scholarly journals ESTIMATION OF MANGROVE FOREST ABOVEGROUND BIOMASS USING MULTISPECTRAL BANDS, VEGETATION INDICES AND BIOPHYSICAL VARIABLES DERIVED FROM OPTICAL SATELLITE IMAGERIES: RAPIDEYE, PLANETSCOPE AND SENTINEL-2

2018 ◽  
Vol IV-3 ◽  
pp. 29-36 ◽  
Author(s):  
Alvin Balidoy Baloloy ◽  
Ariel Conferido Blanco ◽  
Christian Gumbao Candido ◽  
Reginal Jay Labadisos Argamosa ◽  
John Bart Lovern Caboboy Dumalag ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mangrove Forest ◽  
Field Data ◽  
Vegetation Index ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Satellite Systems ◽  
Red Edge ◽  
Simple Ratio ◽  
Sentinel 2

Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10&amp;thinsp;m, 20&amp;thinsp;m and 60&amp;thinsp;m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a <i>Rhizophoraceae</i>-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2&amp;thinsp;ha) and 5 validation plots (0.2&amp;thinsp;ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r<sup>2</sup>) values were obtained using multispectral band predictors for Sentinel-2 (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.89) and Planetscope (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.80); and vegetation indices for RapidEye (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r<sup>2</sup> ranging from 0.62 to 0.92. Based on the r<sup>2</sup> and root-mean-square errors (RMSE’s), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.92) and RapidEye data (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.91).

scholarly journals A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery

2019 ◽  
Vol 11 (17) ◽  
pp. 2043 ◽  
Author(s):  
Jia ◽  
Wang ◽  
Wang ◽  
Mao ◽  
Zhang
Keyword(s):  
Mangrove Forest ◽  
Vegetation Index ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Vegetation Indices ◽  
Short Wave ◽  
Mangrove Forests ◽  
Red Edge ◽  
Jensen Shannon Divergence ◽  
Sentinel 2

Mangrove forests are tropical trees and shrubs that grow in sheltered intertidal zones. Accurate mapping of mangrove forests is a great challenge for remote sensing because mangroves are periodically submerged by tidal floods. Traditionally, multi-tides images were needed to remove the influence of water; however, such images are often unavailable due to rainy climates and uncertain local tidal conditions. Therefore, extracting mangrove forests from a single-tide imagery is of great importance. In this study, reflectance of red-edge bands in Sentinel-2 imagery were utilized to establish a new vegetation index that is sensitive to submerged mangrove forests. Specifically, red and short-wave near infrared bands were used to build a linear baseline; the average reflectance value of four red-edge bands above the baseline is defined as the Mangrove Forest Index (MFI). To evaluate MFI, capabilities of detecting mangrove forests were quantitatively assessed between MFI and four widely used vegetation indices (VIs). Additionally, the practical roles of MFI were validated by applying it to three mangrove forest sites globally. Results showed that: (1) theoretically, Jensen–Shannon divergence demonstrated that a submerged mangrove forest and water pixels have the largest distance in MFI compared to other VIs. In addition, the boxplot showed that all submerged mangrove forests could be separated from the water background in the MFI image. Furthermore, in the MFI image, to separate mangrove forests and water, the threshold is a constant that is equal to zero. (2) Practically, after applying the MFI to three global sites, 99–102% of submerged mangrove forests were successfully extracted by MFI. Although there are still some uncertainties and limitations, the MFI offers great benefits in accurately mapping mangrove forests as well as other coastal and aquatic vegetation worldwide.

scholarly journals Sen2Grass: A Cloud-Based Solution to Generate Field-Specific Grassland Information Derived from Sentinel-2 Imagery

2021 ◽  
Vol 3 (1) ◽  
pp. 118-137
Author(s):  
Tom Hardy ◽  
Lammert Kooistra ◽  
Marston Domingues Franceschini ◽  
Sebastiaan Richter ◽  
Erwin Vonk ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Management Practices ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Weather Conditions ◽  
Grassland Management ◽  
Time Range ◽  
Computing Platform ◽  
Robust Prediction ◽  
Sentinel 2

Grasslands are important for their ecological values and for agricultural activities such as livestock production worldwide. Efficient grassland management is vital to these values and activities, and remote sensing technologies are increasingly being used to characterize the spatiotemporal variation of grasslands to support those management practices. For this study, Sentinel-2 satellite imagery was used as an input to develop an open-source and automated monitoring system (Sen2Grass) to gain field-specific grassland information on the national and regional level for any given time range as of January 2016. This system was implemented in a cloud-computing platform (StellaSpark Nexus) designed to process large geospatial data streams from a variety of sources and was tested for a number of parcels from the Haus Riswick experimental farm in Germany. Despite outliers due to fluctuating weather conditions, vegetation index time series suggested four distinct growing cycles per growing season. Established relationships between vegetation indices and grassland yield showed poor to moderate positive trends, implying that vegetation indices could be a potential predictor for grassland biomass and chlorophyll content. However, the inclusion of larger and additional datasets such as Sentinel-1 imagery could be beneficial to developing more robust prediction models and for automatic detection of mowing events for grasslands.

scholarly journals Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

2020 ◽  
Vol 12 (7) ◽  
pp. 1176 ◽  
Author(s):  
Yukun Lin ◽  
Zhe Zhu ◽  
Wenxuan Guo ◽  
Yazhou Sun ◽  
Xiaoyuan Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Water Potential ◽  
Large Scale ◽  
Temporal Frequency ◽  
Vegetation Indices ◽  
Stem Water Potential ◽  
Spectral Bands ◽  
Red Edge ◽  
Sentinel 2

Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.

scholarly journals Development of a Rapid Mangrove Zonation Mapping Workflow Using Sentinel 2-Derived Indices and Biophysical Dataset

2021 ◽  
Vol 2 ◽  
Author(s):  
Alvin B. Baloloy ◽  
Ariel C. Blanco ◽  
Sahadev Sharma ◽  
Kazuo Nadaoka
Keyword(s):  
Mangrove Forest ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
The Philippines ◽  
Distribution Analysis ◽  
Rhizophora Stylosa ◽  
Area Index ◽  
Zonation Maps ◽  
Mangrove Zonation ◽  
Sentinel 2

Moderate to high resolution satellite imageries are commonly used in mapping mangrove cover from local to global scales. In addition to extent information, studies such as mangrove composition, ecology, and distribution analysis require further information on mangrove zonation. Mangrove zonation refers to unique sections within a mangrove forest being dominated by a similar family, genus, or species. This can be observed both in natural and planted mangrove forests. In this study, a mapping workflow was developed to detect zonation in test mangrove forest sites in Katunggan-It Ibajay (KII) Ecopark (Aklan), Bintuan (Coron), Bogtong, and Sagrada (Busuanga) in the Philippines and Fukido Mangrove Park (Ishigaki, Japan) using Sentinel-2 imagery. The methodology was then applied to generate a nationwide mangrove zonation map of the Philippines for year 2020. Combination of biophysical products, water, and vegetation indices were used as classification inputs including leaf area index (LAI), fractional vegetation cover (FVC), fraction of photosynthetically-active radiation (FAPAR), Canopy chlorophyll content (Cab), canopy water content (Cw), Normalized Difference Vegetation Index (NDVI), modified normalized difference water index (MNDWI), modified chlorophyll absorption in reflectance index (MCARI), and red-edge inflection point (REIP). Mangrove extents were first mapped using either the Maximum Likelihood Classification (MLC) algorithm or the Mangrove Vegetation Index (MVI)-based methodology. The biophysical and vegetation indices within these areas were stacked and transformed through Principal Component Analysis (PCA). Regions of Interest (ROIs) were selected on the PCA bands as training input to the MLC. Results show that mangrove zonation maps can highlight the major mangrove zones in the study sites, commonly limited up to genera level only except for genera with only one known species thriving in the area. Four zones were detected in KII Ecopark: Avicennia zone, Nypa zone, Avicennia mixed with Nypa zone, and mixed mangroves zones. For Coron and Busuanga, the mapped mangrove zones are mixed mangroves, Rhizophora zone and sparse/damaged zones. Three zones were detected in Fukido site: Rhizophora stylosa-dominant zone, Bruguiera gymnorrhiza-dominant zone, and mixed mangrove zones. The zonation maps were validated using field plot data and orthophotos generated from Unmanned Aerial System (UAS) surveys, with accuracies ranging from 75 to 100%.

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 A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China

2020 ◽  
Vol 12 (11) ◽  
pp. 1884 ◽  
Author(s):  
Fugen Jiang ◽  
Andrew R. Smith ◽  
Mykola Kutia ◽  
Guangxing Wang ◽  
Hua Liu ◽  
...  
Keyword(s):  
Spectral Reflectance ◽  
Prediction Accuracy ◽  
Field Data ◽  
Vegetation Indices ◽  
Arid Areas ◽  
Vegetation Canopy ◽  
Area Index ◽  
Red Edge ◽  
Semi Arid ◽  
Sentinel 2

As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images.

scholarly journals ASSESSING CROP MONITORING POTENTIAL OF SENTINEL-2 IN A SPATIO-TEMPORAL SCALE

2018 ◽  
Vol XLII-5 ◽  
pp. 227-231 ◽  
Author(s):  
P. Ghosh ◽  
D. Mandal ◽  
A. Bhattacharya ◽  
M. K. Nanda ◽  
S. Bera
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Crop Monitoring ◽  
Red Edge ◽  
Plant Area Index ◽  
Spatio Temporal ◽  
Sentinel 2

<p><strong>Abstract.</strong> Spatio-temporal variability of crop growth descriptors is of prime importance for crop risk assessment and yield gap analysis. The incorporation of three bands (viz., B5, B6, B7) in ‘red-edge’ position (i.e., 705<span class="thinspace"></span>nm, 740<span class="thinspace"></span>nm, 783<span class="thinspace"></span>nm) in Sentinel-2 with 10&amp;ndash;20<span class="thinspace"></span>m spatial resolution images with five days revisit period have unfolded opportunity for meticulous crop monitoring. In the present study, the potential of Sentinel-2 have been appraised for monitoring phenological stages of potato over Bardhaman district in the state of West Bengal, India. Due to the competency of Vegetation indices (VI) to evaluate the status of crop growth; we have used the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Index45 (NDI45) for crop monitoring. Time series analysis of the VIs exhibited increasing trend as the crop started approaching maturity and attained a maximum value during the tuber development stage and started decreasing as the crop advances to senescence. Inter-field variability of VIs highlighted the need of crop monitoring at high spatial resolution. Among the three vegetation indices, the GNDVI (<i>r</i><span class="thinspace"></span>=<span class="thinspace"></span>0.636), NDVI (<i>r</i><span class="thinspace"></span>=<span class="thinspace"></span>0.620) had the highest correlation with biomass and Plant Area Index (PAI), respectively. NDI45 had comparatively a lower correlation (<i>r</i><span class="thinspace"></span>=<span class="thinspace"></span>0.572 and 0.585 for PAI and biomass, respectively) with both parameters as compared to other two indices. It is interesting to note that the use of Sentinel-2 Green band (B3) instead of the Red band (B4) in GNDVI resulted in 2.5% increase of correlation with biomass. However, the improvement in correlations between NDI45 with crop biophysical parameters is not apparent in this particular study with the inclusion of the Vegetation Red Edge band (B5) in VI. Nevertheless, the strong correlation of VIs with biomass and PAI asserted proficiency of Sentinel-2 for crop monitoring and potential for crop biophysical parameter retrieval with optimum accuracy.</p>

scholarly journals DISEASE DETECTION FROM FIELD SPECTROMETER DATA

2013 ◽  
Vol 14 (2) ◽  
Author(s):  
O. H. Tawfik ◽  
H. Z. Mohd Shafri ◽  
Ali A. Mohammed
Keyword(s):  
Oil Palm ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Red Edge ◽  
Plant Stage ◽  
Simple Ratio ◽  
Stage 1 ◽  
Disease Stages ◽  
Spectrometer Data

ABSTRACT: Oil palm plants have been planted in large scale of areas. Ganoderma disease has been recognized and diagnosed in oil palm plants to infect almost half of the oil palm plants in Malaysia. To deal with this problem, the use of vegetation indices analysis on hyper spectral field data we will examine the ability of this data in discrimination between Ganoderma disease stages in oil palm plants which will be helpful in control the spread of the diseases. By using vegetation indices the oil palm plants could be classified into 1 (T1 healthy), 2 (T2 semi healthy) and 3 (T3 severe damage) plant classes accurately. The results showed that the best vegetation index is the Modified Red Edge Simple Ratio (MSR705) among the vegetation indices to discriminate between oil palm health stages. It was realized that the modification that was applied to the Modified Red Edge Simple Ratio (MSR705) index of Narrowband greenness VIs has been exhibited an acceptable results in differentiate between the oil palm plant stage 1 (T1 healthy) and stage 2 (T2 semi healthy). ABSTRAK: Tanaman kelapa sawit ditanam secara meluas.  Penyakit ganoderma dikenali dan didiagnosikan menjangkiti hampir separuh tanaman kelapa sawit di Malaysia. Untuk mengawal penyakit ini daripada merebak, analisis indeks tanaman dijalankan ke atas data kawasan spektrum melampau di mana keupayaan data ini diuji dalam membezakan peringkat-peringkat penyakit Ganoderma terhadap tanaman kelapa sawit. Dengan menggunakan indeks tanaman, kelapa sawit dapat diklasifikasikan kepada 1 (T1 sihat), 2 (T2 separa sihat) dan 3 (T3 rosak); kelas tanaman dengan tepat. Keputusan menunjukkan indeks tanaman terbaik sebagai Modified Red Edge Simple Ratio (MSR705) yang merupakan indeks tanaman dalam membezakan peringkat kesihatan kelapa sawit. Adalah didapati pengubahsuaian terhadap indeks Modified Red Edge Simple Ratio (MSR705) yang juga indeks Jalur Sempit Hijau VI telah memberikan keputusan yang munasabah dalam membezakan peringkat tanaman kelapa sawit peringkat 1 (T1 sihat) dan peringkat 2 (T2 separa sihat).

scholarly journals Spectral Diffractive Lenses for Measuring a Modified Red Edge Simple Ratio Index and a Water Band Index

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7694
Author(s):  
Veronika Blank ◽  
Roman Skidanov ◽  
Leonid Doskolovich ◽  
Nikolay Kazanskiy
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Diffractive Lenses ◽  
Red Edge ◽  
Water Band ◽  
Direct Laser ◽  
Band Index ◽  
Simple Ratio ◽  
Imaging Sensor ◽  
Linear Scanning

We propose a novel type of spectral diffractive lenses that operate in the ±1-st diffraction orders. Such spectral lenses generate a sharp image of the wavelengths of interest in the +1-st and –1-st diffraction orders. The spectral lenses are convenient to use for obtaining remotely sensed vegetation index images instead of full-fledged hyperspectral images. We discuss the design and fabrication of spectral diffractive lenses for measuring vegetation indices, which include a Modified Red Edge Simple Ratio Index and a Water Band Index. We report synthesizing diffractive lenses with a microrelief thickness of 4 µm using the direct laser writing in a photoresist. The use of the fabricated spectral lenses in a prototype scheme of an imaging sensor for index measurements is discussed. Distributions of the aforesaid spectral indices are obtained by the linear scanning of vegetation specimens. Using a linear scanning of vegetation samples, distributions of the above-said water band index were experimentally measured.

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.

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