Pecora 20 - Observing a Changing Earth; Science for Decisions— Monitoring, Assessment, and Projection
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]
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
Pecora 20 - Observing a Changing Earth; Science for Decisions— Monitoring, Assessment, and Projection
November 13-16, 2017 Sioux Falls, SD
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.
Pecora 20 - Observing a Changing Earth; Science for Decisions— Monitoring, Assessment, and Projection
November 13-16, 2017 Sioux Falls, SD
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
Pecora 20 - Observing a Changing Earth; Science for Decisions— Monitoring, Assessment, and Projection
November 13-16, 2017 Sioux Falls, SD
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)
Pecora 20 - Observing a Changing Earth; Science for Decisions— Monitoring, Assessment, and Projection
November 13-16, 2017 Sioux Falls, SD
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
Field_ID MIN MAX MEAN STD MIN MAX MEAN STD MIN MAX MEAN STD
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
Pecora 20 - Observing a Changing Earth; Science for Decisions— Monitoring, Assessment, and Projection
November 13-16, 2017 Sioux Falls, SD
Figure 4. Soil Adjusted Vegetation Index
Figure 5. Soil Adjusted Vegetation Index – Zonal Statistics
Field_ID MIN MAX MEAN STD MIN MAX MEAN STD MIN MAX MEAN STD
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
Field_ID MIN MAX MEAN STD MIN MAX MEAN STD MIN MAX MEAN STD
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
April 11 April 21 May 11
May 31 June 10 June 20
Pecora 20 - Observing a Changing Earth; Science for Decisions— Monitoring, Assessment, and Projection
November 13-16, 2017 Sioux Falls, SD
Figure 6. Normalized Difference Water Index
Figure 7. Normalized Difference Water Index – Zonal Statistics
Field_ID MIN MAX MEAN STD MIN MAX MEAN STD MIN MAX MEAN STD
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
Field_ID MIN MAX MEAN STD MIN MAX MEAN STD MIN MAX MEAN STD
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
April 11 April 21 May 11
May 31 June 10 June 20
Pecora 20 - Observing a Changing Earth; Science for Decisions— Monitoring, Assessment, and Projection
November 13-16, 2017 Sioux Falls, SD
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.
Pecora 20 - Observing a Changing Earth; Science for Decisions— Monitoring, Assessment, and Projection
November 13-16, 2017 Sioux Falls, SD
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