utilizing sentinel 2 satellite imagery for precision … · 2017. 12. 8. · early 1970’s (mulla...

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November 13-16, 2017 Sioux Falls, SD

UTILIZING SENTINEL – 2 SATELLITE IMAGERY FOR PRECISION

AGRICULTURE OVER POTATO FIELDS IN LEBANON

Hanan Abou Ali, MS GIS student

Donna Delparte, Assistant Professor

Department of Geosciences

Idaho State University

Pocatello, ID 83209

Michael Griffel, Agricultural Research Analyst

Biofuels and Renewable Energy Technology Department

Idaho National Laboratory

Idaho Falls, ID 83415 [email protected]

[email protected]

[email protected]

ABSTRACT

Lebanon has traditionally been a major potato producer, 451,860 tons in 2014, and exporter where 60% of its

production is distributed within the Arab region, to the UK and Brazil and has potato make up 30% of the total

agricultural exports. The purpose of this study is to promote precision agriculture techniques in Lebanon that will help

local farmers in the central Bekaa Valley with land management decisions. The European Space Agency’s satellite

missions Sentinel-2A, launched June 23rd 2015, and the Sentinel-2B, recently launched on March 7th 2017, are

multispectral high resolution imaging systems that provide global coverage every 5 days. The Sentinel program is a

land monitoring program that includes an aim to improve agricultural practices. The imagery is 13 band data in the

visible, near infrared and short wave infrared parts of the electromagnetic spectrum and ranges from 10-20 m pixel

resolution. Sentinel is freely available data that has the potential to empower farmers with information to respond

quickly to maximize crop health. Due to the political and security conflicts in the region, utilizing satellite imagery

for Lebanon is more reasonable and realistic than operating Unmanned Aircraft Systems (UAS) for high resolution

remote sensing. During the 2017 growing season, local farmers provided detailed information in designated fields on

their farming practices, crop health, and pest threats. In parallel, Sentinel-2 imagery was processed to study crop health

using the following vegetation indices: Normalized Difference Vegetation Index, Green Normalized Difference

Vegetation Index, Soil Adjusted Vegetation Index and Modified Soil Adjusted Vegetation Index 2. As most Lebanese

farmers inherit their land from their parents over generations, most still use traditional farming techniques for

irrigation, they make decisions based on prior generations’ practices, which is no longer compatible with changes in

climatic conditions in the region. Normalized Difference Water Indices are calculated from satellite bands in the near-

infrared and short-wave infrared to provide a better understanding about the water stress status of crops within the

field. Preliminary results demonstrate that Sentinel-2 data can provide detailed and timely data for farmers to

effectively manage fields. Despite the fact that most Lebanese farmers rely on traditional farming methods, providing

them with free crop health information on their mobile phones and allowing them to test its efficiency has the potential

to be a catalyst to help them improve their farming practices.

KEYWORDS: crop health, field management, potatoes, remote sensing, Sentinel-2

INTRODUCTION

Lebanon relies heavily on its agricultural sector with an agricultural area of approximately 2,730 km2 out of the

total 10,452 km2 (El Gazzar 2015) and a contribution of 7% to Gross Domestic Product (GDP) while providing work

to 15% of the Lebanese population (FITA 2008). Potato crops account for 56% of vegetable production in the country,

mainly in the Bekaa Valley and North Lebanon states (Hatoum 2005). Hence, it is important to ensure the health

status of potato crops to improve production in order to revive the local market as growers are having a hard time

selling their produce ( 2015شهيب نقل إلى سالم معاناة المزارعين درباس: نمر في أخطر المراحل ) and expand the Lebanese economy

mailto:[email protected]

mailto:[email protected]



by increasing potato exports. The Bekaa Valley located in the center of Lebanon is one of the largest agricultural

regions in the country and potatoes are the main crop. Potatoes are an important irrigated crop that can be vulnerable

to water availability, pests, disease and other crop threats. Precision agriculture has the potential to help minimize

such issues and improve crop yield through empowering farmers with timely scientific knowledge on crop condition. Precision agriculture offers a solution for such problems; however, in a country such as Lebanon, precision agriculture

is still in its early stages of adoption. Lebanon recently teamed up with the Food and Agriculture Organization of the

United Nations on May 16, 2016, to launch the Country Programming Framework from 2016 to 2019 (United Nations

Food and Agriculture Organization 2016) in order to develop more sustainable practices to improve the agricultural

sector and may include the concept of precision agriculture. Hence, this project is aimed at showing the potential of

precision agriculture through open-access satellite imagery and how beneficial it is for the farmer with the main goal

is to submit this work to the Lebanese Ministry of Agriculture.

For potato viruses in Lebanon, Abou-Jawdeh et al. (2001) sampled growth seasons from major agricultural

regions in the country and though various potato viruses were detected, potato virus Y (PVY) (Potyvirus) was the

most dominant one among all samples. PVY represents a big threat to potato crops worldwide as it is considered the

most harmful on potato fields (Steinger, Gilliand, and Hebeisen 2014) and in some places like Turkey they are

recording the highest percentages of infections among other potato viruses (Yardımcı, Kılıç, and Demir 2015). Another

potato threat in Lebanon is the Potato Cyst Nematodes (PCN) (Globodera) which is the major potato pest in the

country (Ibrahim, Abi Saad, and Moussa 2004). PCN are the most severe potato pest where losses could reach up to

80% within a growing area (Hassan et al. 2013). The biggest challenge in detecting PCN is that it needs skilled and

experienced workers to visually identify infected plants and soil in the field or by using, real-time Polymerase Chain

Reaction in the labs, which saves time but is very costly (Hassan et al. 2013).

Satellite imagery technology improved constantly over time which was reflected by increased sensor resolution

and expanding the usages of satellite images to cover various applications such as precision agriculture starting in the

early 1970’s (Mulla 2013). Not only has satellite imagery improved in quality, but they have also become more

accessible to the public (Turner et al. 2015) through constantly updated datasets such as the Sentinel-2 mission by the

European Space Agency (ESA) as a part of the Copernicus program. Sentinel-2A, launched in June 2015, and more

recently, Sentinel-2B launched in March 2017, are two new multispectral imagers covering 13 spectral bands (443 nm

– 2190 nm) at resolutions of 10 - 20 m. The Sentinel 2 program is filling a void for low-cost, medium resolution

imaging with 5 day revisit times to assess plant health and vigor during growing seasons (Dash and Ogutu 2016). In

addition, there is the RapidEye satellite constellation that is currently operated by Planet Labs. It was launched on

August 29 of 2008 with 5 spectral bands (440 nm – 850 nm) at a resolution of 5 m. Both Sentinel-2 mission and

RapidEye are partially aimed for agricultural uses which makes them an ideal source for researching precision

agriculture via open-source datasets.

This paper is focused on demonstrating the potential of Sentinel-2 imagery for monitoring crop health of potato

fields in Lebanon and whether or not vegetation indices relate to potato crop yield. It is hypothesized that a low

vegetation index indicates a certain problem in the field and will correspond to lower yield numbers in the field than

areas where vegetation indices values are higher. This work shows that Sentinel imagery provides a low-cost

alternative for Lebanese farmers that is accessible to the public to use in combination with open source software such

as QGIS for low-cost data processing.

As irrigation plays a major role in the health status of potato crops, and as the world’s climate is constantly

changing, it is important to analyze water stress and content in crops to manage water resources more efficiently and

to figure out what irrigation techniques are the most convenient. According to research performed in Egypt, (Nahry,

Ali, and Baroudy 2011), using satellite imagery proved to be very effective in the intended purpose as it increased

economic profitability by 29.89% and environmental profitability by limiting fertilizers and irrigation to where it is

needed only. Using multispectral satellite imagery as Sentinel data, Normalized Difference Water Index (NDWI)

could be easily calculated to help give a better idea of water stress in plants (Gao 1996).

METHODS Study Area The study area is located in Tal Znoub in the southern western part of the Bekaa Valley in Lebanon. It lies northern of

Quaroun Lake and is along the path of the Litani River. It is located at 4 km north northeast of the city of Jeb Jannine

which is the capital of the West Bekaa. The overall area of the site is 462,267 m2 which is divided into sub fields as shown

in Table 1.



Table 1. Field Areas

Figure 1. Study Area, Tal Znoub, Bekaa, Lebanon - Basemap source:

Planet Team (2017). Planet Application Program Interface: In Space for

Life on Earth. San Francisco, CA. https://api.planet.com

Data Resources The satellite imagery dataset for this work was downloaded through the USGS Earth Explorer interface for the

Sentinel – 2A imagery and through the ESA Copernicus interface for the Sentinel – 2B imagery (Table 2). In addition

to Rapid Eye imagery through the Planet Team application interface.

The Rapid Eye imagery is the base for digitizing the study area location as well as the subfields within the area

due to the high resolution of the imagery of 5m.

Table 2. Satellite Imagery Sources

Data Type Data Source Data Link

Sentinel – 2A USGS Earth Explorer https://earthexplorer.usgs.gov/

Sentinel – 2B ESA Copernicus https://scihub.copernicus.eu/s2b/#/home

Rapid Eye Planet Application Program Interface www.planet.com/explorer

Data Pre – Processing The Sentinel – 2A and Sentinel – 2B imagery are processed using the open source software QGIS for atmospheric

correction via the Semi-Automatic Classification Plugin. The software takes level – 1C Sentinel imagery metadata

and individual bands and converts the imagery from “Digital Count” to “Reflectance Values” to be able to run indices

and perform the needed analyses.

Data Processing Indices best suited for analyzing crop health status are outlined in Error! Reference source not found.3 and

include: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) (Candiago et

Field_ID Area (m2)

1 6000

2 11900

3 19200

4 14000

5 24400

6 38200

7 65700

8 28800

9 320000

10 35400

11 30400

27 38300

28 31500

29 17200

30 18800

33 50800

https://api.planet.com/

https://scihub.copernicus.eu/s2b/#/home

http://www.planet.com/explorer



al. 2015). NDVI is a normalized ratio of near-infrared and red bands that ranges between -1 and 1 where the higher

the value the more vegetated it is while lower values correspond to non-vegetated areas and features. Due to the ratio

calculation of NDVI, it reduces noise and provides a great approach for comparing changes in vegetation over periods

of time (A. Huete et al. 2002). SAVI index on the other side aims at minimizing the effect of soil in vegetation unlike

NDVI that is unable to do that (A. R. Huete 1988). It has a range between -1 and 1 where values between -1 and 0.1

are most likely not vegetation. This index has the variable L in its equation which is related to the density of vegetation

so as the density increases, the value of L decreases (Candiago et al. 2015).

After the scenes are corrected in QGIS, the needed bands (Near Infrared: Band 8, Red: Band 4 and Green: Band3)

were imported into ArcMap for processing of vegetation indices (Table 3). In order to increase efficiency, using raster

calculator, the various indices’ formulas were built into a tool using model builder in ArcMap and the output raster

datasets were saved into a specific geodatabase for data management purposes.

From the processed data and through the vegetation indices’ values, a qualitative analysis was performed to verify

that the different indices are all consistent with one another. Then the farmer in Lebanon provided information

regarding the field and the yield to serve as a control for our results.

Table 3. Indices

Data Post – Processing After the processing of all indices on the fields, “Zonal Statistics as Table” tool in ArcMap was used to summarize

the values obtained. Using the specific output raster of each individual index as the input raster and using the fields as

the zone field, various statistics were calculated and exported as an excel sheet. These statistics included the following

information for each field: minimum value, maximum value, mean and standard deviation.

Information from Grower The Lebanese farmer supported the data analysis by providing information in regard to the fields and the

growing practices throughout the season (Table 4).

Vegetation Index Formula Reference

Normalized Difference

Vegetation Index

𝜌𝑁𝐼𝑅 − 𝜌𝑅𝑒𝑑

𝜌𝑁𝐼𝑅 + 𝜌𝑅𝑒𝑑

(Rouse et al. 1973)

Soil Adjusted

Vegetation Index

𝜌𝑁𝐼𝑅−𝜌𝑅𝑒𝑑

𝜌𝑁𝐼𝑅+ 𝜌𝑅𝑒𝑑+𝐿 + (1+L) (A. R. Huete 1988)

Normalized Difference

Water Index

𝜌𝐺𝑟𝑒𝑒𝑛 − 𝜌𝑁𝐼𝑅

𝜌𝐺𝑟𝑒𝑒𝑛 + 𝜌𝑁𝐼𝑅

(S.K. 1996)



RESULTS

Figure 2. Normalized Difference Vegetation Index

Figure 3. Normalized Difference Vegetation Index – Zonal Statistics

Field_ID MIN MAX MEAN STD MIN MAX MEAN STD MIN MAX MEAN STD

1 0.223198593 0.299333364 0.253078732 0.022080539 0.256576449 0.403726727 0.30827049 0.0414778 0.326241136 0.510144949 0.385356123 0.052160953

2 0.221884489 0.32148841 0.244657094 0.019452899 0.257815421 0.420446396 0.289488686 0.02908002 0.318378776 0.510252595 0.37820012 0.030277371

3 0.218507007 0.315277755 0.236433579 0.012422675 0.269230783 0.366912365 0.290064887 0.01662983 0.357600272 0.516335547 0.418615373 0.032753992

4 0.22367467 0.34321323 0.241806261 0.015883547 0.268668771 0.433574468 0.290762617 0.02091582 0.318181872 0.51831162 0.362003016 0.026524195

5 0.223616257 0.313863456 0.242499004 0.010755605 0.273043483 0.433962256 0.297722794 0.0197757 0.313968658 0.52593112 0.413834791 0.038592926

6 0.219566852 0.352981538 0.259459557 0.029541103 0.269769222 0.501793563 0.331425502 0.05765718 0.348735541 0.615617514 0.468422606 0.062785025

7 0.204853684 0.334048629 0.246253845 0.021508626 0.263451576 0.481655002 0.330882795 0.03495382 0.323996246 0.741176426 0.5795601 0.090468124

8 0.226134583 0.365256935 0.248355989 0.01811585 0.279805362 0.530952394 0.315867204 0.03482229 0.435559005 0.670631349 0.515683205 0.025042878

9 0.223190606 0.392009974 0.247480365 0.016122879 0.281923681 0.507016957 0.32658167 0.02930798 0.368105084 0.727999926 0.595946143 0.083070059

10 0.22674869 0.331497461 0.256492685 0.017712771 0.322262168 0.438626587 0.350844345 0.0193515 0.428739667 0.713688016 0.651522493 0.036210581

11 0.22818549 0.343667597 0.2592387 0.021608078 0.273098946 0.47320804 0.319647354 0.03488797 0.386937618 0.623595536 0.485522273 0.042838557

27 0.300078005 0.439910591 0.370115093 0.029914602 0.359511942 0.61496681 0.484071815 0.06078809 0.278956652 0.502782941 0.362975078 0.03516974

28 0.296753824 0.449769109 0.374206428 0.042116417 0.318073094 0.538664103 0.410581543 0.04287837 0.282238424 0.436518908 0.335735982 0.030229981

29 0.21708639 0.352994323 0.255526865 0.026141189 0.271778136 0.508611619 0.362222619 0.05563052 0.428571373 0.782226145 0.617226055 0.109558429

30 0.222405314 0.345168769 0.246882627 0.020142094 0.327586234 0.487849087 0.367398167 0.02960482 0.494421899 0.783878744 0.658128488 0.063898475

33 0.225570798 0.507936537 0.266711391 0.039141724 0.250612766 0.693090498 0.340078103 0.05975923 0.205007806 0.779737651 0.586438525 0.074240348


1 0.552359045 0.855456054 0.794586314 0.072722712 0.663521945 0.900053799 0.867722711 0.04397426 0.612872243 0.883248687 0.84294816 0.060562744

2 0.609549284 0.896992624 0.83749631 0.051511743 0.722789109 0.913174629 0.881876592 0.04190175 0.740564167 0.907243609 0.868372976 0.035040725

3 0.754965305 0.90097934 0.849769185 0.026795578 0.735375285 0.918723345 0.897078521 0.02220556 0.816037774 0.909234107 0.884305916 0.020223899

4 0.695882797 0.856492937 0.807233026 0.024613832 0.731164813 0.904155731 0.878258005 0.0302815 0.759912848 0.900648534 0.866776239 0.02021008

5 0.656438589 0.904458582 0.823889313 0.040799918 0.670146167 0.928620756 0.877935691 0.05239521 0.722935736 0.913267791 0.869606534 0.027186149

6 0.737383842 0.928885758 0.869982522 0.032609204 0.76253581 0.927751601 0.899636983 0.02194403 0.733363986 0.904746294 0.871797033 0.027659323

7 0.722490191 0.910402596 0.853471757 0.039459172 0.708324015 0.913862288 0.870048512 0.03566227 0.638501108 0.88059175 0.818158344 0.044899122

8 0.717022896 0.912622929 0.881872602 0.026901825 0.682776392 0.92371136 0.901109567 0.03033586 0.70532316 0.906574905 0.876002626 0.027135025

9 0.558393061 0.911637843 0.865388199 0.052669257 0.434415579 0.913387239 0.876472582 0.04916463 0.456953585 0.879655063 0.814448958 0.061592994

10 0.753495336 0.928860486 0.892028249 0.027817528 0.672171652 0.926642895 0.896013667 0.03068429 0.605195999 0.876787245 0.826135755 0.037377802

11 0.619311035 0.90832895 0.836675096 0.055183299 0.685135424 0.915881336 0.869683125 0.04284839 0.480473667 0.891120493 0.815073125 0.089248945

27 0.226887345 0.430184126 0.310174951 0.027912708 0.202220172 0.387040287 0.32254526 0.02748267 0.075392663 0.375679761 0.248249649 0.0892299

28 0.235409483 0.404417753 0.334166283 0.029383654 0.236836225 0.45067209 0.373118235 0.0416581 0.221863553 0.454731256 0.340293758 0.047017854

29 0.559911668 0.875730991 0.770485544 0.082654624 0.588452637 0.888372123 0.807636848 0.05937788 0.41344887 0.82866776 0.718258623 0.084343212

30 0.621115208 0.89222455 0.824671867 0.048545614 0.540689647 0.900510192 0.83687252 0.04821723 0.545610189 0.835685432 0.773782079 0.05217774

33 0.339339346 0.864985764 0.783526114 0.064642374 0.377139956 0.885516703 0.802017565 0.07347777 0.346060991 0.816137791 0.71952561 0.08634322

April 11 April 21 May 11

May 31 June 10 June 20



Figure 4. Soil Adjusted Vegetation Index

Figure 5. Soil Adjusted Vegetation Index – Zonal Statistics


1 1.580943227 1.61224997 1.593048634 0.008836062 1.588285446 1.642820239 1.606691613 0.01543988 1.625827789 1.70828402 1.65262661 0.022048629

2 1.581393838 1.620130301 1.590749854 0.007702969 1.588328958 1.662413597 1.600953853 0.01147044 1.638588667 1.705260038 1.650536703 0.011876427

3 1.574220777 1.615228415 1.581285758 0.004965171 1.581695676 1.620397687 1.589494359 0.00595241 1.629776835 1.692370415 1.650667522 0.012007297

4 1.579442859 1.62787056 1.586731884 0.006667506 1.587433934 1.649337888 1.596868092 0.0082911 1.623615146 1.703113556 1.635986991 0.01033028

5 1.57686162 1.613746524 1.584196881 0.004369459 1.584674001 1.650326729 1.593545776 0.00733868 1.619295597 1.691921711 1.648591545 0.01131304

6 1.576582432 1.630083323 1.591502054 0.011955583 1.5834378 1.674832344 1.606796748 0.02200002 1.636169672 1.744942188 1.676244342 0.024183513

7 1.573570848 1.621104598 1.586343845 0.008119981 1.587424159 1.663184404 1.604069865 0.01102608 1.622510076 1.808751464 1.726756828 0.038330218

8 1.575692892 1.63293469 1.584067371 0.00752538 1.581502438 1.677925587 1.594174346 0.01333509 1.648099303 1.765611768 1.684815008 0.011904864

9 1.576738 1.65309608 1.584116159 0.006915506 1.583676696 1.68112874 1.599506533 0.01084819 1.627984405 1.806457162 1.733018989 0.041216398

10 1.577611208 1.621821642 1.588485103 0.00729314 1.597369909 1.64463079 1.608326549 0.00808025 1.673415899 1.793119669 1.756763015 0.015839256

11 1.580759764 1.629731059 1.59173962 0.008866034 1.585935354 1.672720313 1.601424906 0.0142015 1.655004859 1.74353981 1.682774534 0.015023919

27 1.624217629 1.683492661 1.650585234 0.012003795 1.623704553 1.745128632 1.682678168 0.0276054 1.61435461 1.716231585 1.650300119 0.015524838

28 1.621801615 1.685556173 1.650495592 0.017946177 1.617416143 1.703632355 1.642919828 0.01661524 1.615407825 1.683828592 1.641207091 0.013303439

29 1.583691716 1.640934706 1.598848295 0.010566972 1.597005963 1.687422991 1.62795922 0.02008968 1.680779219 1.872512102 1.767182083 0.058653794

30 1.583593607 1.640587926 1.594964892 0.009145443 1.611874223 1.674567342 1.627756842 0.01190803 1.7180233 1.873600006 1.788854983 0.03751331

33 1.588511825 1.718678832 1.603949767 0.017826014 1.598983526 1.796313286 1.624320626 0.02506942 1.587134004 1.865952015 1.753477155 0.033474917


1 1.726521969 1.923930645 1.877984873 0.045651947 1.7872051 1.97989583 1.945160892 0.03780609 1.778481007 1.978460073 1.943925675 0.04595201

2 1.788124919 1.971998453 1.915755763 0.037523378 1.837387323 2.000406265 1.96285248 0.04128656 1.871536732 2.01096344 1.967726473 0.034739811

3 1.843719363 1.972512841 1.913505871 0.024722966 1.826060295 1.993945003 1.962012774 0.02240588 1.904405832 2.008549213 1.970473257 0.022200633

4 1.807512045 1.9211061 1.881955785 0.017351588 1.832970262 1.980351806 1.947345959 0.02406148 1.863712192 2.002870798 1.953118628 0.020128712

5 1.790775418 1.979909897 1.891485201 0.029629617 1.80400002 2.00408721 1.942370266 0.0389516 1.836752176 2.005668163 1.949648831 0.025383826

6 1.826950908 2.003376484 1.932627176 0.035151193 1.831675887 2.003905535 1.969303532 0.02741245 1.841526031 2.000044584 1.961587636 0.026324725

7 1.8256284 1.996057272 1.925560018 0.03788854 1.824463367 1.988216162 1.93551204 0.03189 1.78701973 1.96825242 1.903935323 0.036006144

8 1.821794748 1.990771413 1.948919362 0.029643261 1.798762679 1.998529434 1.965153347 0.02956571 1.822328448 2.003688574 1.960293161 0.027646425

9 1.718365669 1.98514843 1.932986137 0.044961788 1.665594101 1.981116891 1.939970062 0.03681626 1.700985909 1.964857697 1.902643961 0.044033474

10 1.859433651 2.004024982 1.961897747 0.026995812 1.801093102 2.001147985 1.960857571 0.02598422 1.780206203 1.965728998 1.913931822 0.028729838

11 1.774367809 1.980154634 1.913107816 0.042119153 1.808563232 1.989096045 1.939176826 0.03585873 1.712621212 1.981148481 1.908922764 0.057251832

27 1.606461763 1.693168044 1.636311367 0.011939986 1.60382843 1.672398567 1.644512675 0.01087785 1.545569658 1.680264115 1.624169299 0.036614415

28 1.605750203 1.671204567 1.642644436 0.012025518 1.609474897 1.706260681 1.665211931 0.01802096 1.606872559 1.724906445 1.661647622 0.022358353

29 1.735212207 1.95760119 1.870492763 0.054836534 1.765163898 1.969742179 1.893226443 0.04475905 1.712920785 1.956080437 1.864648693 0.051577191

30 1.789889097 1.972211838 1.905852325 0.040004265 1.751550794 1.972759843 1.914340941 0.03688975 1.763495326 1.954163551 1.901726962 0.039135353

33 1.643652201 1.931002378 1.87419447 0.038630423 1.666963816 1.957952976 1.888968985 0.04566911 1.665009975 1.928598523 1.854974366 0.048636158





Figure 6. Normalized Difference Water Index

Figure 7. Normalized Difference Water Index – Zonal Statistics


1 -0.417850614 -0.37047133 -0.38793933 0.012913523 -0.572656572 -0.47037375 -0.50406018 0.02435117 -0.59327209 -0.474264771 -0.528018811 0.031340009

2 -0.425678104 -0.36319998 -0.383101412 0.01247287 -0.564011157 -0.471853554 -0.494141814 0.01654247 -0.595103562 -0.49281764 -0.525325928 0.016220487

3 -0.426204771 -0.34401971 -0.365387597 0.010032857 -0.551491857 -0.46381247 -0.489824394 0.01147313 -0.597020268 -0.496278912 -0.540984677 0.0210489

4 -0.448075533 -0.35867369 -0.380096513 0.011456434 -0.563953519 -0.480834574 -0.49619547 0.01208518 -0.591929674 -0.492836714 -0.515133519 0.014505499

5 -0.425190806 -0.35679719 -0.375732856 0.008519062 -0.585315049 -0.474048436 -0.502882864 0.0138196 -0.6095891 -0.471491247 -0.544754776 0.021344802

6 -0.446683735 -0.35557428 -0.38593666 0.020054662 -0.628349185 -0.476784796 -0.520689213 0.03432675 -0.666042447 -0.499479324 -0.579103343 0.035699603

7 -0.441711247 -0.35364875 -0.378925437 0.014237489 -0.621529341 -0.477071553 -0.527079382 0.02287726 -0.738444269 -0.479652166 -0.645683639 0.055543593

8 -0.442925721 -0.35054705 -0.373891748 0.012364646 -0.627161503 -0.490259767 -0.516794003 0.02027974 -0.695450306 -0.554524302 -0.606194901 0.017290078

9 -0.468554437 -0.3490364 -0.372612782 0.011493396 -0.603369057 -0.442823559 -0.513203868 0.01934514 -0.742345393 -0.44338876 -0.653410244 0.054749807

10 -0.435250461 -0.35781381 -0.381783252 0.013688606 -0.588625669 -0.503722131 -0.534658438 0.01332239 -0.733897805 -0.548675418 -0.69386134 0.024336044

11 -0.444680899 -0.36310023 -0.389176757 0.015759462 -0.61049825 -0.487992764 -0.521884278 0.02199115 -0.668743789 -0.529488444 -0.59616046 0.026992395

27 -0.501531541 -0.43298623 -0.46540744 0.010916505 -0.652706146 -0.542343915 -0.603727335 0.02135519 -0.628842294 -0.479232162 -0.543857342 0.024535281

28 -0.51026392 -0.42348525 -0.468010869 0.019358033 -0.641770422 -0.536941171 -0.589144522 0.01699337 -0.59975487 -0.476402462 -0.528002422 0.033817292

29 -0.465927601 -0.37985665 -0.422002391 0.016374786 -0.664835215 -0.528492689 -0.599912489 0.0276743 -0.78898406 -0.606810153 -0.707964884 0.050435957

30 -0.465613723 -0.39143068 -0.414084048 0.012437471 -0.636441112 -0.556742251 -0.598071553 0.0149571 -0.790246248 -0.614352226 -0.730696585 0.029254856

33 -0.603826702 -0.40356314 -0.448128348 0.026285591 -0.791737437 -0.566975653 -0.609771292 0.02960786 -0.802667916 -0.547216952 -0.704421177 0.031652711


1 -0.810963273 -0.63119709 -0.774529324 0.043442471 -0.868725836 -0.725938082 -0.848573868 0.02796616 -0.817439616 -0.637250066 -0.786334819 0.039156933

2 -0.842897117 -0.67806476 -0.807571096 0.031735986 -0.890800834 -0.767198265 -0.869021612 0.02639419 -0.837132514 -0.724124312 -0.806355298 0.024036776

3 -0.84939301 -0.756226 -0.819170048 0.01920375 -0.903082073 -0.784663022 -0.882932494 0.0161957 -0.844147384 -0.76504302 -0.821072764 0.017094703

4 -0.813289165 -0.718454 -0.791036926 0.013404012 -0.888951898 -0.78379643 -0.869403673 0.0182781 -0.82766068 -0.734965682 -0.803712069 0.013303965

5 -0.864541888 -0.70887387 -0.804504858 0.024850915 -0.904327989 -0.759904504 -0.874255329 0.02969879 -0.847758353 -0.722145855 -0.808947018 0.018397111

6 -0.867483795 -0.74131852 -0.827194799 0.022675365 -0.905409932 -0.784793496 -0.878505248 0.01458567 -0.842374265 -0.714869499 -0.810606466 0.020274386

7 -0.85711956 -0.72525442 -0.815640086 0.026218873 -0.880740225 -0.770660102 -0.852338999 0.01928771 -0.81092149 -0.659637809 -0.761980324 0.027771897

8 -0.863083363 -0.74183899 -0.843362845 0.017530077 -0.907869577 -0.747930586 -0.889203819 0.02054799 -0.842894614 -0.706134081 -0.817464908 0.019153056

9 -0.865502119 -0.60384613 -0.830215064 0.037156089 -0.893086135 -0.601885498 -0.865449515 0.03188624 -0.824105442 -0.558158517 -0.765335099 0.042537822

10 -0.871136606 -0.75375116 -0.852734774 0.018896937 -0.90231353 -0.737259984 -0.882811421 0.01978526 -0.813715458 -0.632119536 -0.776494393 0.025845908

11 -0.845357299 -0.67490244 -0.80129572 0.032855052 -0.886128724 -0.763473868 -0.8551685 0.02238621 -0.822134972 -0.580911815 -0.768823477 0.050545361

27 -0.59110868 -0.44978905 -0.524813841 0.023012245 -0.626277387 -0.488318831 -0.574959204 0.02316053 -0.538610995 -0.187828183 -0.415766538 0.10646306

28 -0.604361355 -0.46099693 -0.541642579 0.032452092 -0.667536974 -0.460332751 -0.603615238 0.03769944 -0.590614498 -0.393584847 -0.511190308 0.039382818

29 -0.848370194 -0.67308259 -0.784744977 0.040932145 -0.875305533 -0.714356899 -0.827289862 0.02869368 -0.787342608 -0.551374912 -0.713302668 0.044635188

30 -0.853649795 -0.69227099 -0.808873994 0.02577338 -0.876861572 -0.679278314 -0.84079449 0.02564929 -0.78558594 -0.602575421 -0.736743252 0.031035484

33 -0.828264832 -0.62477767 -0.782016546 0.026917198 -0.8624928 -0.677914083 -0.819562352 0.02656497 -0.757809699 -0.522840202 -0.708814766 0.03938476





DISCUSSION/CONCLUSION

Through the various indices processed over Sentinel – 2 data, satellite imagery proved that it is a reliable source for

analysis and for precision agriculture applications. All the indices that were used showed compatibility with one

another as well as with data provided from the farmer in Lebanon. The peak of the season based on the imagery

analysis was in late May through mid-June which is based on the farmer’s input just before the crops were harvested

in late June. In addition, the water index showed that the water content was not uniform throughout the fields and that

is related to the fact that the field is not at one level which according to the grower influences how the water is

distributed around the field.

This work faced some challenges mainly in the communication with the Lebanese growers as well as lack of precise

data that could be used as a control for our results. In addition to the limitation in Sentinel – 2 imagery over the study

area, which having access to Sentinel – 2B would help close the gap and cover the missing dates but due to the recent

launch of the satellite, the imagery that is publicly available is very limited. Hence, once the Sentinel – 2B data is

publicly available, more thorough analyses could be done.

Despite the limitations in this project, it still has long ways to come. While the only dataset used for this was Sentinel

– 2, processing Planet RapidEye and 4Band data will allow the more accurate analysis due to the almost daily coverage

over the study area as opposed to much less with Sentinel – 2. In addition, Planet data provides a higher resolution

and having more data will allow the option to run regression models on the data which was not feasible with this

limited dataset here so this is the next step.



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