scholarly journals Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

2021 ◽  
Vol 13 (6) ◽  
pp. 1144
Author(s):  
Mahendra Bhandari ◽  
Shannon Baker ◽  
Jackie C. Rudd ◽  
Amir M. H. Ibrahim ◽  
Anjin Chang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Grain Filling ◽  
Unmanned Aerial Systems ◽  
High Temporal Resolution ◽  
Wheat Growth ◽  
Spatial Domains ◽  
Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

scholarly journals Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1842
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski ◽  
Michał Stępień ◽  
Stanisław Samborski ◽  
Dominik Ruciński ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
Soil Nutrient ◽  
Growth Stages ◽  
Plant Growth Stages

The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha−1.

scholarly journals Retrieving the Diurnal FPAR of a Maize Canopy from the Jointing Stage to the Tasseling Stage with Vegetation Indices under Different Water Stresses and Light Conditions

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3965 ◽  
Author(s):  
Liang Zhao ◽  
Zhigang Liu ◽  
Shan Xu ◽  
Xue He ◽  
Zhuoya Ni ◽  
...  
Keyword(s):  
Regression Models ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Quadratic Function ◽  
High Temporal Resolution ◽  
Area Index ◽  
Red Edge ◽  
Jointing Stage ◽  
Simple Ratio

The fraction of absorbed photosynthetically active radiation (FPAR) is a key variable in the model of vegetation productivity. Vegetation indices (VIs) that were derived from instantaneous remote-sensing data have been successfully used to estimate the FPAR of a day or a longer period. However, it has not yet been verified whether continuous VIs can be used to accurately estimate the diurnal dynamics of a vegetation canopy FPAR, which may fluctuate dramatically within a day. In this study, we measured the high temporal resolution spectral data (480 to 850 nm) and FPAR data of a maize canopy from the jointing stage to the tasseling stage under different irrigation and illumination conditions using two automatic observation systems. To estimate the FPAR, we developed regression models based on a quadratic function using 13 kinds of VIs. The results show the following: (1) Under nondrought conditions, although the illumination condition (sunny or cloudy) influenced the trend of the canopy diurnal FPAR, it had only a slight effect on the model accuracies of the FPAR-VIs. The maximum coefficients of determination (R2) of the FPAR-VIs models generated for the sunny nondrought data, the cloudy nondrought data, and all of the nondrought data were 0.895, 0.88, and 0.828, respectively. The VIs—including normalized difference vegetation index (NDVI), green NDVI (GNDVI), red-edge simple ratio (SR705), modified simple ratio 2 (mSR2), red-edge normalized difference vegetation index (NDVI705), and enhanced vegetation index (EVI)—that were related to the canopy structure had higher estimation accuracies (R2 > 0.8) than the other VIs that were related to the soil adjustment, chlorophyll, and physiology. The estimation accuracies of the GNDVI and some red-edge VIs (including NDVI705, SR705, and mSR2) were higher than the estimation accuracy of the NDVI. (2) Under drought stress, the FPAR decreased significantly because of leaf wilting and the effective leaf area index decrease around noon. When we included drought data in the model, accuracies were reduced dramatically and the R2 value of the best model was only 0.59. When we built the regression models based only on drought data, the EVI, which can weaken the influence of soil, had the best estimate accuracy (R2 = 0.68).

scholarly journals Coupling Remote Sensing Data and AquaCrop Model for Simulation of Winter Wheat Growth under Rainfed and Irrigated Conditions in a Mediterranean Environment

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2265
Author(s):  
Marie Therese Abi Saab ◽  
Razane El Alam ◽  
Ihab Jomaa ◽  
Sleiman Skaf ◽  
Salim Fahed ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Canopy Cover ◽  
Mediterranean Environment ◽  
Wheat Growth ◽  
Aquacrop Model ◽  
Rainfed Conditions ◽  
Sentinel 2

The coupling of remote sensing technology and crop growth models represents a promising approach to support crop yield prediction and irrigation management. In this study, five vegetation indices were derived from the Copernicus-Sentinel 2 satellite to investigate their performance monitoring winter wheat growth in a Mediterranean environment in Lebanon’s Bekaa Valley. Among those indices, the fraction of canopy cover was integrated into the AquaCrop model to simulate biomass and yield of wheat grown under rainfed conditions and fully irrigated regimes. The experiment was conducted during three consecutive growing seasons (from 2017 to 2019), characterized by different precipitation patterns. The AquaCrop model was calibrated and validated for different water regimes, and its performance was tested when coupled with remote sensing canopy cover. The results showed a good fit between measured canopy cover and Leaf Area Index (LAI) data and those derived from Sentinel 2 images. The R2 coefficient was 0.79 for canopy cover and 0.77 for LAI. Moreover, the regressions were fitted to relate biomass with Sentinel 2 vegetation indices. In descending order of R2, the indices were ranked: Fractional Vegetation Cover (FVC), LAI, the fraction of Absorbed Photosynthetically Active Radiation (fAPAR), the Normalized Difference Vegetation Index (NDVI), and the Enhanced Vegetation Index (EVI). Notably, FVC and LAI were highly correlated with biomass. The results of the AquaCrop calibration showed that the modeling efficiency values, NSE, were 0.99 for well-watered treatments and 0.95 for rainfed conditions, confirming the goodness of fit between measured and simulated values. The validation results confirmed that the simulated yield varied from 2.59 to 5.36 t ha−1, while the measured yield varied from 3.08 to 5.63 t ha−1 for full irrigation and rainfed treatments. After integrating the canopy cover into AquaCrop, the % of deviation of simulated and measured variables was reduced. The Root Mean Square Error (RMSE) for yield ranged between 0.08 and 0.69 t ha−1 before coupling and between 0.04 and 0.42 t ha−1 after integration. This result confirmed that the presented integration framework represents a promising method to improve the prediction of wheat crop growth in Mediterranean areas. Further studies are needed before being applied on a larger scale.

scholarly journals Evaluation of RapidEye vegetation indices for prediction of biophysical/biochemical variables of winter wheat

2018 ◽  
Vol 30 ◽  
pp. 63-74
Author(s):  
Ilina Kamenova ◽  
Petar Dimitrov ◽  
Rusina Yordanova
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Linear Models ◽  
Chlorophyll Concentration ◽  
Vegetation Indices ◽  
Area Index ◽  
Leaf Chlorophyll ◽  
Red Edge ◽  
Absorbed Photosynthetically Active Radiation

The aim of the study is to evaluate the possibility for using RapidEye data for prediction of Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR), fraction of vegetation Cover (fCover), leaf Chlorophyll Concentration (CC) and Canopy Chlorophyll Content (CCC) of winter wheat. The relation of a number of vegetation indices (VIs) with these crop variables are accessed based on a regression analysis. Indices, which make use of the red edge band, such as Chlorophyll Index red edge (CIre) and red edge Normalized Difference Vegetation Index (reNDVI), were found most useful, resulting in linear models with R2 of 0.67, 0.71, 0.72, and 0.76 for fCover, LAI, CCC, and fAPAR respectively. CC was not related with any of the VIs.

scholarly journals Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor

2021 ◽  
Vol 13 (4) ◽  
pp. 577
Author(s):  
Per-Ola Olsson ◽  
Ashish Vivekar ◽  
Karl Adler ◽  
Virginia E. Garcia Millan ◽  
Alexander Koc ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Large Degree ◽  
Sensor Data ◽  
Flying Height ◽  
Unmanned Aerial Systems ◽  
Radiometric Correction ◽  
The Individual

Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R2 was 0.80–0.97 for the red-edge, near-infrared, and red bands.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winter wheat under terminal heat stress

2012 ◽  
Vol 131 (6) ◽  
pp. 716-721 ◽  
Author(s):  
Shahnoza Hazratkulova ◽  
Ram C. Sharma ◽  
Safar Alikulov ◽  
Sarvar Islomov ◽  
Tulkin Yuldashev ◽  
...  
Keyword(s):  
Heat Stress ◽  
Winter Wheat ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Genotypic Variation ◽  
Terminal Heat Stress

scholarly journals A comparison of satellite-based indices for hazard assessment of MSW open dumps using spatial analysis

2018 ◽  
Vol 37 (3) ◽  
pp. 219-236 ◽  
Author(s):  
Khalid Mahmood ◽  
Zia Ul-Haq ◽  
Fiza Faizi ◽  
Syeda A. Batol
Keyword(s):  
Hazard Assessment ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Moving Average ◽  
Vegetation Indices ◽  
Temporal Analysis ◽  
Zone Size ◽  
Vegetation Health ◽  
Spatio Temporal ◽  
Hazardous Zone

This study compares the suitability of different satellite-based vegetation indices (VIs) for environmental hazard assessment of municipal solid waste (MSW) open dumps. The compared VIs, as bio-indicators of vegetation health, are normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI) that have been subject to spatio-temporal analysis. The comparison has been made based on three criteria: one is the exponential moving average (EMA) bias, second is the ease in visually finding the distance of VI curve flattening, and third is the radius of biohazardous zone in relation to the waste heap dumped at them. NDVI has been found to work well when MSW dumps are surrounded by continuous and dense vegetation, otherwise, MSAVI is a better option due to its ability for adjusting soil signals. The hierarchy of the goodness for least EMA bias is MSAVI> SAVI> NDVI with average bias values of 101 m, 203 m, and 270 m, respectively. Estimations using NDVI have been found unable to satisfy the direct relationship between waste heap and hazardous zone size and have given a false exaggeration of 374 m for relatively smaller dump as compared to the bigger one. The same false exaggeration for SAVI and MSAVI is measured to be 86 m and -14 m, respectively. So MSAVI is the only VI that has shown the true relation of waste heap and hazardous zone size. The best visualization of distance-dependent vegetation health away from the dumps is also provided by MSAVI.

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