scholarly journals Sensitivity of ground heat flux to vegetation cover fraction and leaf area index

1999 ◽  
Vol 104 (D16) ◽  
pp. 19505-19514 ◽  
Author(s):  
Z-L. Yang ◽  
Y. Dai ◽  
R. E. Dickinson ◽  
W. J. Shuttleworth
Keyword(s):  
Heat Flux ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Cover ◽  
Area Index ◽  
Ground Heat Flux

scholarly journals Plant- and Soil-Parameter-Caused Uncertainty of Predicted Surface Fluxes

2005 ◽  
Vol 133 (12) ◽  
pp. 3498-3516 ◽  
Author(s):  
Nicole Mölders
Keyword(s):  
Heat Flux ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Stomatal Resistance ◽  
Surface Fluxes ◽  
Critical Parameters ◽  
Net Radiation ◽  
Area Index ◽  
Vegetation Fraction ◽  
Ground Heat Flux

Abstract Simulated surface fluxes depend on one or more empirical plant or soil parameters that have a standard deviation (std dev). Thus, simulated fluxes will have a stochastic error (or std dev) resulting from the parameters’ std dev. Gaussian error propagation (GEP) principles are used to calculate the std dev for fluxes predicted by the hydro–thermodynamic soil–vegetation scheme to identify prediction limitations due to stochastic errors, parameterization weaknesses, and critical parameters, and to prioritize which parameters to measure with higher accuracy. Relative errors of net radiation, sensible, latent, and ground heat flux, on average, are 7%, 10%, 6%, and 26%, respectively. The analysis identified the parameterization of thermal conductivity as the dominant influence on the std dev of ground heat flux. For net radiation, critical parameters are vegetation fraction and ground emissivity; for sensible and latent heat fluxes, vegetation fraction. Minimum stomatal resistance and leaf area index dominate the std dev of stomatal resistance for most vegetation and soil types. The empirical parameters with the highest relative error are not necessarily the greatest contributors to the std dev of the predicted flux. Based on the analysis high priority should be given to measurements of vegetation fraction, ground emissivity, minimum stomatal resistance, leaf area index in general, and the permanent wilting point and field capacity for clay and clay loam. Moreover, further specification of clay-type soils and tundra-type vegetation may improve the accuracy of the lower boundary condition in Arctic numerical weather prediction. Since GEP showed itself able to identify critical parameters and (parts of) parameterizations, GEP analysis could form a basis for parameterization intercomparisons and for parameter determination aimed at improving models.

scholarly journals Representing winter wheat in the Community Land Model (version 4.5)

2017 ◽  
Vol 10 (5) ◽  
pp. 1873-1888 ◽  
Author(s):  
Yaqiong Lu ◽  
Ian N. Williams ◽  
Justin E. Bagley ◽  
Margaret S. Torn ◽  
Lara M. Kueppers
Keyword(s):  
Heat Flux ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Latent Heat Flux ◽  
Carbon Allocation ◽  
Net Ecosystem Exchange ◽  
Area Index ◽  
Community Land Model ◽  
The Us

Abstract. Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35 % during the spring growing season. The model accurately simulated the interannual variation in yield at the US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

scholarly journals Combination of Linear Regression Lines to Understand the Response of Sentinel-1 Dual Polarization SAR Data with Crop Phenology—Case Study in Miyazaki, Japan

2020 ◽  
Vol 12 (1) ◽  
pp. 189 ◽  
Author(s):  
Emal Wali ◽  
Masahiro Tasumi ◽  
Masao Moriyama
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Cover ◽  
Polynomial Regression ◽  
Backscattering Coefficient ◽  
Biophysical Parameters ◽  
Area Index ◽  
Dry Biomass ◽  
Green Vegetation

This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage.

Spatial variability of aboveground net primary production for a forested landscape in northern Wisconsin

2003 ◽  
Vol 33 (10) ◽  
pp. 2007-2018 ◽  
Author(s):  
S N Burrows ◽  
S T Gower ◽  
J M Norman ◽  
G Diak ◽  
D S Mackay ◽  
...  
Keyword(s):  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Patterns ◽  
Vegetation Cover ◽  
Land Surface ◽  
Net Primary Production ◽  
Mixed Forest ◽  
Forested Wetlands ◽  
Area Index

Quantifying forest net primary production (NPP) is critical to understanding the global carbon cycle because forests are responsible for a large portion of the total terrestrial NPP. The objectives of this study were to measure above ground NPP (NPPA) for a land surface in northern Wisconsin, examine the spatial patterns of NPPA and its components, and correlate NPPA with vegetation cover types and leaf area index. Mean NPPA for aspen, hardwoods, mixed forest, upland conifers, nonforested wetlands, and forested wetlands was 7.8, 7.2, 5.7, 4.9, 5.0, and 4.5 t dry mass·ha–1·year–1, respectively. There were significant (p = 0.01) spatial patterns in wood, foliage, and understory NPP components and NPPA (p = 0.03) when the vegetation cover type was included in the model. The spatial range estimates for the three NPP components and NPPA differed significantly from each other, suggesting that different factors are influencing the components of NPP. NPPA was significantly correlated with leaf area index (p = 0.01) for the major vegetation cover types. The mean NPPA for the 3 km × 2 km site was 5.8 t dry mass·ha–1·year–1.

Early regeneration conifer identification and competition cover assessment using airborne digital camera imagery

2000 ◽  
Vol 76 (6) ◽  
pp. 915-928 ◽  
Author(s):  
K. A. Haddow ◽  
D. J. King ◽  
D. A. Pouliot ◽  
D. G. Pitt ◽  
F. W. Bell
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Cover ◽  
Early Stage ◽  
Digital Camera ◽  
Vegetation Management ◽  
Forest Vegetation ◽  
Crop Species ◽  
Area Index ◽  
Forest Vegetation Management

The potential of low cost, high-resolution airborne digital camera imagery for use in early stage forest regeneration assessment was investigated. Airborne imagery with 2.5-cm pixel size was acquired near Sault Ste. Marie, Ontario, over a forest vegetation management research site to: i) evaluate capabilities for identification and stem counting of two-year old conifer crop species under leaf-off and leaf-on conditions using classification of spectral and textural image information, and ii) develop models relating vegetation cover parameters to image spectral and texture information. Results indicate strong potential for identification and counting of conifer trees when competing vegetation cover is low or in leaf-off condition. However, systematic decreases in class separability and conifer count accuracy were observed with increasing competition. In image modelling of competition Leaf Area Index and Cover, statistically significant relations were found using primarily spectral measures. Stratification by competition species improved model fits and included texture measures in some models. Key words: airborne remote sensing, forest vegetation management, regeneration, digital cameras, leaf area index, cover, tree classification

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