Application of Remote Sensing Techniques inLocating Dry and Irrigated Farmland Parcels

Selver Senturk∗, Serdar Bagis†, Burak Berk Ustundag‡Turkish Agricultural Monitoring & Information Center

Istanbul Technical University

Maslak, Istanbul 344690∗selver.senturk@tarbil.com, †serdarbagis@yahoo.com, ‡berk@berk.tc

Abstract—For structuring national irrigation policies and de-termining the exact yield production shares separately generatedfrom irrigated and unirrigated farmland practices fast and simpleto employ methods are of great importance. In this work,through utilizing entire satellite image frames, with no maskingor cropping any parts out, a local, parcel-based NormalizedDifference Vegetation Index (NDVI) and Normalized DifferenceMoisture Index (NDMI) means and variance techniques’ mappingabilities were investigated. This was the preliminary stage inthe delineation of irrigated and non-irrigated parcels. Although,even at this phase we obtained mapping results with reasonablyhigh precision, a further process was performed using LandSurface Temperature (LST) data retrieved from Landsat 8satellite images. LST tuning up produced irrigated areas to bemapped with accuracy rates escalating above 89%. The resultsobtained suggest that the NDVI, NDMI means and varianceapproach coupled up along with LST data holds the capacity toassist in building up trustworthy agrarian statistics for TARBILproject and in formation of a robust Agricultural GeographicInformation System on national basis.

Keywords—TARBIL project, Remote sensing, NDVI, NDMI,LST, mapping dry and irrigated parcels, Landsat-8

I. INTRODUCTION

In Turkey, the total agricultural area amounts to nearly one-third (about 28 Million ha) of the country’ surface area. Basedon a recent inquiry carried out by the General Directorate ofState Hydraulic Works the upper limit of economically irriga-ble land makes circa 8.5 million hectares. In relation to thestated figures from year 2011, the total irrigated land is on theorder of 5.61 million hectares. However, the irrigation alone to-day exhausts almost three-fourths from the national total waterconsumption. Furthermore, the average of exploitable amountof water per capita is 1,500m3, classifying Turkey as a waterstress country [1]. In addition, the State Institute of Statistics(TUIK) has approximated Turkey’s population to reach 100million before 2030 [2]. It is obvious that these circumstanceswill definitely worsen the situation by dipping the annualavailable amount of water per capita down to 1,000m3. So, inorder to diminish the rough decline in available water resourcesthe future national projections should be alertly restructured.As the present irrigation patterns need to be rapidly altered andproperly regulated, consistent geospatial information on waterconsumption is necessary. A significant portion of irrigatedcroplands in Turkey are fed by groundwater, and there areapproximately more than 180,000 off the record groundwater-pumping wells merely used in irrigation [1]. For that reason,

continuous and practical irrigation monitoring is of criticalimportance. In addition, this information is notably valuablein forming yield-prediction models as well. Indeed, one of themajor statistics used by agricultural policy and decision makersin agrarian site-specific crop management is the productionamount separately yielded from dry and irrigated farm landsystems. Theoretically, with the assistance of water-delivery ir-rigation records the division between these two main practicesappears to be quite straightforward. However, as mentionedabove, incidents like tapping from the urban water networkfor irrigation activities, unsanctioned groundwater withdrawals,along with occasionally inaccurate, partial and inconsistentirrigation records it is difficult to secure information reliableenough for further analysis in building long-term nationalpolicies. In this paper our objective was to explore accurateand easy to employ methods of localizing the irrigated andnon-irrigated parcels using images derived from the compar-atively newly launched Landsat 8 space satellite. NormalizedDifference Vegetation Index (NDVI), Normalized DifferenceMoisture Index (NDMI) along with Land Surface Temperature(LST) data were exploited and used in parcels mapping for anarea in the south-eastern part of Turkey.

II. DATA AND METHODS

A. The Study Area

The exploration of appropriate methods on mapping ir-rigation was carried out in a region covering the majorityfrom Sanlıurfa and Adıyaman, and in parts from Gaziantep,Malatya, Diyarbakır, which are some of the important southand southeastern agricultural and administrative provinces ofTurkey. The chosen Landsat-8 173/34 scene and study area,Figure 1, lies in the upper part of Tigris–Euphrates Basinthat is distinguished with an arid and semi-arid climate. Ac-cording to an updated version of Koppen-Geiger climate mapclassification the region mostly is characterized with climatethat is subjected to little precipitation during most of the year(Koppen-Geiger climate classification Bsa, and in a very smallpart is classified as Csa), where the potential losses of waterfrom evaporation and transpiration considerably exceed theatmospheric input [3].The extensively irrigated Harran Plainwas selected as a reference location and deliberately includedwithin the study region. This extremely fertile plain is locatedaround 44 kilometers away from the southeastern part ofSanlıurfa city and extends its range down to the Syrian border.The annual mean precipitation is around 430 mm, while the

Fig. 1: Location of the selected Landsat-8 Path173/Row 34scene and study area

mean annual evaporation is between 1251 to 1750 mm, [4].The main cultivated vegetation types are cereals, safflower, andcotton. The water supply is done from various water reservoirsand the Ataturk Dam, which is estimated to double Turkey’sirrigable farmland [5].

B. The Data

The used Landsat 8 satellite scene images were acquiredfrom the USGS EROS archive. Landsat 8 measures diverseand atypical ranges of frequencies along the electromagneticspectrum. The bands used in this work were Band 4 – thevisible red, Band 5 – the near infrared, or NIR, Band 6 -the shortwave infrared, SWIR 1 (1.56–1.66μm), Band 7 - theshortwave infrared, SWIR 2 (2.10–2.30 μm), Band 9 – theclouds band, Bands 10 and 11 – the thermal infrared, or TIR1&2. The first 4 bands were used in producing the NDVIand NDMI indices, the last two for delineation of the groundsurfaces with different LSTs (Land Surface Temperature) [6].The cloud band (Band 9) was used as land cover mask, wherethe cloud footprints were present in the scenes. We did not useany other masks other than aforementioned, and it was usedonly in 7 out of 25 images.

C. The Method

The applied method in the allocation of irrigated and non-irrigated areas is based on some of the known vegetation andwater EM reflection realities. Stemmed from the distinctiveresponses of healthy vegetation to near infrared, red andshortwave infrared frequencies the following three useful keyvegetation indices were used as inputs derived from Landsat-8images:

1) NDVI: The Normalized Difference Vegetation Index isderived from red and near infrared band is an explicit remotesensing proxy indicating the vegetation vigor, which to greatextent is correlated with irrigation efficiency (Equation 1).Usually irrigated crops display higher annual NDVI values

Fig. 2: The architecture of the applied method

than non-irrigated crops in the same local area [7][8][9][10].

NDV I =NIR(Band5)−RED(Band4)

NIR(Band5) +RED(Band4)(1)

2) NDMI: The Normalized Difference Moisture Index, onthe other hand is a resultant from red and short wave infraredband/s (Equation 2 and 3). The reflectance of vegetation inshortwave infrared region, in our case bands 6 and 7 forLandsat-8, is more heterogeneous, counting on the crops typesand their relevant water content. Farther than the absorptionbands (1.45, 1.95 and 2.50 (eksik), where water has intenseabsorption), in the shortwave infrared region, the reflectanceof the green biomass normally drops when leaf liquid watercontent increases. So, this also suggested that it can be possiblyused as another proxy for identifying irrigated from unirrigatedcrops from same type and within the same local region.

NDMI6 =SWIR1(Band6)−NIR(Band5)

SWIR1(Band6) +NIR(Band5)(2)

NDMI7 =SWIR2(Band7)−NIR(Band5)

SWIR2(Band6) +NIR(Band5)(3)

Considering that in our work we did not make use ofany spectral signatures taken at Earth’s surface the removal ofany atmospheric effects was not necessary as a preprocessingmeasure. However, prior to any further step, as we were tryingto classify multiple of Landsat-8 satellite images acquiredat different times of the year, we converted images’ DNs

to their actual spectral radiance values at satellite’s sensorusing ENVI-5.0 software. Merely for a small number of theimages, retaining comparatively high cloud cover ratio, weused Band-11 as a masking tool afore any clustering. TheNDVI, NDMIs indices and Land Surface Temperature (LST)maps were generated for each individual image. The workflow as it is shown in Fig. 2. Subsequently, k-means algorithmclustering method helped to distinguish three predefined maindata sets for each satellite image. The preliminary three mainclasses were labeled as “irrigated”, “non-irrigated” and as“undefined”. There is an apparent variance in NDVI valuesbetween irrigated and unirrigated parcels, which is less dis-tinguished when enough precipitation exists. However, oncethe overall precipitation level drops below the one needed forhealthy vegetation grow, as it is in the image number 7 fromAugust 14th, 2013 then the NDVI difference between the twois much more evident (Fig. 4). Following the formation of thepreliminary three main clusters mentioned above, we used themean and standard deviation based histograms to readjust themargin values for NDVI, MDMIs indices and LST. In figure 3the preliminary values of NDVI means and standard deviationranges are given for each image. After tuning up the thresholdvalues, we overlapped the resultant indices and LST maps fora given acquisition date images and next extracted the irrigatedregions complying simultaneously requirements like NDVI >0.25 and LST < 41◦C. Such as, any NDVI value that wasgreater than the specified irrigation threshold, but then again,which was not less than the specified thresholds for LST andNDMI values were classified into “non-irrigated” class. Orelse, eventually all NDVI’s falling below our least specifiedNDVI, were grouped into “undefined” class. The “undefined”class comprises mainly the area with water and no vegetation.LST filtering was used for the separation between the irrigatedagricultural areas and a few existing woodland or forest areas.All aforementioned areas exhibit high NDVI values; neverthe-less, the LST values for woodlands are comparatively higherthan those for the irrigated agricultural areas. So, within ourbuilt procedure workflow there were implemented a numberdecision supporting steps for further processing. Eventually,some of the initially classified images were reclassified inorder to fit into the predefined ranges for “irrigated” or “non-irrigated” classes.

III. RESULTS AND DISCUSSIONS

With the aim to map irrigated from unirrigated parcels, inthis study we generated NDVI, NDMI-6 & 7, and LST mapsfrom 25 Landsat-8 images obtained during the period starting24 April 2013 and ending in June 2014. By means of theseproduced maps we extracted the temporal NDVI, NDMI andLST dynamics to support the decision in accurate irrigationmapping, without the support of any cadastral informationon irrigated and unirrigated parcels. The results obtainedthrough setting decision thresholds and logical statementswere assessed and compared to the data obtained from 44robo-stations present within the study region and belongingto TARBIL project network (given in Fig. 5). The resultscollected from the overlapped NDVI & NDMI and LST signalsstrength received indicate that though the overall accuracy ishigher than 89% and a kappa statistic of 0.78, the accuracy forirrigated parcels falls down to 79%, with errors of omissionequal to 0.21. The accuracy for non-irrigated is 100%. The

Fig. 4: NDVI distance variance between irrigated and unirri-gated parcels

Fig. 5: Tarbil’s network robo-stations used as ground refer-ences

lower accuracy in irrigated parcels was found to be mainlybecause of the parcels, which utilize drip irrigation (knownalso as micro or trickle irrigation). This type of irrigatedparcels indicates higher LST values than the parcels, whichare watered through surface irrigation techniques. However,with the reality of water scarcity, the deficit in irrigation willincrease more rapidly. Accordingly, there will be an urgentneed in replacement of existing surface irrigation practiceswith drip or subsurface textile irrigation methods in order toguarantee the sustainability of the state owned and privateirrigation projects. So, the results obtained suggest that oncethe irrigation methods change to localized type of wateringLST (Land Surface Temperature) derived methods will weighless in mapping irrigated and non-irrigated areas. Therefore,alternative ways need to be customized if our aim is toensure precise agricultural geospatial statistics with referenceto irrigation.

#1 cluster / "irrigated"

#2 cluster / "non-

irrigated"

#3 cluster / "water +

undefined"

#1 cluster / "irrigated"

#2 cluster / "non-

irrigated"

#3 cluster / "water +

undefined"1 24-Apr-13 LC81730342013114LGN01 0.4173 0.3564 -0.1381 0.1434 0.0946 0.26052 26-May-13 LC81730342013146LGN00 0.2375 0.2882 -0.0477 0.0997 0.1051 0.15933 11-Jun-13 LC81730342013162LGN00 0.2045 0.2864 -0.0853 0.0548 0.0858 0.15274 27-Jun-13 LC81730342013178LGN01 0.1817 0.2833 -0.4125 0.0447 0.0719 0.07566 29-Jul-13 LC81730342013210LGN01 0.1808 0.4019 -0.2076 0.0571 0.1154 0.05877 14-Aug-13 LC81730342013226LGN00 0.1827 0.4255 -0.2775 0.0682 0.1399 0.10338 30-Aug-13 LC81730342013242LGN00 0.1803 0.4165 -0.2043 0.0566 0.1384 0.03559 15-Sep-13 LC81730342013258LGN00 0.1763 0.4128 -0.3018 0.0552 0.1525 0.100910 1-Oct-13 LC81730342013274LGN00 0.1752 0.371 -0.3361 0.0594 0.1123 0.073311 17-Oct-13 LC81730342013290LGN00 0.1643 0.3193 -0.2822 0.0414 0.0984 0.076212 2-Nov-13 LC81730342013306LGN00 0.1492 0.2565 -0.1979 0.0393 0.0543 0.083213 18-Nov-13 LC81730342013322LGN00 0.1294 0.1689 -0.1635 0.0492 0.0781 0.098514 4-Dec-13 LC81730342013338LGN00 0.0518 0.0486 0.0291 0.0277 0.0154 0.005915 20-Dec-13 LC81730342013354LGN00 0.1498 0.1219 -0.371 0.0764 0.0621 0.060816 5-Jan-14 LC81730342014005LGN00 0.1654 0.1134 -0.2002 0.0844 0.0661 0.134617 21-Jan-14 LC81730342014021LGN00 0.0974 0.0533 0.037 0.0694 0.0249 0.007218 6-Feb-14 LC81730342014037LGN00 0.2096 0.1459 -0.264 0.1032 0.0695 0.15319 26-Mar-14 LC81730342014085LGN00 0.2959 0.2176 -0.0764 0.1426 0.0986 0.088820 11-Apr-14 LC81730342014101LGN00 0.3295 0.2532 -0.2192 0.1448 0.0962 0.146121 27-Apr-14 LC81730342014117LGN00 0.3322 0.3043 -0.1589 0.1257 0.0718 0.164122 13-May-14 LC81730342014133LGN00 0.2275 0.2333 -0.0975 0.0742 0.0753 0.112923 29-May-14 LC81730342014149LGN00 0.1728 0.2348 -0.0356 0.058 0.0918 0.06224 14-Jun-14 LC81730342014165LGN00 0.1718 0.2738 -0.3133 0.042 0.082 0.108525 30-Jun-14 LC81730342014181LGN00 0.1636 0.3094 -0.1944 0.0381 0.0714 0.1355

NDVI mean values NDVI standard deviation values

# Acq. Date Landsat Scene Identifier

Fig. 3: NDVI initial means and standard deviation values of the image set used

IV. CONCLUSION

Irrigated agriculture aided not only in overcoming thepoverty in the majority of the world, but it also multipliedthe varieties of cultivated crops [11]. Moreover, it addeda big deal of value to the countries’ economic and socialdevelopment. Inversely, as the worlds’ population and theirdemands gradually grow the fresh water shortage is and willbe frequently confronted problem. Today and more so in thefuture, irrigated agriculture will come off under water scarcity.Therefore proper regulation and management of water could bedone mainly though accurate geospatial irrigation mapping in-formation. With TARBIL project in Turkey one of the primarygoals is to control agricultural supplies and reduce excessivewater use and at the same time to maximize crop yield perunit of water consumed. This can be achieved with preciseirrigation mapping, modeling new irrigation schemes with lessnegative environmental impact, in addition to reshaping theexisting systems to more efficient and sustainable standards.

ACKNOWLEDGEMENT

This research was funded by TR Ministry of Food, Agri-culture and Livestock and ITU TARBIL Agro-InformaticsResearch Center.

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