Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model

2010 ◽  
Vol 53 (5) ◽  
pp. 721-730 ◽  
Author(s):  
DongWei Wang ◽  
JinDi Wang ◽  
ShunLin Liang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Model ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Area Index ◽  
Modis Data

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.

scholarly journals Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

2020 ◽  
Author(s):  
Francesco Novelli ◽  
Heide Spiegel ◽  
Taru Sandén ◽  
Francesco Vuolo
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Field Data ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Area Index ◽  
Sentinel 2

<p>The work is based on a previously published study with the aim to further analyse the results obtained. Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields.<br>LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. Better RMSE and RRMSE were obtained in 2017 compared to 2016 (RMSE = 0.44 vs. 0.46) (RRMSE = 17% vs. 19%). In 2016 year, a slightly lower R<sup>2</sup> value was found compared to 2017 (R<sup>2</sup> = 0.72 vs. 0.89). A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The work shows that the assimilation of remote sensing data into the crop growth model can help to overtake some structural problems of the model.  The assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.</p>

scholarly journals Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

2018 ◽  
Vol 11 (7) ◽  
pp. 2789-2812 ◽  
Author(s):  
Werner von Bloh ◽  
Sibyll Schaphoff ◽  
Christoph Müller ◽  
Susanne Rolinski ◽  
Katharina Waha ◽  
...  
Keyword(s):  
Growth Model ◽  
Nitrogen Cycle ◽  
Crop Productivity ◽  
Crop Growth ◽  
Limiting Factors ◽  
Full Detail ◽  
Crop Growth Model ◽  
Maintenance Respiration ◽  
Area Index ◽  
Global Vegetation

Abstract. The well-established dynamical global vegetation, hydrology, and crop growth model LPJmL is extended with a terrestrial nitrogen cycle to account for nutrient limitations. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included in the model. All new model features are described in full detail and the results of a global simulation of the historic past (1901–2009) are presented for evaluation of the model performance. We find that the implementation of nitrogen limitation significantly improves the simulation of global patterns of crop productivity. Regional differences in crop productivity, which had to be calibrated via a scaling of the maximum leaf area index, can now largely be reproduced by the model, except for regions where fertilizer inputs and climate conditions are not the yield-limiting factors. Furthermore, it can be shown that land use has a strong influence on nitrogen losses, increasing leaching by 93 %.

scholarly journals Growth and yield of wheat at different dates of sowing under chrland ecosystem of Bangladesh

2017 ◽  
Vol 14 (2) ◽  
pp. 147-154 ◽  
Author(s):  
MM Kamrozzaman ◽  
MAH Khan ◽  
S Ahmed ◽  
N Sultana
Keyword(s):  
Growth Rate ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Dry Matter ◽  
Block Design ◽  
Grain Filling ◽  
Crop Growth ◽  
Growth And Yield ◽  
Area Index ◽  
Crop Growth Rate

An experiment was conducted at Sadipur charland under Farming System Research and Development Site, Hatgobindapur, Faridpur, during rabi season of 2012-13 and 2013-14 to study the growth and yield performance of cv. BARI Gom-24 as affected by different dates of sowing under Agro-ecological Zone-12 (AEZ-12) of Bangladesh. The experiment was laid out in randomized complete block design with six replications, comprising five different dates of sowing viz. November 5, November 15, November 25, December 5 and December 15. Results reveal that the tallest plant, leaf area index, total dry matter, and crop growth rate were observed in November 25 sown crop and leaf area index, total dry matter and crop growth rate were higher at booting, grain filling, and tillering stages of the crop. Maximum effective tillers hill-1 (3.49), spikes m-2, (311), number of grains spike-1 (42.20) and 1000-grain weight (52.10 g) were produced by November 25 sown crop exhibited the highest grain (4.30 t ha-1) and straw yield (4.94 t ha-1) as well as harvest index (46.88%) of the crop. Lowest performance was observed both in early (November 5) and late sown crop (December 15). The overall results indicated that November 25 sown crop showed better performance in respect of growth and yield of wheat under charland ecosystem of Bangladesh.J. Bangladesh Agril. Univ. 14(2): 147-154, December 2016

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