Use of Remote Sensing techniques for spatial analysis of topsoil

properties in the semiarid region of Portugal

Ana Sofia das Neves Moreiraana.sofia.moreira@tecnico.ulisboa.pt

Instituto Superior Tecnico, Lisboa, Portugal

September 2016

Abstract

Desertification has an extension up to 35% of total Earth land surface and cause the loss ofecosystem services. With increasing importance given to the the need for soil information hasbeen increasing. Remote sensing complement in situ soil sampling by providing meaningful data.In this study the tasseled cap transformation was used to obtain the overall brightness, greennessand wetness of two Landsat images, which cover the regions of Alentejo and Algarve in August2009. By performing a principal components analysis between the three generated features and theLUCAS topsoil survey samples, collected in the summer of 2009, it was concluded that nitrogenand organic carbon are the chemical properties that correlate more strongly with soil brightness,being nitrogen the one that best explains brightness variation, according to the linear regressionmodels. Areas with low brightness have higher concentrations of nitrogen and organic carbon,therefore higher organic matter content. The presence of organic mater in soils is a good indicator ofsoil quality, since it has the ability to store nutrients for plant growth. Although it has limitations,soil brightness arises as a qualitative and quantitative soil quality index that can to help decision-makers.

Keywords: Remote Sensing, soil degradation, Landsat imagery, Portugal, soil brightness, LUCASsurvey

1. Introduction

Over the years the study of soil has gained increas-ing importance due to the increase of desertificationand consequent loss of ecosystem services. Suchservices are carbon sequestration, supply of fresh-water, fibers, wood and food and maintenance ofbiodiversity have a direct impact on the develop-ment of a region or country. Desertification is de-fined as ”land degradation in arid, semi-arid anddry sub-humid areas resulting from various factors,including climatic variations and human activities”(United Nations, 1994). It is associated with soilproperties changes and poor vegetation growth, re-sulting in soil degradation and decreases in land po-tential productivity (D’Odorico et al., 2013).

Due to the increase demand of soil information,since 2006 the EUROSTAT has carried out LandUse/Land Cover Area Frame Surveys (LUCAS)with the aim of gathering harmonised informationin situ on land use and land cover and their changesover time. In 2009 this survey was extended by theEuropean Commission together with the Join Re-search Center (JRC) to sample the main propertiesof topsoil (Toth et al., 2013).

Soil is the top layer of the earth’s crust com-posed by mineral particles, organic matter, water,air and living organisms (European Commission,2016; Jenny, 1994). The mineral components of soilare sand, silt and clay, and their relative proportionsdetermine the soil texture. It affects soil behaviour,in particular its retention capacity for nutrients andwater. Nitrogen, phosphorus and potassium are thethree major nutrients necessary for plant growth.Nitrogen is necessary in the formation of proteinsand enzymes. It is directly involved in metabolicprocesses that allow the synthesis and energy trans-fer. Moreover it is part of the chlorophyll molecule,responsible for photosynthetic processes. Phospho-rus is responsible for the development of roots, flow-ers, seeds and fruits. Potassium increases the resis-tance of plants to plagues, diseases, dryness andcold (Neves, 2009). For soils to be able to supplynutrients for plant growth, organic matter must bepresent in the soil composition. Soil organic matter(SOM) is composed by organic compounds and in-cludes plant, animal and microbial material, bothliving and dead (Bot and Benites, 2005). SOM istypically estimated to contain 58% C, being denom-

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inated as ’soil organic carbon’ (Pribyl, 2010). Theremain components are oxygen, hydrogen, nitrogenand a small percentage of secondary plant nutrients(Neves, 2010). It increases soil fertility by provid-ing cation exchange sites and acting as reserve ofplant nutrients. During the process of organic mat-ter mineralization bacteria digest organic materialand release Ca+2 , K+, PO2−

4 , CO2, H2O, NH+4 and

NO−3 (Neves, 2007) which are assimilated by plants

(Xu et al., 2012).

Remote sensing is in a good position to providemeaningful spatial data for studying soil propertieson various spatial scales. It is the acquisition ofinformation about an object or phenomenon with-out making physical contact with the object andtherefore is a complement, or even an alternative toon site observation. Therefore, soil texture, organiccarbon and soil moisture are some of the soil prop-erties studied, in recent years, by several authors.

Regarding soil texture, Apan et al. (2002)concluded that ASTER bands 2 (visible red), 8(SWIR), and the first principal component (ofbands 1 to 9) are the best layers to use for dis-criminating soil colour features, which allows to dis-tinguish between soils with loamy sand and heavyclay. Soil organic matter is mainly concentrated onthe topsoil layer, once it is exposed to the sun’sradiation, it is a perfect property to be assessed byremote sensing technology. Rossel et al. (2006) con-cluded that considering the visible part of the spec-trum the organic carbon content could be studiedusing the soil colour as index. Dark soils typicallycontain more soil organic matter than pale soils,higher organic carbon content is due to the effectof saturated organic matter. Some researchers usedstatistical approaches to map organic carbon con-tent with extensive calibration by soil samples, forexample, Selige et al. (2006) used linear regres-sion and Partial Least-Square Regression (PLSR)obtaining satisfying results for soil organic matter.Finally, soil moisture is deeply studied by manyauthors, it can be retrieved from different remotesensing methods. Wagner et al. (2007) determinedthe Soil Water Index (SWI), this index combinesERS (European Remote Sensing) satellite data andMETOP data, a series of three polar orbiting me-teorological satellites. Paulik et al. (2014) com-pared in situ soil moisture data of 664 stations withthe SWI data and obtained a Pearson correlation of0.54.

The objective of this study is to spatially anal-yse soil properties in the Alentejo and Algarve re-gions using satellite images acquired in the summerof 2009. For such, remote sensing processing tech-niques and GIS tools were applied, using ENVI andArcGIS programs, respectively.

2. Study Area

The study area covers the regions of Alentejo andAlgarve. Considering the aridity index in Portu-gal (IM and INAG, 2003), these regions have thelowest aridity value, between 0.34 and 0.5, whichcorresponds to a semi-arid area. This means thatthe vegetation cover is low, which is essential whenusing satellite images to study topsoil properties. InAugust 2009, the time of year when the satellite im-ages for this study were acquired, the average tem-perature in the region was around 26◦C, reachingmaximum temperatures above 34◦C (IPMA, 2009).Moreover, precipitation is zero for the consideredperiod (SNIRH, 2009). According to the land usemap of Portugal (DGT, 2007) agricultural fieldscover most of the study area, once agriculture is thethird activity that most contribute to local econ-omy (INE and PORDATA, 2014). The geologicalcharacterization of the site has a direct influence onthe land use. The study area is covered mainly bycambisols, lithosols, luvisols and podzols only in theNorthwest zone of the study area (APA, 1982).

3. Methodology

3.1. Datasets

Two datasets were used for the research (figure 1and table 1). Given the fact that the samples of theLUCAS Topsoil survey were collected during theSummer of 2009 the chosen satellite images are alsofrom that period. Considering the climatic charac-teristics of the region, August is one of the driestmonths of the year, consequently, the chance of hav-ing dense vegetation is low. Data was acquired bythe satellite on the 21st August 2009 and has 0% ofcloud cover for both images.

The satellite that acquired the datasets is Land-sat 5 and, it was launched March 1st 1984 and de-commissioned June 5th 2013 (USGS, 2014). Thedata was acquired by the Thematic Mapper (TM)sensor. TM data are sensed in seven spectral bandssimultaneously, each band represents a differentportion of the electromagnetic spectrum (Table 1).

Table 1: TM bands and correspondent wave length(USGS, 2014)

Band Number Wave length (µm) Resolution1 0.45 - 0.52 302 0.52 - 0.60 303 0.63 - 0.69 304 0.76 - 0.90 305 1.55 - 1.75 306 10.41 - 12.05 1207 2.08 - 2.35 30

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Figure 1: Mosaic images represented in true colour(R-3, G-2, B-1), path: 203, row: 33 (top image) and34 (bottom image) (USGS, 2009)

Table 2: Geographic Coordinates for each dataset

Path 203Row 33 34

Upper left cornerLatitude 39.85588 38.43690

Longitude -8.71826 -9.12948

Upper right cornerLatitude 39.81564 38.40808

Longitude -5.93211 -6.39794

Lower left cornerLatitude 37.94457 36.51969

Longitude -6.01313 -9.12621

Lower right cornerLatitude 37.90696 36.49279

Longitude -8.72571 -6.46350

3.2. Pre-processingThe atmosphere influences the amount of electro-magnetic energy that is sensed by the detectors ofan imaging system, and these effects are wavelengthdependent (Chavez, 1988). Especially for imag-ing systems such as the Landsat Thematic Map-per (TM) that record data in the visible and near-infrared parts of the spectrum. The atmosphereaffects images by scattering, absorbing, and refract-ing light (Chavez,1988). Campbell (2011) refersthat an observed digital number (DN) value mightbe in part the result of surface reflectance and inpart the result of atmospheric scattering. Radio-metric pre-processing is a technique that influencesthe brightness values of an image in order to correctsensor malfunctions or to adjust the digital values tocompensate the atmospheric degradation describedabove (Campbell & Wynne, 2011).

Without performing any pre-processing tech-nique, it was observed that the minimum value forall the bands is already zero, which means thatthere isn’t an atmospheric influence in the qualityof the dataset. This is explained by the fact that,as the TM incorporates an internal calibrator, in2003 an exponential-decay model was implementedto represent the radiometric response or gain of eachreflective band as a function of time since the launchof the satellite. This model and the bands coeffi-cients generate a day-specific band-average lookuptable (LUT) of detector gains for use in process-ing. After the application of the LUT gains, thedata are rescaled to a fixed radiance range betweenLMIN (corresponding to zero DN) and LMAX (cor-responding to 255 DN) (Chander et al.,2007).

3.3. Data Processing

Tasseled Cap Transformation

For the processing of the data the ENVI (Har-ris Geospatial, 2015) and ArcGIS software (ESRI,2011) were used. ENVI is a geospatial imagery anal-ysis and processing software, it is used for severalapplications, such as agriculture, mineral resources,oceanography, urbanism and land cover. ArcGISis a geographical information system that allowsworking with maps and geographic information.

Using ENVI interface a Tasseled Cap Transfor-mation (Kauth and Thomas, 1976) was performedfor both images. It is performed by taking linearcombinations of the original image bands. Eachtasseled-cap band (TC) is created by the sum ofimage band 1 times a constant plus image band 2times a constant, and so on. The coefficients used tocreate the tasseled-cap bands (Table 3) are derivedstatistically from images and empirical observations(Campbell, 2011). The tasseled cap generate thesame number of bands as the inputs bands, how-ever the amount of information that a band containsis lesser through the bands. Only the first threebands (brightness, greenness and wetness) containuseful information, while the others are mostly im-age’s ”noise”.

Table 3: Thematic Mapper Tasseled Cap Coeffi-cients, adapted from Crist and Cicone (1984)

Feature Brightness Greenness WetnessBand 1 0.3037 -0.2848 0.1509Band 2 0.2793 -0.2435 0.1973Band 3 0.4743 -0.5436 0.3279Band 4 0.5584 0.7243 0.3406Band 5 0.5082 0.0840 -0.7112Band 7 0.1863 -0.1800 -0.4572

The first band (TC1) represents the overallbrightness of the image, it is a weighted sum of allthe six bands. The second (TC2) is greenness and is

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associated with green vegetation, while the last one(TC3), wetness, is associated with soil moisture.

NDVI and Land UseIn order to restrict the study area to surfaces

without dense vegetation cover the vegetation in-dex, NDVI was measured. Calculations of NDVIfor a given pixel always result in a number thatranges from minus one (-1) to plus one (+1). Areasof barren rock, sand, or snow usually show very lowNDVI values (approximately between 0 and 0.1).High NDVI values correspond to dense vegetation(between 0.6 to 0.9) and negative values to waterbodies (USGS, 2015). After computing the NDVIit was necessary to intersect it with the land use sothat the study area only covers natural areas, elim-inating agriculture, urban areas and water bodies.This step was performed in ArcGIS and only forestsand natural and semi-natural areas were selected inthe land use mao and afterwards the ”extract bymask” tool between the NDVI raster and the landuse (mask data) was used. The resultant layer is araster with NDVI values only for the selected landuse.

Finally, using the output raster from the previousstep, the pixels with an NDVI value between 0 and0.1 were selected, in order that the soil was as bareas possible. This feature was then used as a maskfor the extract by mask tool of the resultant imagesfrom the tasseled cap transformation. The outcomewas divided in the different bands and converted topoints, so that each band would have an attributetable with the value of the respective feature of eachpixel.

Topsoil Survey Samples

Figure 2: Samples collected inthe study area, in natural areas

There is a totalof 205 samplescollected withinthe study area,of which 71 arefrom naturalareas (figure 2).At each samplingsite around 0.5kg of topsoil,between 0 and 20cm depth, werecollected. Thesamples werethen dispatchedto a centrallaboratory foranalysis (Tth etal., 2013). The

properties measured for each sample are coarse(%), clay (%), silt (%), sand (%), pH in H2O and inCaCl2 (-), organic carbon (g/kg), CaCO3 (g/kg),nitrogen (g/kg), phosphorus (mg/kg), potassium

(g/kg) and cation-exchange capacity (cmol(+)/kg).Considering only the samples collected in natural

areas, for each of them three buffers were defined,with 500, 1000 and 1500 meters. The objective ofusing the buffers is to group the pixels that are in-side each buffer and perform statistical calculationsof brightness, greenness and wetness. The resultswere then compared with the values of soil proper-ties of the respective sample, instead of comparingthose values with the closest pixel, to have a rep-resentative value of the area covered for each sam-ple. Each buffer layer was intersect with the pointfeature layers. The resultant attribute tables wereexported to an excel sheet where statistical calcu-lations were performed.

Data AnalysisFor the analysis of the results the Andad (CER-

ENA 2002) and SPSS (IBM, 2015) programs wereused. Initially, for each set of buffers, scatter plotsfor brightness and soil properties and correlationmatrices between those, including greenness andwetness were performed in Andad. By doing thisit is possible to comprehend the relationships andtrends between these variables, as well as to under-stand which buffers size shows the best correlationsbetween the variables.

Afterwards, using the same software, a principalcomponents analysis was performed. It is a mul-tidimensional data analysis technique that trans-forms linearly the original amount variables into asmaller set of uncorrelated variables called princi-pal components, which represents most of the infor-mation in the original set (Dunteman, 1989). Thegraphics that are presented in the results chapterrepresent the correlation circle obtained when per-forming this statistical analysis. Well representedvariables are those that are closer to the circumfer-ence, while variables closer to center are poorly rep-resented. Also, well represented variables closer toeach other means that they are strongly correlated,and variables in opposite points present a strongnegative correlation (Sousa, 2007). To conclude theresult analysis, using SPSS program, multiple lin-ear regression models were formulated. They helpunderstanding which soil property has the biggestinfluence in the values of the tasseled cap features,more specifically brightness and, on the other hand,if brightness can explain the variation of some soilproperties. That way, firstly, brightness was consid-ered to be the depend variable, as the soil chemicalproperties are the independent variables. Posteri-orly, the contrary was performed, chemical proper-ties that present good correlation with brightnesswere used as dependent variables to understand ifthese properties can be explained at the expenses ofbrightness, greenness or wetness. The method usedto perform the regression was the Stepwise method.

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4. Results

4.1. Tasseled Cap TransformationThe tasseled cap bands are represented in figure 3.

Figure 3: Tasseled Cap Bands: Brightness (top left),Greenness (top right) and Wetness (bottom)

Regarding brightness, it can be observed that inthe center of the study area, brightness is higher,once it has a very vivid red. The extremes Northand South are very dark, corresponding to analmost non-existent brightness. Considering thegreenness map, areas with dark green have low vege-tation cover, moreover where the scene is very dark,almost black, the vegetation is non-existent, theland is manly covered by water bodies. On the otherhand, zones where a lighter green is observed have agreater vegetation cover, like the ones noticed in theWest and South part of the figure. Likewise, verybright green points that are observed correspond toagriculture fields. Finally, the bottom map repre-sents wetness, the third tasseled cap band. Thisband contains information related with soil mois-ture. At first glance, comparing with the bright-ness map the inverse is observed, i.e, areas withdark blue and consequently lower soil moisture arebrighter. Comparing the three bands simultane-ously there is a tendency for high brightness andlow wetness being associated with low greenness,and vice versa.

4.2. NDVI and Land Use

The study area was restricted to zones with NDVIbetween 0 and 0.1 and natural areas.

The new study area is the result of the inter-section of the tasseled cap bands with these areas(figure 4).

Figure 4: Tasseled cap raster considering 0.1 <NDVI < 0 and natural areas

To simplify the visualization of the figure, onlybrightness and wetness are represented. Greenness,on the other hand, is not presented given the factthat once NDVI was restricted to a relatively smallrange, greenness ended up by having very low spa-tial variation.

4.3. Data AnalysisFor the pixels inside each buffer maximum, mini-mum, average and standard deviation of brightness,greenness and wetness were calculated. However,once it showed the best results, only the averagewas used to do the comparison with the soil prop-erties concentration values.

For the buffers of 500 meters there is a total of57 samples that were collected in natural areas andhave pixels of tasseled cap bands inside each buffer,while that for 1000 and 1500 meters buffers thenumber of samples is 60. In table 4 it is representedthe Pearson correlation value between the tasseledcap features and the soil properties for the three setof buffers. The correlations values vary between -1and 1, these values correspond to the best negativeand positive correlation possible, respectively.

Sand is the physical property with the highestcorrelation with brightness, followed by silt. Astrong correlation of brightness with the chemicalproperties organic carbon and nitrogen also exists.Moreover, as wetness is well correlated with bright-ness, these three properties are also the ones with

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the highest correlation value with average wetness.Wetness is negatively correlated with brightness,therefore properties that are positively correlatedwith brightness are negatively correlated with wet-ness, and vice versa. Greenness values, on the otherhand, have very low correlation with all topsoilproperties. A slight increase in the correlation valuefrom the 500 meters buffers to the higher buffers,except for coarse. When comparing the two high-est buffers (1000 and 1500 m), it is inferred thatthe correlation between average brightness and top-soil properties is very similar, slightly increasing forsome properties, such as nitrogen and decreasing forothers, like organic carbon.

However, larger buffers can also jeopardize theresults, once that in the areas where the furtheraway pixels are, they may not be related with thetopsoil properties measured at a particular samples.For these reason buffers larger than 1500 metersweren’t considered.

The relations are better perceived by performingthe principal components analysis (figures 5 to 7).

The correlation circles for the three set of buffersare very similar, showing the same behaviour be-tween the variables.

Variables pH’s, cation exchange capacity, potas-sium, clay, nitrogen, organic carbon, silt, sand andaverage brightness are well represent, because theyare near the circumference.

Three groups of variables stand out, which are or-ganic carbon, silt and nitrogen; pH1, pH2, CEC andCaCO3 and average brightness and sand. The vari-ables of each group are close to each other, whichmeans that they are strongly positively correlated.

Table 4: Pearson Correlation values for the threesets of buffers

Soil PropertiesBuffer

500 1000 1500Brightness Greenness Wetness Brightness Greenness Wetness Brightness Greenness Wetness

Brightness 1.000 0.4070** -0.6512** 1.0000 0.2875* -0.7856** 1.0000 0.2208 -0.7785**Greenness 0.4070** 1.000 -0.3803 0.2875* 1.0000 -0.3244* 0.2208 1.0000 -0.3293*Coarse (%) -0.3559** -0.0389 0.2546 -0.2205 -0.1200 0.2572 -0.3366** -0.0150 0.2981*Clay (%) -0.4712** 0.1332 0.3429** -0.4764** 0.1697 0.4420** -0.5002** 0.2208 0.4952**Silt (%) -0.4983** 0.0912 0.1941 -0.5865** 0.0341 0.3969** -0.5878** 0.1049 0.4089**

Sand (%) 0.5407** -0.1126 -0.2837* 0.6122** -0.0936 -0.4676** 0.6167** -0.1621 -0.4924**pH in H2O 0.0143 0.3568** 0.0341 -0.0535 0.3815** 0.0052 -0.0374 0.3950** 0.0210

pH in CaCl2 -0.0379 0.3095 0.0463 -0.1262 0.3426** 0.0762 -0.1012 0.3750** 0.0830O.C. (g/kg) -0.4973** 0.0018 0.2525 -0.5629** -0.0888 0.5058** -0.5565** -0.0177 0.4849**

CaCO3 (g/kg) -0.0117 0.3359* 0.1068 -0.0640 0.2959* 0.1581 -0.0484 0.3546** 0.1542N (g/kg) -0.5318** -0.0052 0.2471 -0.5890** -0.0363 0.4304** -0.6250** -0.0442 0.4843**

P (mg/kg) 0.0962 0.0628 -0.0980 0.0623 0.0602 -0.1305 0.0533 0.0882 -0.1172K (g/kg) -0.3474** 0.2509 0.2280 -0.3636** 0.2321 0.3059* -0.4220** 0.2426 0.3545**

CEC (cmol(+)/kg) -0.1862 0.2101 0.2287 -0.2188 0.1609 0.2464 -0.2394 0.2150 0.2543

** Correlation is significant at the 0.01 level*Correlation is significant at the 0.05 level

Figure 5: Principal Components Analysis (axes F1and F2) for 500 meters buffers

Figure 6: Principal Components Analysis (axes F1and F2) for 1000 meters buffers

Figure 7: Principal Components Analysis (axes F1and F2) for 1500 meters buffers

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On the other hand, variables that are in oppositelocations have a negative correlation, therefore av-erage brightness is negatively correlated with wet-ness and with soil properties nitrogen, organic car-bon and silt.

Phosphorus is near the center of the three corre-lation circles circumference, which means that thisvariable isn’t well projected in the chosen axes (F1and F2). The factors on which the variable is wellrepresented are F1 and F4 for 500 m buffers (fig-ure 8), and F1 and F3 for 1000 and 1500 m buffers(figures 9 and 10).

Figure 8: Principal Components Analysis (axes F1and F4) for 500 meters buffers

Figure 9: Principal Components Analysis (axes F1and F3) for 500 meters buffers

Figure 10: Principal Components Analysis (axesF1 and F3) for 1500 m buffers

In these graphics phosphorus is graphically rep-resented near the circle, however a positive or nega-tive correlation with other variables is not noted asthere aren’t any near phosphorus or in a symmetri-cal position with it.

The final step of the statistical analysis is to per-form a multiple linear regression analysis. Firstly, a

predictive model for brightness was produced. Af-ter, the same method was used to predict the con-centrations of soil properties as a function of thetasseled cap features. This allows to perceive if theresultant models are significant. For this, the chem-ical properties that have a direct influence in the soilquality, such as nitrogen, organic carbon, cation ex-change capacity, phosphorus and potassium wereused as dependent variables in the generation ofthe models. For the three set of buffers considered,only nitrogen and organic carbon produced signifi-cant models, once that for the remaining propertiesno independent variable was selected, which meansthat neither of the tasseled cap features explainsthe behaviour of those properties. Therefore, onlythe models for nitrogen and organic carbon are pre-sented.

When considering brightness as the dependentvariable nitrogen is the only chemical property se-lected by the model for 500 and 1000 m buffers.While that for 1500 m buffer two models were gen-erated, the first considering only nitrogen, and thesecond adding phosphorus. For nitrogen and or-ganic carbon, brightness is the only selected vari-able for all sets of buffers, except for the 500 metersbuffers where greenness appears the have influencein the variation of nitrogen.

Therefore, the models obtained when performinga multiple linear regression are the following:

500 meter buffersR R2

(1) Brightness = 239.538 − 26.072N 0.532 0.283(2) Nitrogen = 3.622 − 0.011(Br) 0.532 0.283(3) Nitrogen = 3.713 − 0.013(Br) +0.062(Gr)

0.580 0.336

(4) OrganicCarbon = 44.129− 0.134(Br) 0.497 0.247

1000 meter buffersR R2

(5) Brightness = 243.941 − 28.359N 0.589 0.347(6) Nitrogen = 3.900 − 0.012(Br) 0.589 0.347(7) OrganicCarbon = 46.449− 0.147(Br) 0.563 0.317

1500 meter buffersR R2

(8) Brightness = 241.737 − 26.096N 0.625 0.391(9) Brightness = 242.326 − 28.279N +0.357P

0.659 0.435

(10) Nitrogen = 4.499 − 0.015(Br) 0.625 0.391(11) OrganicCarbon = 53.616 −0.178(Br)

0.557 0.310

Legend Br - Brightness (average)Gr - Greenness (average)

The units of the soil variables are g/kg for nitro-gen and organic carbon and mg/kg for phosphorus.

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R represents the multiple correlation coefficient,it measures the quality of prediction of the depen-dent variable. R2 is the proportion of variance inthe dependent variable that can be explained by theindependent variable. Both values increase with theenlargement of the buffers, being (9) the model thatbetter fits the data, explaining 43.5 % of brightnessvariability. Nevertheless, nitrogen is the chemicalsoil property that better relates with brightness,this means that the values of brightness reflect bet-ter the spatial distribution of nitrogen concentra-tion instead of any other chemical property.

5. Conclusions

The importance of analysis of soil properties usingremote sensing has been increasing due to grow-ing need of better management of the services thatecosystems deliver. The steady population growthhas led to intensive land exploitation and when as-sociated with adverse climate causes soil to degrade.In this study it was intended to analyse the topsoilproperties of the semi arid region of Portugal, Alen-tejo and Algarve, using remote sensing techniques.Through the application of the tasseled cap trans-formation it was possible to analyse the spatial vari-ation of brightness, greenness and wetness over thestudy area.

Using the samples collected in natural areas un-der the 2009 LUCAS survey it was possible tocomprehend the correlation between brightness andphysical and chemical topsoil properties.

The application of a filter to the tasseled capbands in order to calculate the average value ofthe pixels proved that using brightness, greennessor wetness to assess soil properties without condi-tioning to land use and vegetation density leads toinconclusive results.

Considering only areas with NDVI between 0 and0.1, and natural land use, buffers of 500, 1000 and1500 meters were generated around each sample sothat concentrations would be compared with a setof pixels instead of one. The results of the statisticalanalysis for buffers of 1000 and 1500 meters showedbetter results when compared to the other buffersinstead, i.e. larger correlations between variables.Smaller buffers are not representative of the sampleand larger buffers contain unrelated pixels that areno longer correlated with the samples. In this case,it can’t be concluded that buffers of 1500 resultedin better results than buffers of 1000 meters andvice-versa. Although the models performed fromthe multiple linear regression seemed to be moresignificant for the 1500 meters buffers, the princi-pal components analysis proved that while some soilproperties correlate better with brightness for oneset of buffers, others show better correlation for theother.

From the principal components analysis it wasinferred that some soil properties are strongly cor-related with each other. It is the case of organiccarbon, silt and nitrogen. In soils, nitrogen occursin both organic and inorganic forms. A portion ofnitrogen exists in the soil through processes of at-mospheric, industrial and biological fixation (Neves,2009). In this case, as the study is performed in anatural area, it is assumed that there isn’t applica-tion of industrial products, such as fertilizers. Thelargest part of organic nitrogen exists in the soilin the form of organic matter, through decomposi-tion of animals and plants remains (Neves, 2009).Organic matter is composed by both organic car-bon and nitrogen, in a ratio of approximately 12:1(Priblyl, 2010; Neves, 2010). This means that theexistence of organic carbon, implies the existence ofnitrogen, even if in smaller amount. This explainsthe strong correlation that is observed between bothproperties.

Both pH in water and in CaCl2 correlate withCaCO3, as well potassium with clay. However,given the fact that both clay and CaCO3 have, foralmost all samples, concentrations near zero, the ex-istent correlation may not be real, as the there aresome samples with very high concentrations valuethat influence the results.

Considering brightness, the first tasseled capband, the one that contains most of the informa-tion regarding soil, it shown a good correlation withchemical soil properties organic carbon (-0.5565)and nitrogen (-0.6250). Demonstrating that, whenorganic carbon and nitrogen in soil increase bright-ness decreases. Which means that darker areas (lowbrightness) have higher concentrations of organicmatter and vice versa, as Rossel et al. (2006) con-cluded.

Silt, unlike organic carbon and nitrogen, is aphysical property of the soils. It is part of its texturealong with sand and clay. Due to the fact that themajority of the samples has low percentage of clay,this property isn’t well represented, as it was con-cluded in the principal components analysis. There-fore the soil in the study area is mainly composed bysilt and sand. Given the scarcity of clayey soils, siltshows a good positive correlation with organic car-bon and nitrogen. On the other hand, being sandthe most chemical inactive soil, it explains why sandhas a strong positive correlation with brightness,denoting that sandy soils have lower concentrationsof chemical compounds and therefore have less po-tential. This result proves that the soil type has aninfluence in the quality and potentiality of the landit self.

When performing the multiple linear regression,it was concluded that from the soil chemical prop-erties, nitrogen is the one that best explains the

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variation of brightness for the three buffers sets.For the 500 and 1000 meters buffers, any of the re-maining soil variables add information to the mod-els, whereas for the 1500 meters buffers, phosphorusalso influences brightness variability. This influenceis very low compared to nitrogen, once that the co-efficient for nitrogen is 28.279 and for phosphorusis 0.357. However, when phosphorus is added tothe model the fitting parameters are the highest(R=0.659 and R2=0.435).

Nitrogen and organic carbon, both performed sig-nificant models when considered as dependent vari-ables. It was observed that the two variables arewell explained by brightness, although greennessalso adds information to the nitrogen model for the500 meters buffers. Between those two variable, ni-trogen generated models that fit better the datathan organic carbon, which is in agreement withwhat is observed when brightness is modelled atthe expenses of the chemical properties of the soil.The fact that nitrogen and organic carbon have thehighest correlation values with brightness explainswhy only for these two properties independent vari-ables were selected.

Organic matter suffers mineralization processes,that consists on its transformation in simple com-pounds. These compounds are Ca+2 , K+, NH+

4 ,PO2−

4 ,NO−3 , CO2, H2O (Neves, 2010). Plants as-

similate nitrogen through the form of ammonium(NH+

4 ) and nitrate (NO−3 ) (Xu et al., 2012). Being

brightness a good indicator of nitrogen in soil, itis, consequently an index of organic matter, due tothe relation between organic carbon and nitrogen.Therefore, soil quality can be perceived when usingbrightness as an index.

Nevertheless, by generating a linear regressionmodel, although the quality of model and the coef-ficient of determination (R2) improve when increas-ing the buffer sizes, the obtained equations are notfully representative of the results, as the highest R2

has the value of 0.435, meaning that the model onlyexplains 43.5% of brightness variability. Therefore,using brightness as a quantitative indicator will gen-erate results with high level of uncertainty.

Moreover, the obtained results and consequentconclusions in this study apply only for bare soils innatural areas. Dense or even scarce vegetation willprevent radiation to reach the soil, affecting bright-ness. The same way, land use of agriculture and ur-ban type also constitute a limitation to the correctstudy of soil properties through brightness. The useof fertilizers and pesticides in agriculture, as well asthe presence of cattle directly influence the chemicalcomposition of soil, specially the amount of organicmatter. Furthermore soils in urban areas are mostlycovered by man made infrastructures and, at thesame time, the appearance of shadows is intensified

by the existence of tall buildings. For these rea-sons, a set of factors are necessary to be taken intoaccount when studying soil properties with remotesensing techniques, or even using other techniquesto complement the results, such as proximal sensingor soil sampling. Remote sensing is also highly de-pendent on available datasets, weather and sensorcalibration.

Soil brightness appears as a qualitative and/orquantitative indicator that has the potential to helpdecision makers understand which areas have thehighest potential to be explored and which need tobe protected and more sustainably managed.

For future researches it is proposed the applica-tion of the tasseled cap transformation to differentregions with the same conditions of aridity that mayhave different land uses and soil types, and, at thesame time, have enough soil data to be used forresult validation. This way the outcome could becompared with what was obtained and concludedin this study, and maybe generalized.

ReferencesAgencia Portuguesa do Ambiente (1982). Atlas do Ambiente- Carta de Solos. Version 1.2.

Apan A., Kelly R., Jensen T., Butler D., Strong W., &Basnet B. (2002). Spectral discrimination and separabil-ity analysis of agricultural crops and soil attributes usingASTER imagery. 11th Australasian Remote Sensing andPhotogrammetry Conference (ARSPC). Brisbane/Australia.

Bot, A. & Benites, J. (2005). The importance of soil or-ganic matter - Key to drought-resistant soil and sustainedfood production. Food and Agriculture Organization of theUnited Nations, Rome.

Campbell, J. B. & Wynne, R. H. (2011). Introduction toRemote Sensing. The Guilford Press (5th ed., Vol. 26).

CERENA (2002). Andad Software. Instituto SuperiorTcnico, Universidade de Lisboa.

Chavez, P. S. (1988). An Improved Dark - Object Sub-traction Technique for Atmospheric Scattering Correctionof Multispectral Data. Remote Sensing of Environment,24:459-479.

Chander, G., Markham, B. L., & Barsi, J. A. (2007).Revised landsat-5 thematic mapper radiometric calibration.IEEE Geoscience and Remote Sensing Letters, 4(3), 490494.

Crist, E. P., & Cicone, R. C. (1984). A Physically-BasedTransformation of Thematic Mapper Data - The TM Tas-seled Cap. IEEE Transactions on Geoscience and RemoteSensing, GE-22(3), pp. 256263.

Direo Geral do Territrio (2007). Carta de Ocupao dosSolos.

DOdorico, P., Bhattachan, A., Davis, K. F., Ravi, S., &Runyan, C. W. (2013). Global desertification: Drivers andfeedbacks. Advances in Water Resources, 51, 326344.

Dunterman, G. H. (1989). Principal Components Analy-sis. Sage University Paper series on Quantitative Applicationin the Social Sciences, series no 69.

ESRI (2011). ArcGIS Desktop: 10.3. Redlands, CA: En-vironmental Systems Research Institute.

European Commission (2016). Environment - Soil. Re-trieved from: ec.europa.eu/environment/soil/indexen.htm(Accessed in April 2016).

FAO (2001). Lecture Notes on the Major Soilsof the World (Chapter 9). Retrieved from: http ://www.fao.org/docrep/003/Y 1899E/y1899e12.htm#P00.FAO, Rome (Accessed in May 2016).

9

Harris Geospatial (2015). ENVI version 5.3. Exelis VisualInformation Solutions. Boulder, Colorado.

Haralick R. M., Sternberg S. R., and Zhuang X. (1987).Image Analysis Using Mathematical Morphology. IEEETransactions on Pattern Analysis and Machine Intelligence.Vol. 9, no. 4, 532-550.

IBM (2015). SPSS Statistics Software, version 23. IBMCorporation, New York, USA.

IM & INAG (2003). Cartografia do Indice de Aridez, pre-sented in the DISMED workshop Avaliao de Resultados parandices de Desertificao. Estao Agronmica Nacional, Oeiras.

INE & PORDATA (2016). Valor acrescentadobruto das empresas no financeiras: total e por sec-tor de actividade econmica - Municpios. Retrievedfrom: http : //www.pordata.pt/Municipios/V alor +acrescentado+bruto+das+empresas+nao+financeiras+total+ e+ por + sector + de+ actividade+ economica− 588(Accessed in August 2007).

IPMA (2009). O Clima - Acompanhamentodo clima. Lisboa. Retrieved from: https ://www.ipma.pt/pt/oclima/monitorizacao/index.jsp?selT ipo =m&selV ar = tx&selAna = me&selAno = 2009 (Accessedin April 2016).

Jenny, H. (1994). Factors of soil formation. A system ofquantitative pedology. Geoderma (Vol. 68).

Kauth, R. J. & Thomas, G. S. (1976). ”The tasseled cap -A graphic description of the spectral-temporal developmentof agricultural crops as seen by Landsat”. Symposium onMachine Processing of Remotely Sensed Data. Purdue Uni-versity, West Lafayette, Indiana. 41-50.

Lamb, J. A., Fernandez, F. G., & Kaiser, D. E. (2014).Nutrient Management: Understanding Nitrogen in Soils: Ex-tension Specialists in Nutrient Management. University ofMinnesota.

Neves, M. O. (2007). Constituintes do Solo III [PowerPoint Slides]. Pedology lectures, academic year 2013/2014.Instituto Superior Tcnico, Universidade de Lisboa.

Neves, M. O. (2009). Ciclos N, P e K [Power Point Slides].Pedology lectures, academic year 2013/2014. Instituto Su-perior Tcnico, Universidade de Lisboa.

Neves, M. O. (2010). Constituintes do Solo III [PowerPoint Slides]. Pedology lectures, academic year 2013/2014.Instituto Superior Tcnico, Universidade de Lisboa.

Paulik, C., W. Dorigo, W. Wagner, and R. Kidd (2014),.Validation of the ASCAT Soil Water Index using in situdata from the International Soil Moisture Network. Inter-national journal of applied earth observation and geoinfor-mation, 30(0), 1-8.

Pribyl, D. W. (2010). A critical review of the conventionalSOC to SOM conversion factor. Geoderma, 156(3-4), 7583.

Rossel, R.A., Minasny, B., Roudier, P., McBratney, A.B.,(2006). Colour space models for soil science. Geoderma 133(34), 320337.

Selige, T., Bhner, J., & Schmidhalter, U. (2006). Highresolution topsoil mapping using hyperspectral image andfield data in multivariate regression modelling procedures.Geoderma, 136(1), 235244.

SNIRH (2009). Boletim de Precipitao. Retrieved from:http : //snirh.pt/index.php?idMain = 1&idItem = 1.1(Accessed in April 2016).

Sousa, J. (2007). Anlise em Componentes Principais. In-stituto Superior Tcnico, Universidade de Lisboa.

Tth, G., Jones, A., & Montanarella, L. (2013). LUCASTopsoil survey. Joint Research Center Technical Reports.European Commission.

United Nations (1994). United Nations Conven-tion to Combat Desertification, Part I. Retrievedfrom: http : //www.unccd.int/en/about − the −convention/Pages/Text − Part − I.aspx (Accessed inJune 2016).

USGS (2009). Earth Explorer. Retrieved from: http ://earthexplorer.usgs.gov (Accessed in March 2016).

USGS (2014). Landsat 5 History. Retrieved from:landsat.usgs.gov/aboutlandsat5.php (Accessed in April2016).

USGS (2015). NDVI, the Foundation for Re-mote Sensing Phenology. Retrieved from: http ://phenology.cr.usgs.gov/ndvifoundation.php (Accessed inApril 2016).

Wagner, W., Pathe, C., Sabel, D., Bartsch, A., Kunzer, C.& Scipal, K. (2007). Experimental 1 km soil moisture prod-ucts from ENVISTAT ASAR for Southern Africal, SHAREproject and the MISAR project. European Space Agency.

Xu, Guohua, Xiaorong Fan, & Anthony J. Miller. ”Plantnitrogen assimilation and use efficiency.” Annual review ofplant biology 63 (2012): 153-182.

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