Prediction of soil properties using a process-based forest growth model to match satellite-derived estimates of leaf area index

2012 ◽  
Vol 126 ◽  
pp. 160-173 ◽  
Author(s):  
Nicholas C. Coops ◽  
Richard H. Waring ◽  
Thomas Hilker
Keyword(s):  
Soil Properties ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Model ◽  
Forest Growth ◽  
Area Index ◽  
Forest Growth Model

scholarly journals Leaf area index mapping with optical methods and allometric models in SMEAR flux tower footprint at Järvselja, Estonia

2015 ◽  
Vol 63 (1) ◽  
pp. 85-99
Author(s):  
Marta Mõistus ◽  
Mait Lang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Growth ◽  
Height Distribution ◽  
Area Index ◽  
Flux Tower ◽  
Gap Fraction ◽  
Plant Area Index ◽  
The Relationship

AbstractLeaf area index (LAI) characterizes the amount of photosynthetically active tissue in plant canopies. LAI is one of the key factors determining ecosystem net primary production and gas and energy exchange between the canopy and the atmosphere. The aim of the present study was to test different methods for LAI and effective plant area index (PAIe) estimation in mixed hemiboreal forests in Järvselja SMEAR Estonia (Station for Measuring Ecosystem-Atmosphere Relations) flux tower footprint. We used digital hemispherical images from sample plots, forest management inventory data, allometric foliage mass models, airborne discrete-return recording laser scanner (ALS) data and multispectral satellite images. The free ware program HemiSpherical Project Manager (HSP) was used to calculate canopy gap fraction from digital hemispherical photographs taken in 25 sample plots. PAIewas calculated from the gap fraction for up-scaling based on ALS point cloud metrics. The all ALS pulse returns-based canopy transmission was found to be the most suitable lidar metric to estimate PAIein Järvselja forests. The 95-percentile (H95) of lidar point cloud height distribution correlates very well with allometric regression models based LAI and in birch stands the relationship was fitted with 0.7 m2m−2residual error. However, the relationship was specific to each allometric foliage mass model and systematic discrepancies were detected at large LAI values between the models. Relationships between the spectral reflectance and allometric LAI were not good enough to be used for LAI mapping. Therefore, airborne laser scanning data-based PAIemap was created for areas near SMEAR tower. We recommend to establish a network of permanent sample plots for forest growth and gap fraction measurements into the flux footprint of SMEAR Estonia flux tower in Järvselja to provide consistent up to date data for interpretation of the flux measurements.

scholarly journals Optimization of a coupled hydrology-crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter

2007 ◽  
Vol 43 (4) ◽  
Author(s):  
Valentijn R. N. Pauwels ◽  
Niko E. C. Verhoest ◽  
Gabriëlle J. M. De Lannoy ◽  
Vincent Guissard ◽  
Cozmin Lucau ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Model ◽  
Ensemble Kalman Filter ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Area Index

scholarly journals Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model

2020 ◽  
Vol 12 (18) ◽  
pp. 2896
Author(s):  
Wen Zhuo ◽  
Jianxi Huang ◽  
Xinran Gao ◽  
Hongyuan Ma ◽  
Hai Huang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Model ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Area Index ◽  
Maturity Date ◽  
Crop Maturity

Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction.

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