scholarly journals An optimized non-linear vegetation index for estimating leaf area index in winter wheat

2019 ◽  
Vol 20 (6) ◽  
pp. 1157-1176 ◽  
Author(s):  
Wei Feng ◽  
Yapeng Wu ◽  
Li He ◽  
Xingxu Ren ◽  
Yangyang Wang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Area Index ◽  
Non Linear

scholarly journals Joint Retrieval of Winter Wheat Leaf Area Index and Canopy Chlorophyll Density Using Hyperspectral Vegetation Indices

2021 ◽  
Vol 13 (16) ◽  
pp. 3175
Author(s):  
Naichen Xing ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
Yu Ren ◽  
Qiaoyun Xie
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Area Index

Leaf area index (LAI) and canopy chlorophyll density (CCD) are key biophysical and biochemical parameters utilized in winter wheat growth monitoring. In this study, we would like to exploit the advantages of three canonical types of spectral vegetation indices: indices sensitive to LAI, indices sensitive to chlorophyll content, and indices suitable for both parameters. In addition, two methods for joint retrieval were proposed. The first method is to develop integration-based indices incorporating LAI-sensitive and CCD-sensitive indices. The second method is to create a transformed triangular vegetation index (TTVI2) based on the spectral and physiological characteristics of the parameters. PROSAIL, as a typical radiative transfer model embedded with physical laws, was used to build estimation models between the indices and the relevant parameters. Validation was conducted against a field-measured hyperspectral dataset for four distinct growth stages and pooled data. The results indicate that: (1) the performance of the integrated indices from the first method are various because of the component indices; (2) TTVI2 is an excellent predictor for joint retrieval, with the highest R2 values of 0.76 and 0.59, the RMSE of 0.93 m2/m2 and 104.66 μg/cm2, and the RRMSE (Relative RMSE) of 12.76% and 16.96% for LAI and CCD, respectively.

scholarly journals Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 505
Author(s):  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
Water Vapor ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
Linear Regression Models ◽  
Area Index ◽  
Estimation Performance ◽  
Red Edge ◽  
Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

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 %.

Spring top-dressings of ‘Nitro-Chalk’ and late sprays of a liquid N-fertilizer and a broad spectrum fungicide for consecutive crops of winter wheat at Saxmundham, Suffolk

1978 ◽  
Vol 90 (3) ◽  
pp. 509-516 ◽  
Author(s):  
A. Penny ◽  
F. V. Widdowson ◽  
J. F. Jenkyn
Keyword(s):  
Calcium Carbonate ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Ammonium Nitrate ◽  
Leaf Area ◽  
Broad Spectrum ◽  
N Fertilizer ◽  
N Content ◽  
Area Index ◽  
Liquid Fertilizer

SummaryAn experiment at Saxmundham, Suffolk, during 1974–6, tested late sprays of a liquid N-fertilizer (ammonium nitrate/urea) supplying 50 kg N/ha, and a broad spectrum fungicide (benomyl and maneb with mancozeb) on winter wheat given, 0, 50, 100 or 150 kg N/ha as ‘Nitro-Chalk’ (ammonium nitrate/calcium carbonate) in springMildew (Erysiphe graminisf. sp. tritici) was most severe in 1974. It was increased by N and decreased by the fungicide in both 1974 and 1975, but was negligible in 1976. Septoria (S. nodorum) was very slight in 1974 and none was observed in 1976. It was much more severe in 1975, but was unaffected by the fungicide perhaps because this was applied too late.Yield and N content, number of ears and leaf area index were determined during summer on samples taken from all plots given 100 or 150 kg N/ha in spring; each was larger with 150 than with 100 kg N/ha. The effects of the liquid N-fertilizer on yield and N content varied, but leaf area index was consistently increased. None was affected consistently by the fungicide.Yields, percentages of N in, and amounts of N removed by grain and straw were greatly and consistently increased by each increment of ‘Nitro-Chalk’. Yields of grain were increased (average, 9%) by the liquid fertilizer in 1974 and 1975, and most where most ‘Nitro-Chalk’ had been given, but not in 1976 when the wheat ripened in July; however, both the percentage of N in and the amount of N removed by the grain were increased by the liquid fertilizer each year. The fungicide increased the response to the liquid N-fertilizer in 1974, but not in 1975 when Septoria was not controlled, nor in 1976 when leaf diseases were virtually absent.The weight of 1000 grains was increased by each increment of ‘Nitro-Chalk’ in 1975 but only by the first one (50 kg N/ha) in 1974 and 1976; it was very slightly increased by the liquid fertilizer and by fungicide each year.

scholarly journals Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska

2015 ◽  
Author(s):  
Santonu Goswami ◽  
John Gamon ◽  
Sergio Vargas ◽  
Craig Tweedie
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Species ◽  
Vegetation Index ◽  
Plant Biomass ◽  
Plant Cover ◽  
Strongly Correlated ◽  
Area Index ◽  
Spectral Bands ◽  
Biomass Plant

Here we investigate relationships between NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species near Barrow, Alaska. We explore how key plant species differ in biomass, leaf area index (LAI) and how can vegetation spectral indices be used to estimate biomass and LAI for key plant species. A vegetation index (VI) or a spectral vegetation index (SVI) is a quantitative predictor of plant biomass or vegetative vigor, usually formed from combinations of several spectral bands, whose values are added, divided, or multiplied in order to yield a single value that indicates the amount or vigor of vegetation. For six key plant species, NDVI was strongly correlated with biomass (R2 = 0.83) and LAI (R2 = 0.70) but showed evidence of saturation above a biomass of 100 g/m2 and an LAI of 2 m2/m2. Extrapolation of a biomass-plant cover model to a multi-decadal time series of plant cover observations suggested that Carex aquatilis and Eriophorum angustifolium decreased in biomass while Arctophila fulva and Dupontia fisheri increased 1972-2008.

scholarly journals Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2460 ◽  
Author(s):  
Yangyang Zhang ◽  
Jian Yang ◽  
Xiuguo Liu ◽  
Lin Du ◽  
Shuo Shi ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Gaussian Process Regression ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index ◽  
Look Up Table ◽  
Band Combinations

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.

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