Comparison of architecture among different cultivars of hybrid rice using a spatial light model based on 3-D digitising

2008 ◽  
Vol 35 (10) ◽  
pp. 900 ◽  
Author(s):  
Bangyou Zheng ◽  
Lijuan Shi ◽  
Yuntao Ma ◽  
Qiyun Deng ◽  
Baoguo Li ◽  
...  

Modification of plant types (i.e. plant architecture) is an important strategy to enhance the yield potential of crops. The aims of this study were to specify rice plant types using 3-D modelling methodology. The architecture of three typical hybrid rice cultivars were measured in situ in a paddy field using a 3-D digitiser at four development stages from the panicle initiation to the filling stage. The structural parameters of the rice canopies were calculated and their light capture and potential carbon gain were simulated based on a 3-D light model. The results confirmed that a plant type with steeper leaf angles let light penetrate more deeply with relatively uniform light distribution in the canopy at higher sun elevation angles, although this result was related to leaf area index. The variations of plant types, however, did not convert into differences of light distribution across rice varieties at lower sun elevation angles. Light use efficiency at the higher leaf area index could be enhanced by reducing mutual-shading. These results indicate that a promising approach to quantify the rice architecture in situ is to combine 3-D digitising and a 3-D light model to evaluate light interception and photosynthesis of rice plant types.

2013 ◽  
Vol 6 (3) ◽  
pp. 4603-4663 ◽  
Author(s):  
Z. Yin ◽  
S. C. Dekker ◽  
B. J. J. M. van den Hurk ◽  
H. A. Dijkstra

Abstract. A myriad of interactions exist between vegetation and local climate for arid and semi-arid regions. Vegetation function, structure and individual behavior have large impacts on carbon-water-energy balances, which consequently influence local climate variability that, in turn, feeds back to the vegetation. In this study, a conceptual vegetation structure scheme is formulated and tested in a new carbon-water-energy coupled model to explore the importance of vegetation structure and vegetation adaptation to water stress on equilibrium biomass states. Surface energy, water and carbon fluxes are simulated for a range of vegetation structures across a precipitation gradient in West Africa and optimal vegetation structures that maximizes biomass for each precipitation regime are determined. Two different strategies of vegetation adaptation to water stress are included. Under dry conditions vegetation tries to maximize the Water Use Efficiency and Leaf Area Index as it tries to maximize carbon gain. However, an important negative feedback mechanism is found as the vegetation also tries to minimize its cover to optimize the surrounding bare ground area from which water can be extracted, thereby forming patches of vertical vegetation. Under larger precipitation, a positive feedback mechanism is found in which vegetation tries to maximize its cover as it then can reduce water loss from bare soil while having maximum carbon gain due to a large Leaf Area Index. The competition between vegetation and bare soil determines a transition between a "survival" state to a "growing" state.


2020 ◽  
Vol 12 (15) ◽  
pp. 2378
Author(s):  
Yang Song ◽  
Jinfei Wang ◽  
Jiali Shang ◽  
Chunhua Liao

Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In this study, the SOPC-LAIe maps, for the first time, were applied to the simple algorithm for yield estimation (SAFY) to generate the sub-field biomass and yield maps. First, the dry aboveground biomass (DAM) measurements were used to determine the crop cultivar-specific parameters and simulated green leaf area index (LAI) in the SAFY model. Then, the SOPC-LAIe maps were converted to green LAI using a normalization approach. Finally, the multiple SOPC-LAIe maps were applied to the SAFY model to generate the final DAM and yield maps. The root mean square error (RMSE) between the estimated and measured yield is 88 g/m2, and the relative root mean squire error (RRMSE) is 15.2%. The pixel-based DAM and yield map generated in this study revealed clearly the within-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale.


2009 ◽  
Author(s):  
Zhuo Fu ◽  
Jindi Wang ◽  
Jinling Song ◽  
Hongmin Zhou ◽  
Yong Pang ◽  
...  

2020 ◽  
Author(s):  
Jan-Peter George ◽  
Jan Pisek ◽  

<p>Leaf area index (i.e. one-half the total green leaf area per unit of horizontal ground surface area) is a crucial parameter in carbon balancing and modeling. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as in ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. The aim of this study is to compare currently available understory LAI assessment methodologies over a diverse set of greenhouse gas measurement sites distributed along a wide latitudinal and elevational gradient across Europe. This will help to quantify  the fraction of the canopy LAI which is represented by understory, since this is still the major source of uncertainty in global LAI products derived from remote sensing data. For this, we took ground photos as well as in-situ reflectance measurements of the understory vegetation at 30 ICOS (Integration Carbon Observation System) sites distributed across 10 countries in Europe. The data were analyzed by means of three conceptually different methods for LAI estimation and comprised purely empirical (fractional cover), semi-empirical (in-situ NDVI linked to the radiative transfer model FLiES), and purely deterministic (Four-scale geometrical optical model) approaches. Finally, our results are compared with global forest understory LAI maps derived from remote sensing data at 1 km resolution (Liu et al. 2017). While we found some agreement among the three methods (e.g. Pearson-correlation between empirical and semi-empirical = 0.63), we also identified sources that are particularly prone to error inclusion such as inaccurate assessment of fractional cover from ground photos. Relationships between understory LAI and long-term climate variables were weak and suggested that understory LAI at the ICOS sites is probably more strongly determined by microclimatic conditions.</p><p><strong>Liu Y. et al. (2017):</strong> Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data. Biogeosciences 14: 1093-1110.</p>


2015 ◽  
Vol 160 ◽  
pp. 118-131 ◽  
Author(s):  
Angela Kross ◽  
David R. Lapen ◽  
Heather McNairn ◽  
Mark Sunohara ◽  
Catherine Champagne ◽  
...  

2019 ◽  
Vol 165 ◽  
pp. 104867 ◽  
Author(s):  
Jan Bauer ◽  
Thomas Jarmer ◽  
Siegfried Schittenhelm ◽  
Bastian Siegmann ◽  
Nils Aschenbruck

2014 ◽  
Vol 61 (1) ◽  
pp. 5-26
Author(s):  
Tiit Nilson ◽  
Mattias Rennel ◽  
Mait Lang

Abstract. The merits and possible problems of the light use efficiency-concept based GPP/NPP models applied together with satellite images and meteorological data to quantitatively understand the role of different meteorological factors in forest productivity are analysed. A concept of the complex meteorological limiting factor for plant productivity is introduced. The factor includes the effects of incoming photosynthetically active radiation as well as the temperature and water limiting factors. Climatologically averaged seasonal courses of the complex meteorological limiting factor derived from the records of two contrasting meteorological stations in Estonia - inland Tartu/Tõravere and coastal Sõrve - are shown. Leaf phenology, here described via the seasonal course of leaf area index (LAI), is interpreted as a possible means to maximise the carbon gain under particular meteorological conditions. The equations for the optimum seasonal course of LAI as derived from the NPP model are presented. As the daily adjustment of plant LAI to sudden changes in meteorological conditions is not possible, several approximate strategies for LAI seasonal course to maximise the yearly NPP of vegetation are analysed. Typical optimal courses of LAI show some seasonal asymmetry resulting in lower values of LAI in the second half of the vegetation period due to higher air temperatures and respiration costs. Knowledge about optimum LAI courses has a cognitive value, but can also be used as the simulated LAI courses in several models when the measured LAI values are not available. As the considered GPP/NPP models fail to adequately describe the local trends in forest and agricultural productivity in Estonia, the ways to improve the model’s performance are shown.


Author(s):  
Lijuan Wang ◽  
Guimin Zhang ◽  
Hui Lin ◽  
Liang Liang ◽  
Zheng Niu

The Normalized Difference Vegetation Index (NDVI) is widely used for Leaf Area Index (LAI) estimation. It is well documented that the NDVI is extremely subject to the saturation problem when LAI reaches a high value. A new multi-angular vegetation index, the Hotspot-darkspot Difference Vegetation Index (HDVI) is proposed to estimate the high density LAI. The HDVI, defined as the difference between the hot and dark spot NDVI, relative to the dark spot NDVI, was proposed based on the Analytical two-layer Canopy Reflectance Model (ACRM) model outputs. This index is validated using both in situ experimental data in wheat and data from the multi-angular optical Compact High-Resolution Imaging Spectrometer (CHRIS) satellite. Both indices, the Hotspot-Darkspot Index (HDS) and the NDVI were also selected to analyze the relationship with LAI, and were compared with new index HDVI. The results show that HDVI is an appropriate proxy of LAI with higher determination coefficients (R2) for both the data from the in situ experiment (R2=0.7342, RMSE=0.0205) and the CHRIS data (R2=0.7749, RMSE=0.1013). Our results demonstrate that HDVI can make better the occurrence of saturation limits with the information of multi-angular observation, and is more appropriate for estimating LAI than either HDS or NDVI at high LAI values. Although the new index needs further evaluation, it also has the potential under the condition of dense canopies. It provides the effective improvement to the NDVI and other vegetation indices that are based on the red and NIR spectral bands.


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