scholarly journals IN-SEASON ASSESSMENT OF WHEAT CROP HEALTH USING VEGETATION INDICES BASED ON GROUND MEASURED HYPER SPECTRAL DATA

2014 ◽  
Vol 9 (2) ◽  
pp. 138-146 ◽  
Author(s):  
Al-Gaadi
Keyword(s):  
Spectral Data ◽  
Vegetation Indices ◽  
Wheat Crop ◽  
Hyper Spectral ◽  
Crop Health

scholarly journals Mapping Wheat Dry Matter and Nitrogen Content Dynamics and Estimation of Wheat Yield Using UAV Multispectral Imagery Machine Learning and a Variety-Based Approach: Case Study of Morocco

2021 ◽  
Vol 3 (1) ◽  
pp. 29-49
Author(s):  
Ghizlane Astaoui ◽  
Jamal Eddine Dadaiss ◽  
Imane Sebari ◽  
Samir Benmansour ◽  
Ettarid Mohamed
Keyword(s):  
Random Forest ◽  
Nitrogen Uptake ◽  
Dry Matter ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Wheat Crop ◽  
Multispectral Imagery ◽  
Biophysical Parameters ◽  
Phenological Stages ◽  
Wheat Varieties

Our work aims to monitor wheat crop using a variety-based approach by taking into consideration four different phenological stages of wheat crop development. In addition to highlighting the contribution of Red-Edge vegetation indices in mapping wheat dry matter and nitrogen content dynamics, as well as using Random Forest regressor in the estimation of wheat yield, dry matter and nitrogen uptake relying on UAV (Unmanned Aerial Vehicle) multispectral imagery. The study was conducted on an experimental platform with 12 wheat varieties located in Sidi Slimane (Morocco). Several flight missions were conducted using eBee UAV with MultiSpec4C camera according to phenological growth stages of wheat. The proposed methodology is subdivided into two approaches, the first aims to find the most suitable vegetation index for wheat’s biophysical parameters estimation and the second to establish a global model regardless of the varieties to estimate the biophysical parameters of wheat: Dry matter and nitrogen uptake. The two approaches were conducted according to six main steps: (1) UAV flight missions and in-situ data acquisition during four phenological stages of wheat development, (2) Processing of UAV multispectral images which enabled us to elaborate the vegetation indices maps (RTVI, MTVI2, NDVI, NDRE, GNDVI, GNDRE, SR-RE et SR-NIR), (3) Automatic extraction of plots by Object-based image analysis approach and creating a spatial database combining the spectral information and wheat’s biophysical parameters, (4) Monitoring wheat growth by generating dry biomass and wheat’s nitrogen uptake model using exponential, polynomial and linear regression for each variety this step resumes the varietal approach, (5) Engendering a global model employing both linear regression and Random Forest technique, (6) Wheat yield estimation. The proposed method has allowed to predict from 1 up to 21% difference between actual and estimated yield when using both RTVI index and Random Forest technique as well as mapping wheat’s dry biomass and nitrogen uptake along with the nitrogen nutrition index (NNI) and therefore facilitate a careful monitoring of the health and the growth of wheat crop. Nevertheless, some wheat varieties have shown a significant difference in yield between 2.6 and 3.3 t/ha.

Effect of multi-level and multi-scale spectral data source on vineyard state assessment via spectral vegetation indices

2021 ◽  
Author(s):  
Antonello Bonfante ◽  
Arturo Erbaggio ◽  
Eugenia Monaco ◽  
Rossella Albrizio ◽  
Pasquale Giorio ◽  
...  
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
State Estimation ◽  
Spectral Data ◽  
Spatial Resolution ◽  
Spectral Region ◽  
Vegetation Indices ◽  
Plant Management ◽  
Spectral Vegetation Indices ◽  
Data Source

<p>Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality, under climate change conditions. Climate change is one of the major challenges for high incomes crops, as the vineyards for high-quality wines, since it is expected to drastically modify plant growth, with possible negative effects especially in arid and semi-arid regions of Europe. In this context, the reduction of negative environmental impacts of intensive agriculture (e.g. soil degradation), can be realized by means of high spatial and temporal resolution of field crop monitoring, aiming to manage the local spatial variability.</p><p>The monitoring of spatial behaviour of plants during the growing season represents an opportunity to improve the plant management, the farmer incomes and to preserve the environmental health, but it represents an additional cost for the farmer.</p><p>The UAS-based imagery might provide detailed and accurate information across visible and near infrared spectral regions to support monitoring (crucial for precision agriculture) with limitation in bands and then on spectral vegetation indices (Vis) provided. VIs are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. While differences in imagery and point-based spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500nm with spatial resolution of <2m) through Convolutional Neural Network (CNN) approach (Brook et al., 2020), UAS-based multispectral (5 bands across 450-800nm spectral region with spatial resolution of 5cm) imagery and point-based field spectroscopy (collecting 600 wavelength across  400-1000nm spectral region with a surface footprint of 1-2cm) in application to crop state estimation.</p><p>The test site is a portion of vineyard placed in southern Italy cultivated on Greco cultivar, in which the soil-plant and atmosphere system has been monitored during the 2020 vintage also through ecophysiological analyses. The data analysis will follow the methodology presented in a recently published paper (Polinova et al., 2018).</p><p>The study will connect the method and scale of spectral data collection with in vivo plant monitoring and prove that it has a significant impact on the vegetation state estimation results. It should be noted that each spectral data source has its advantages and drawbacks. The plant parameter of interest should determine not only the VIs type suitable for analysis but also the method of data collection.</p><p>The contribution has been realized within the CNR BIO-ECO project.</p>

scholarly journals Estimating Nitrogen from Structural Crop Traits at Field Scale—A Novel Approach Versus Spectral Vegetation Indices

2019 ◽  
Vol 11 (17) ◽  
pp. 2066 ◽  
Author(s):  
Nora Tilly ◽  
Georg Bareth
Keyword(s):  
Spectral Data ◽  
Spring Barley ◽  
Vegetation Indices ◽  
Structural Data ◽  
Crop Growth ◽  
Negative Consequences ◽  
Field Scale ◽  
Crop Height ◽  
N Concentration ◽  
Dry Biomass

A sufficient nitrogen (N) supply is mandatory for healthy crop growth, but negative consequences of N losses into the environment are known. Hence, deeply understanding and monitoring crop growth for an optimized N management is advisable. In this context, remote sensing facilitates the capturing of crop traits. While several studies on estimating biomass from spectral and structural data can be found, N is so far only estimated from spectral features. It is well known that N is negatively related to dry biomass, which, in turn, can be estimated from crop height. Based on this indirect link, the present study aims at estimating N concentration at field scale in a two-step model: first, using crop height to estimate biomass, and second, using the modeled biomass to estimate N concentration. For comparison, N concentration was estimated from spectral data. The data was captured on a spring barley field experiment in two growing seasons. Crop surface height was measured with a terrestrial laser scanner, seven vegetation indices were calculated from field spectrometer measurements, and dry biomass and N concentration were destructively sampled. In the validation, better results were obtained with the models based on structural data (R2 < 0.85) than on spectral data (R2 < 0.70). A brief look at the N concentration of different plant organs showed stronger dependencies on structural data (R2: 0.40–0.81) than on spectral data (R2: 0.18–0.68). Overall, this first study shows the potential of crop-specific across‑season two-step models based on structural data for estimating crop N concentration at field scale. The validity of the models for in-season estimations requires further research.

SPATIAL TECHNOLOGIES IN PASTURE AND RANGE MANAGEMENT: A REVIEW

2021 ◽  
Vol 19 (1) ◽  
pp. 168-185
Author(s):  
J.A. OYEDEPO ◽  
O.S. ONIFADE
Keyword(s):  
Remote Sensing ◽  
Information Science ◽  
Vegetation Indices ◽  
Rangeland Management ◽  
Geographical Information ◽  
Specific Reference ◽  
Range Management ◽  
Grazing Land ◽  
Hyper Spectral ◽  
Complex Models

This paper looked at practical ways in which pasture and range management (P&RM) can benefit from application of spatial technologies; namely Satellite Remote Sensing, Global Positioning System and Geographical Information Science. Brief mention of these spatial technologies’ components and ways of their integrations (linear, interactive, hierarchical and complex models) were discussed with specific reference to P&RM. The paper also dwells on salient principles of applied remote sensing and geospatial technics in P&RM using examples and case studies revolving around rangeland management, spatial decision support and resource conservation. Specifically, the relevance of hyper spectral imageries and vegetation indices in cattle population and range roaming determination, grazing land and paddock site-specific management were demonstrated. It is hoped that the review will create awareness for the inclusion and use of remote sensing and geospatial technics in many areas of livestock management in Nigeria.      

scholarly journals Influence of Soil Background on Spectral Reflectance of Winter Wheat Crop Canopy

2019 ◽  
Vol 11 (16) ◽  
pp. 1932 ◽  
Author(s):  
Elena Prudnikova ◽  
Igor Savin ◽  
Gretelerika Vindeker ◽  
Praskovia Grubina ◽  
Ekaterina Shishkonakova ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Soil Type ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Soil Surface ◽  
Wheat Crop ◽  
Crop Canopy ◽  
Winter Wheat Crop ◽  
Type Factor

The spectral reflectance of crop canopy is a spectral mixture, which includes soil background as one of the components. However, as soil is characterized by substantial spatial variability and temporal dynamics, its contribution to the spectral reflectance of crops will also vary. The aim of the research was to determine the impact of soil background on spectral reflectance of crop canopy in visible and near-infrared parts of the spectrum at different stages of crop development and how the soil type factor and the dynamics of soil surface affect vegetation indices calculated for crop assessment. The study was conducted on three test plots with winter wheat located in the Tula region of Russia and occupied by three contrasting types of soil. During field trips, information was collected on the spectral reflectance of winter wheat crop canopy, winter wheat leaves, weeds and open soil surface for three phenological phases (tillering, shooting stage, milky ripeness). The assessment of the soil contribution to the spectral reflectance of winter wheat crop canopy was based on a linear spectral mixture model constructed from field data. This showed that the soil background effect is most pronounced in the regions of 350–500 nm and 620–690 nm. In the shooting stage, the contribution of the soil prevails in the 620–690 nm range of the spectrum and the phase of milky ripeness in the region of 350–500 nm. The minimum contribution at all stages of winter wheat development was observed at wavelengths longer than 750 nm. The degree of soil influence varies with soil type. Analysis of variance showed that normalized difference vegetation index (NDVI) was least affected by soil type factor, the influence of which was about 30%–50%, depending on the stage of winter wheat development. The influence of soil type on soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI2) was approximately equal and varied from 60% (shooting phase) to 80% (tillering phase). According to the discriminant analysis, the ability of vegetation indices calculated for winter wheat crop canopy to distinguish between winter wheat crops growing on different soil types changed from the classification accuracy of 94.1% (EVI2) in the tillering stage to 75% (EVI2 and SAVI) in the shooting stage to 82.6% in the milky ripeness stage (EVI2, SAVI, NDVI). The range of the sensitivity of the vegetation indices to the soil background depended on soil type. The indices showed the greatest sensitivity on gray forest soil when the wheat was in the phase of milky ripeness, and on leached chernozem when the wheat was in the tillering phase. The observed patterns can be used to develop vegetation indices, invariant to second-type soil variations caused by soil type factor, which can be applied for the remote assessment of the state of winter wheat crops.

scholarly journals Post Processing Hyper-Spectral Data and Generating More Information from X-ray Maps

2013 ◽  
Vol 19 (S2) ◽  
pp. 828-829 ◽  
Author(s):  
R. Wuhrer ◽  
K. Moran
Keyword(s):  
Spectral Data ◽  
Post Processing ◽  
X Ray ◽  
Hyper Spectral

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

scholarly journals Analysis of dynamics for 15 vegetation indices based on Sentinel-2A image data for the test sites of winter wheat crop different on the state from each other within the forest-steep zone in Ukraine

2018 ◽  
pp. 32-39
Author(s):  
Galina Zholobak ◽  
Oksana Sybirtseva ◽  
Mariana Vakolyuk ◽  
Inna Romanciuc
Keyword(s):  
Winter Wheat ◽  
Vegetation Indices ◽  
Image Data ◽  
Early Summer ◽  
Wheat Crop ◽  
Winter Wheat Cultivars ◽  
Winter Wheat Crop ◽  
Analysis Of Dynamics ◽  
Sentinel 2A ◽  
Reflecting Surface

Dynamics of 15 vegetation indices estimated from the Sentinel-2A images within two test sites with the area of 1 ha for the production crops of two winter wheat cultivars (Bohdana and Skagen) are analyzed for winter dormancy and spring-early summer in 2016. The decrease of total nitrogen content in dry matter of the plant organs, which are formed the reflecting surface of the vegetation cover from the booting stage to milk one is consistent with the behavior of the Green NDVI (740, 560) for the both test sites of winter wheat cover. Dynamics of the other 14 indices have been analyzed under the conditions of the deterioration of phytosanitary situation for the winter wheat crop of Bohdana cultivar.

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