.
基金supported by the Science Founda-tion of Guangxi Zhuang Autonomous Region, China(0832002)
文摘In this study, we developed a computer program for automatic prediction of watering time point by considering the environmental factors such as solar radiation, air temperature and relative humidity based on the multiple linear regression equation of leaf area and Penman Method. The experiments were carried out for a year in two watering experimental plots, one of which was controlled by pF value, and the other by the computer program. After comparing the results of the two plots, the following findings were obtained. In the computer program plot, the observed and predicted values of both leaf area and evapotranspiration indicated significant correlation at the 1% level, which suggested that the computer program had high prediction accuracy. In addition, no significant difference was observed between the two experimental plots with respects to the plant height, plant diameter, leaf area, leaf number, fresh weight, and dry weight, which demonstrated that the plants in the computer program plot had normal growth. On the other hand, although the number of flower buds and flowering shoots showed higher values at the end of certain cultivations in the computer program plot than those in pF value plot, we proposed that it was due to the effect of cumulative daily solar radiation in the greenhouse, rather than the watering. Thus, we have reached the conclusion that the computer program for automatic prediction of watering time point developed by this study has high applicability in miniature pot rose production.
基金supported by the National Key Research and Development Program of China(Grant No.2016YFA0602701)the National Natural Science Foundation of China(Grant Nos.41721091,41630754,91644225)the Open Program(Grant No.SKLCS-OP-2017-02)from the State Key Laboratory of Cryospheric Science,Northwest Institute of EcoEnvironment and Resources,Chinese Academy of Sciences
文摘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.
基金supported financially by the National Natu ral Science Foundation of China(31301830)the Natural Science Basic Research Plan in Shaanxi Province of China(2014JQ3108)+1 种基金the Special Fund for Basic Research in Northwest A&F University,China(QN2012001)the Chinese Scholarship Council(CSC)
文摘Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize (Zea mays L.). Thus, investigating the genetic basis of leaf area will aid efforts to breed maize with high yield. In this study, a total of 150 F7 recombinant inbred lines (RILs) derived from a cross between the maize lines Xu 178 and K12 were used to evaluate three ear-leaves area (TELA) under multi-environments. Inclusive composite interval map- ping (ICIM) was used to identify quantitative trait loci (QTLs) for TELA under a single environment and estimated breeding value (EBV). A total of eight QTLs were detected under a single environmental condition, and four QTLs were identified for EBV which also can be detected in single environment. This indicated that the EBV-detected QTLs have high genetic stability. A major QTL (qTELA_2-9) located in chromosome bin 2.04/2.05 could be detected in four environments and has a high phenotypic contribution rate (ranging from 10.79 to 16.51%) that making it a good target for molecular breeding. In addition, joint analysis was used to reveal the genetic basis of leaf area in six environments. In total, six QTLxenvironment interactions and nine epistatic interactions were identified. Our results reveal that the genetic basis of the leaf area is not only mainly determined by additive effects, but also affected by epistatic effects environmental interaction effects.
基金National Fundamental Research and Development (No. G1999011709 ) the National Natural Science Foundation (No.49971042).
文摘After water stress at various levels and durations at different growth stages, rewatering could greatly stimulate the leaf area development of winter wheat. The results showed that the stimulation effect changed with water stress time, degree and duration. Rewatering under earlier stress had greater stimulation effect on leaf area than that under later stress. Higher stimulation effect was observed under severe water stress than that under moderate stress. Longer duration of stress resulted in low stimulation effect. In spite of the greater stimulation effect under severe and longer stress, the final leaf area in these situations was lower than that under moderate stress and shorter duration. Whenever the stress occurred, the stimulating effect was due to the increase of the leaf area of the tillers. Once the leaf on the main stem emerged during stress period, rewatering had no effect on its size, and consequently its leaf area. The stimulation of rewateirng on leaf area contributed to the final grain yield by 45% under moderate stress, and 67% under severe stress. Although the stimulation partly compensated for the loss during stress, the final leaf area and the grain yield could not reach the level without water stress.
文摘The amount of photosynthetic radiation inter- cepted by a crop is a function of the incident solar radiation on the plants, the leaf area index (LAI), and the light extinction coefficient (k). We quantified LAI and k in stands of black wattle (Acacia mearnsii De Wild.) over a 7-year growth cycle at two locations in the state of Rio Grande do Sul, Brazil. Our study was conducted in commercial stands in agroecological regions with high densities of black wattle plantations. LAI was calculated as the ratio between the leaf area of a tree and its planting space, and k was derived from Beer's law. LAI depends on the planting site and stand age. Between the two sites, the LAI was similar over time, the amount of variation differed. Values of k depended only on stand age, with the highest average observed for stands up to 5 years old. The trend of k during the plantation cycle was inversely proportional to LAI and was correlated with LAI, leaf area, leaf dry mass, canopy volume, height, branches dry mass, total dry mass, and crown diameter.