Using Sentinel-2 images to implement Precision Agriculture techniques in large arable fields: First results of a case study

This work assesses the potential of Sentinel-2A images in precision agriculture for Barley production in a case study. Two workflows are proposed: 1) images were acquired with a relatively simple methodology to follow the crop development; 2) two images around harvest time were downloaded and processed using a more complex and accurate methodology to calculate four vegetation indices (NDVI, WDRVI, GRVI and GNDVI) to be correlated to yield with linear regression models. Yield data were acquired with a yield monitor installed in a combine harvester. Green-based vegetation indices performed slightly better. However, the highest correlation coefficient was 0.48. Better results may be achieved with earlier imagery and other vegetation indices. Sentinel-2 is a promising tool for precision agriculture in large arable crop fields.

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Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy

10.5194/egusphere-egu2020-15941 ◽

2020 ◽

Author(s):

Calogero Schillaci ◽

Edoardo Tomasoni ◽

Marco Acutis ◽

Alessia Perego

Keyword(s):

Spatial Resolution ◽

Spatial Data ◽

Precision Agriculture ◽

Vegetation Index ◽

Vegetation Indices ◽

Northern Italy ◽

Yield Data ◽

Batch Mode ◽

Area Index ◽

Sentinel 2

<p>To improve nitrogen fertilization is well known that vegetation indices can offer a picture of the nutritional status of the crop. In this study, field management information (maize sowing and harvesting dates, tillage, fertilization) and estimated vegetation indices VI (Sentinel 2 derived Leaf Area Index LAI, Normalized Difference Vegetation Index NDVI, Fraction of Photosynthetic radiation fPAR) were analysed to develop a batch-mode VIs routine to manage high dimensional temporal and spatial data for Decision Support Systems DSS in precision agriculture, and to optimize the maize N fertilization in the field. The study was carried out in maize (2017-2018) on a farm located in Mantua (northern Italy); the soil is a Vertic Calciustepts with a fine silty texture with moderate content of carbonates. A collection of Sentinel 2 images (with <25% cloud cover) were processed using Graph Processing Tool (GPT). This tool is used through the console to execute Sentinel Application Platform (SNAP) raster data operators in batch-mode. The workflow applied on the Sentinel images consisted in: resampling each band to 10m pixel size, splitting data into subsets according to the farm boundaries using Region of Interest (ROI). Biophysical Operator based on Biophysical Toolbox was used to derive LAI, fPAR for the estimation of maize vegetation indices from emergence until senescence. Yield data were acquired with a volumetric yield sensing in a combine harvester. Fertilization plans were then calculated for each field prior to the side-dressing fertilization. The routine is meant as a user-friendly tool to obtain time series of assimilated VIs of middle and high spatial resolution for field crop fertilization. It also overcomes the failures of the open source graphic user interface of SNAP. For the year 2018, yield data were related to the 34 LAI derived from Sentinel 2a products at 10 m spatial resolution (R<sup>2</sup>=0.42). This result underlined a trend that can be further studied to define a cluster strategy based on soil properties. As a further step, we will test whether spatial differences in assimilated VIs, integrated with yield data, can guide the nitrogen top-dress fertilization in quantitative way more accurately than a single image or a collection of single images.</p>

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Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

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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First Results of Phytoplankton Spatial Dynamics in Two NW-Mediterranean Bays from Chlorophyll-a Estimates Using Sentinel 2: Potential Implications for Aquaculture

Remote Sensing ◽

10.3390/rs11151756 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1756 ◽

Cited By ~ 4

Author(s):

Soriano-González ◽

Angelats ◽

Fernández-Tejedor ◽

Diogene ◽

Alcaraz

Keyword(s):

Chlorophyll A ◽

Spatial Dynamics ◽

Ebro Delta ◽

Band Ratio ◽

Environmental Forcing ◽

Shellfish Farming ◽

First Results ◽

Nw Mediterranean ◽

Sentinel 2A ◽

Sentinel 2

Shellfish aquaculture has a major socioeconomic impact on coastal areas, thus it is necessary to develop support tools for its management. In this sense, phytoplankton monitoring is crucial, as it is the main source of food for shellfish farming. The aim of this study was to assess the applicability of Sentinel 2 multispectral imagery (MSI) to monitor the phytoplankton biomass at Ebro Delta bays and to assess its potential as a tool for shellfish management. In situ chlorophyll-a data from Ebro Delta bays (NE Spain) were coupled with several band combination and band ratio spectral indices derived from Sentinel 2A levels 1C and 2A for time-series mapping. The best results (AIC = 72.17, APD < 10%, and MAE < 0.7 mg/m3) were obtained with a simple blue-to-green ratio applied over Rayleigh corrected images. Sentinel 2–derived maps provided coverage of the farm sites at both bays allowing relating the spatiotemporal distribution of phytoplankton with the environmental forcing under different states of the bays. The applied methodology will be further improved but the results show the potential of using Sentinel 2 MSI imagery as a tool for assessing phytoplankton spatiotemporal dynamics and to encourage better future practices in the management of the aquaculture in Ebro Delta bays.

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Identification of High-Variation Fields based on Open Satellite Imagery

Advances in Animal Biosciences ◽

10.1017/s2040470017000693 ◽

2017 ◽

Vol 8 (2) ◽

pp. 388-393 ◽

Cited By ~ 6

Author(s):

J. H. Jeppesen ◽

R. H. Jacobsen ◽

R. N. Jørgensen ◽

A. Halberg ◽

T. S. Toftegaard

Keyword(s):

Satellite Imagery ◽

Open Source Software ◽

Vegetation Indices ◽

Open Data ◽

Agricultural Practices ◽

Regional Level ◽

Simple Method ◽

Potential Benefits ◽

Sentinel 2

This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective. The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update of satellite imagery, hence coupling the geospatial data analysis to direct improvements for the farmers, contractors, and consultants.

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Relationships between the Spatio-Temporal Variation in Reflectance Data from the Sentinel-2 Satellite and Potato (Solanum Tuberosum L.) Yield and Stem Density

Remote Sensing ◽

10.3390/rs13214371 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4371

Author(s):

Joseph K. Mhango ◽

W. Edwin Harris ◽

James M. Monaghan

Keyword(s):

Solanum Tuberosum ◽

Temporal Variation ◽

Precision Agriculture ◽

Solanum Tuberosum L ◽

Satellite Image ◽

Vegetation Indices ◽

Stem Density ◽

Marketable Yield ◽

Sampling Locations ◽

Sentinel 2

Satellite Image Time Series (SITS) have been used to build models for predicting Potato (Solanum tuberosum L.) yields at regional scales, but evidence of extension of such models to local field scale for practical use in precision agriculture is lacking. In this study, multispectral data from the Sentinel-2 satellite were used to interpolate continuous spectral signatures of potato canopies and generate vegetation indices and the red edge inflection point (REIP) to relate to marketable yield and stem density. The SITS data were collected from 94 sampling locations across five potato fields in England, United Kingdom. The sampling locations were georeferenced and the number of stems per square meter, as well as marketable yield, were determined at harvest. The first principal components of the temporal variation of each SITS wavelength were extracted and used to generate 54 vegetation indices to relate to the response variables. Marketable yield was negatively related to the overall seasonal reflectance (first principal component) at 559 nm with a beta coefficient of −0.53 (±0.18 at p = 0.05). Seasonal reflectance at 703 nm had a positive significant relationship with Marketable yield. Marketable yield was modeled with a normalized root mean square error (nRMSE) of 0.16 and R2 of 0.65. On the other hand, Stem density was significantly related to the Specific Leaf Area Vegetation Index (β = 1.66 ± 1.59) but the REIP’s farthest position during the season was reached later in dense canopies (β = 1.18 ± 0.79) with a higher reflectance (β = 3.43 ± 1.9). This suggested that denser canopies took longer to reach their maximum chlorophyll intensity and the intensity was lower than in sparse canopies. Potato stem density was modeled with an nRMSE of 0.24 and R2 of 0.51. These results reinforce the importance of SITS analysis as opposed to the use of single-instance intrinsic indices.

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Remote monitoring of the state of winter wheat during the spring-summer vegetation of 2016 year, by using vegetation indices of Sentinel-2A satellite (case study by foreststeppe area of Ukraine)

Ukrainian journal of remote sensing ◽

10.36023/ujrs.2017.15.115 ◽

2018 ◽

pp. 23-30 ◽

Cited By ~ 3

Author(s):

Galina Zholobak ◽

Oksana Sybirtseva ◽

Mariana Vakolyuk ◽

Yuliia Zakharchyk

Keyword(s):

Winter Wheat ◽

Survey Data ◽

Remote Monitoring ◽

Vegetation Indices ◽

The State ◽

Spectral Vegetation Indices ◽

Sentinel 2A ◽

New Generation

The spectral vegetation indices NDVI (842, 665), NDVI (740, 665) and GreenNDVI received from the survey data of new generation satellite Sentinel2A, were analyzed in publication for studying the vegetation of two cultivars of winter wheat, grown up of crops production for the harvest in 2016 year of Grain Alliance Ukraine (Berezan, Kyiv oblast, Ukraine).

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Precision Agriculture Techniques and Practices: From Considerations to Applications

Sensors ◽

10.3390/s19173796 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3796 ◽

Cited By ~ 29

Author(s):

Uferah Shafi ◽

Rafia Mumtaz ◽

José García-Nieto ◽

Syed Ali Hassan ◽

Syed Ali Raza Zaidi ◽

...

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Precision Agriculture ◽

Vegetation Indices ◽

Real Data ◽

Wireless Sensor ◽

Agriculture Sector ◽

Enhanced Production ◽

Crop Health

Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.

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Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle

Remote Sensing ◽

10.3390/rs11212541 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2541 ◽

Cited By ~ 2

Author(s):

Pause ◽

Raasch ◽

Marrs ◽

Csaplovics

Keyword(s):

Time Series ◽

Weed Management ◽

Time Series Data ◽

Vegetation Indices ◽

Agricultural Practices ◽

Series Data ◽

Herbicide Treatment ◽

Proof Of Principle ◽

Sentinel 2

In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring.

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Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment

Sensors ◽

10.3390/s21082861 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2861

Author(s):

Josephine Bukowiecki ◽

Till Rose ◽

Henning Kage

Keyword(s):

Precision Agriculture ◽

Near Infrared ◽

Absolute Error ◽

Yield Data ◽

Area Index ◽

Crop Monitoring ◽

Red Edge ◽

Using Data ◽

Simple Ratio ◽

Sentinel 2

An approach of exploiting and assessing the potential of Sentinel-2 data in the context of precision agriculture by using data from an unmanned aerial vehicle (UAV) is presented based on a four-year dataset. An established model for the estimation of the green area index (GAI) of winter wheat from a UAV-based multispectral camera was used to calibrate the Sentinel-2 data. Large independent datasets were used for evaluation purposes. Furthermore, the potential of the satellite-based GAI-predictions for crop monitoring and yield prediction was tested. Therefore, the total absorbed photosynthetic radiation between spring and harvest was calculated with satellite and UAV data and correlated with the final grain yield. Yield maps at the same resolution were generated by combining yield data on a plot level with a UAV-based crop coverage map. The best tested model for satellite-based GAI-prediction was obtained by combining the near-, infrared- and Red Edge-waveband in a simple ratio (R2 = 0.82, mean absolute error = 0.52 m2/m2). Yet, the Sentinel-2 data seem to depict average GAI-developments through the seasons, rather than to map site-specific variations at single acquisition dates. The results show that the lower information content of the satellite-based crop monitoring might be mainly traced back to its coarser Red Edge-band. Additionally, date-specific effects within the Sentinel-2 data were detected. Due to cloud coverage, the temporal resolution was found to be unsatisfactory as well. These results emphasize the need for further research on the applicability of the Sentinel-2 data and a cautious use in the context of precision agriculture.

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Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications

Applied Sciences ◽

10.3390/app9245314 ◽

2019 ◽

Vol 9 (24) ◽

pp. 5314 ◽

Cited By ~ 8

Author(s):

Marica Franzini ◽

Giulia Ronchetti ◽

Giovanna Sona ◽

Vittorio Casella

Keyword(s):

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

P Value ◽

Multispectral Imagery ◽

Radiometric Calibration ◽

Processing Strategies ◽

Geometric Consistency ◽

Sentinel 2

This paper is about the geometric and radiometric consistency of diverse and overlapping datasets acquired with the Parrot Sequoia camera. The multispectral imagery datasets were acquired above agricultural fields in Northern Italy and radiometric calibration images were taken before each flight. Processing was performed with the Pix4Dmapper suite following a single-block approach: images acquired in different flight missions were processed in as many projects, where different block orientation strategies were adopted and compared. Results were assessed in terms of geometric and radiometric consistency in the overlapping areas. The geometric consistency was evaluated in terms of point cloud distance using iterative closest point (ICP), while the radiometric consistency was analyzed by computing the differences between the reflectance maps and vegetation indices produced according to adopted processing strategies. For normalized difference vegetation index (NDVI), a comparison with Sentinel-2 was also made. This paper will present results obtained for two (out of several) overlapped blocks. The geometric consistency is good (root mean square error (RMSE) in the order of 0.1 m), except for when direct georeferencing is considered. Radiometric consistency instead presents larger problems, especially in some bands and in vegetation indices that have differences above 20%. The comparison with Sentinel-2 products shows a general overestimation of Sequoia data but with similar spatial variations (Pearson’s correlation coefficient of about 0.7, p-value < 2.2 × 10−16).

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