Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices

2021 ◽  
Vol 252 ◽  
pp. 106906
Author(s):  
Guomin Shao ◽  
Wenting Han ◽  
Huihui Zhang ◽  
Shouyang Liu ◽  
Yi Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Crop Coefficient ◽  
Random Forest Algorithm ◽  
Maize Crop ◽  
Area Index

scholarly journals A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

2021 ◽  
Vol 14 (1) ◽  
pp. 98
Author(s):  
Quanjun Jiao ◽  
Qi Sun ◽  
Bing Zhang ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Leaf Angle ◽  
Transfer Model ◽  
Growth Monitoring ◽  
Random Forest Algorithm ◽  
Area Index ◽  
Important Indicator

Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALAadj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALAadj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm−2; R2 from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm−2; R2 from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm−2; R2 from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALAadj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.

scholarly journals The effect of using different vegetation indices for mangrove leaf area index modelling

2020 ◽  
Vol 500 ◽  
pp. 012006
Author(s):  
Aldo Restu Agi Prananda ◽  
Muhammad Kamal ◽  
Denny Wijaya Kusuma
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Area Index

scholarly journals Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops

2015 ◽  
Vol 34 ◽  
pp. 235-248 ◽  
Author(s):  
Angela Kross ◽  
Heather McNairn ◽  
David Lapen ◽  
Mark Sunohara ◽  
Catherine Champagne
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Area Index

scholarly journals Narrowband Vegetation Indices for Estimating Boreal Forest Leaf Area Index

10.5772/31160 ◽  
2012 ◽  
Author(s):  
Ellen Eigemeier ◽  
Janne Heiskanen ◽  
Miina Rautiainen ◽  
Matti Mttus ◽  
Veli-Heikki Vesanto ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Area Index

Red-Edge Band Vegetation Indices for Leaf Area Index Estimation From Sentinel-2/MSI Imagery

2020 ◽  
Vol 58 (2) ◽  
pp. 826-840 ◽  
Author(s):  
Yuanheng Sun ◽  
Qiming Qin ◽  
Huazhong Ren ◽  
Tianyuan Zhang ◽  
Shanshan Chen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Area Index ◽  
Red Edge ◽  
Index Estimation ◽  
Edge Band ◽  
Sentinel 2

scholarly journals Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6732
Author(s):  
Haixia Qi ◽  
Bingyu Zhu ◽  
Zeyu Wu ◽  
Yu Liang ◽  
Jianwen Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Field Experiments ◽  
Predictive Accuracy ◽  
Vegetation Indices ◽  
Planting Density ◽  
Coefficient Of Determination ◽  
Multispectral Images ◽  
Area Index

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

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