scholarly journals Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data

2018 ◽  
Vol 75 (1) ◽  
Author(s):  
Xiaojun Xu ◽  
Huaqiang Du ◽  
Guomo Zhou ◽  
Fangjie Mao ◽  
Xuejian Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Moso Bamboo ◽  
Phyllostachys Edulis ◽  
Area Index ◽  
Remote Estimation ◽  
Reflectance Data

Estimation of Leaf Area Index of Moso Bamboo Canopies

2021 ◽  
pp. 1-16
Author(s):  
Feilong Huang ◽  
Weiliang Fan ◽  
Huaqiang Du ◽  
Xiaojun Xu ◽  
Jun Wu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Moso Bamboo ◽  
Area Index

Estimation of leaf area index and leaf chlorophyll content in Sporobolus densiflorus using hyperspectral measurements and PROSAIL model simulations

2020 ◽  
Vol 42 (4) ◽  
pp. 1181-1200
Author(s):  
Estefanía Piegari ◽  
Juan I. Gossn ◽  
Francisco Grings ◽  
Verónica Barraza Bernadas ◽  
Ángela B. Juárez ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Area Index ◽  
Model Simulations ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll

scholarly journals Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data

2019 ◽  
Vol 11 (21) ◽  
pp. 2517 ◽  
Author(s):  
Huaan Jin ◽  
Weixing Xu ◽  
Ainong Li ◽  
Xinyao Xie ◽  
Zhengjian Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Area Index ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Modis Lai ◽  
Temporal Profiles ◽  
Reflectance Data

As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.

scholarly journals Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

2015 ◽  
Vol 159 ◽  
pp. 203-221 ◽  
Author(s):  
Rasmus Houborg ◽  
Matthew McCabe ◽  
Alessandro Cescatti ◽  
Feng Gao ◽  
Mitchell Schull ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Landsat Data ◽  
Area Index ◽  
Model Inversion ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
Regularized Model

Retrieval of leaf chlorophyll content in field crops using narrow-band indices: effects of leaf area index and leaf mean tilt angle

2015 ◽  
Vol 36 (24) ◽  
pp. 6031-6055 ◽  
Author(s):  
Xiaochen Zou ◽  
Rocío Hernández-Clemente ◽  
Priit Tammeorg ◽  
Clara Lizarazo Torres ◽  
Frederick L. Stoddard ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Tilt Angle ◽  
Narrow Band ◽  
Area Index ◽  
Leaf Chlorophyll Content ◽  
Field Crops ◽  
Leaf Chlorophyll

scholarly journals Effect of Organic Manures and Inorganic Fertilizer on the Leaf Characters of Banana

2021 ◽  
pp. 91-96
Author(s):  
Jaiz Isfaqure Rahman ◽  
D. N. Hazarika ◽  
D. Bhattacharjee
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Research Work ◽  
Inorganic Fertilizer ◽  
Microbial Consortia ◽  
Vegetative Stage ◽  
Area Index ◽  
Organic Manures ◽  
Content Index

A field experiment was carried out at Instructional cum Research Farm, Department of Horticulture, Biswanath College of Agriculture, AAU, Biswanath Chariali to study the effects of organic manures and inorganic fertilizer on leaf characters of banana cv. Amritsagar (AAA) during 2016-2017. The research work was carried out with the treatments as follows T1: FYM (Farm Yard Manure) + Microbial Consortia, T2: Enriched Compost, T3: Vermicompost, T4: Microbial Consortia, T0: RDF (FYM + NPK). Healthy suckers were planted in each plot with spacing of 2.1m x 2.1m on 27th May 2016. The treatments T1, T2, T3 and T4 were laid out in certified organic block in RBD with 5 replications while the treatment T0 was laid out outside the organic block with five replications. In the organics, T1 recorded the highest number of functional leaves (7.97, 12.46 and 5.37) in vegetative stage, shooting stage and harvesting stage respectively. Highest leaf area of 2.69 m2 at vegetative stage and 11.17 m2 at shooting stage were recorded in T1 while lowest leaf area of 2.41 m2 at vegetative stage and 8.89 m2 at shooting stage were recorded in T4. Leaf area index was highest in T1. Chlorophyll content index in both vegetative stage (45.29) and shooting stage (65.56) was also highest in T1. Comparing the leaf characters (number of functional leaves, leaf area, leaf area index and chlorophyll content index) under organic treatments with that of T0 treated plants, it was found that plants treated with inorganic fertilizer had more number of functional leaves and better leaf character than that of the plants treated with organics.

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.

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