Physics and Chemistry of the Earth 34 (2009) 749–754

Contents lists available at ScienceDirect

Physics and Chemistry of the Earth

journal homepage: www.elsevier .com/locate /pce

Estimating the surface temperature of Lake Malawi using AVHRR and MODISsatellite imagery

Geoffrey Chavula a,*, Patrick Brezonik b, Prasad Thenkabail c, Thomas Johnson d, Marvin Bauer e

a University of Malawi – The Polytechnic, P/B 303, Blantyre 3, Malawib University of Minnesota, Civil Engineering, 500 Pillsbury Dr. SE, Minneapolis, MN 55455, USAc International Water Management Institute (IWMI), P.O. Box 2075, Colombo, Sri Lankad University of Minnesota (Duluth) – Large Lakes Observatory (LLO), 10 University Dr. Duluth, MN 55812, USAe University of Minnesota – Forestry Resources, 1530 N Cleveland Ave., St. Paul, MN 55108, USA

a r t i c l e i n f o a b s t r a c t

Article history:Received 24 January 2009Received in revised form 31 July 2009Accepted 5 August 2009Available online 11 August 2009

Keywords:AVHRRLake MalawiLake surface temperatureMODISReflectance

1474-7065/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.pce.2009.08.001

* Corresponding author. Tel.: +265 5946056; fax: +E-mail addresses: gchavula@poly.ac.mw (G. Chav

Brezonik), p.thenkabail@cgiar.org (P. Thenkabail),mbauer@umn.edu (M. Bauer).

This paper discusses the potential offered by both Advanced Very High Resolution Radiometer (AVHRR)and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery in estimating lake sur-face temperature. The findings from regression analysis show that MODIS/Terra Land Surface Tempera-ture data (MOD11A1) and Ocean Color MODIS/Terra Sea Surface Temperature (SST)-SeaDAS data areclosely related to in situ data as evidenced by a relatively high correlation coefficient (r2 values of 0.7and 0.74, respectively). In the light of the above, the developed algorithm may be used for estimating lakesurface temperature over Lake Malawi from MODIS satellite imagery.

Maps of temperature distribution that were generated for the lake from MODIS satellite imagery showthat the circulation pattern of Lake Malawi is very complex, with no clear set pattern of temperature dis-tribution over the entire lake. However, the persistence of a cold water zone between Salima and thenorthern part of Nkhotakota may be indicative of an upwelling region, ideal for the development of fish-ery industry.

Inadequate satellite data made it difficult to assess whether or not the triple window equation previ-ously developed by Wooster et al. [Wooster, M., Patterson, G., Loftie, R., Sear, C., 2001. Derivation and val-idation of the seasonal thermal structure of Lake Malawi using multi-satellite AVHRR observations.International Journal of Remote Sensing 22(15), 2953–2972] for estimating lake surface temperatureusing AVHRR was applicable to the entire lake.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

The seasonal fluctuation of primary productivity in Lake Malawi(Fig. 1) is controlled principally by the thermal structure of thelake, which modulates the mixing of deep water rich in nutrientswith that of the phytoplankton rich near-surface layer (Woosteret al., 2001). The thermal structure in turn is highly dependenton the distribution of solar radiation within the water body. Assuch, the ability to determine temperature distribution over thelake surface offers an opportunity to obtain vital information aboutthe nature and extent of the existing thermal structure, and alsoaids in locating upwelling zones in the lake where primary produc-tivity might be taking place. Such areas are considered ideal for thedevelopment of the fishery industry; and in a country like Malawiwhere 70% of the animal protein uptake by its people is derived

ll rights reserved.

265 1870578.ula), brezonik@umn.edu (P.

tcj@d.umn.edu (T. Johnson),

from fish (Bland and Donda, 1995), the necessity of mapping zonesof high primary productivity over the lake cannot be overempha-sized. However, conducting in situ measurements of temperaturedistribution over the entire length and breadth of Lake Malawi isa daunting task hence recourse is made to satellite remote sensingtechnology, which provides the desired spatial and temporal reso-lution to monitor temperature distribution over the lake. In addi-tion to providing maps of surface temperature distribution overthe lake, remote sensing also may yield information about thelake’s circulation patterns. Previous studies by Wooster et al.(2001) demonstrated the potential of using Advanced Very HighResolution Radiometer (AVHRR) satellite data to estimate lake sur-face temperature (LST).

Patterson and Kachinjika (1995) described a ‘‘typical tempera-ture profile” of Lake Malawi as comprising a surface layer (the epi-limnion) at about 27.2 �C, extending to a depth of 40 m. Below theepilimnion is a thermocline, which is characterized by a steeptemperature decline extending to a depth of 85 m; and below thislayer and extending to the bottom is the hypolimnion, with watertemperatures of 22.5–23.5 oC.

Fig. 1. Lake Malawi/Nyasa Basin (source: Bootsma and Jorgensen, 2006).

750 G. Chavula et al. / Physics and Chemistry of the Earth 34 (2009) 749–754

2. Objective of the study

The main objectives of the study were to develop algorithms/equations that could be used to estimate lake surface temperaturefrom MODIS satellite imagery, and to determine the circulation pat-tern of Lake Malawi from temperature distribution maps. Suchmaps could be useful in delineating areas over the lake whereupwelling may be taking place, areas that may be targeted for thedevelopment of the fishery industry by the Malawi Government.

3. Datasets

To conduct the study, the following datasets were used: ob-served (in situ) data for the period 1997–1998 collected over Lake

Malawi by Bootsma and others, and AVHRR data for the period1997–1998 from NOAA; and Land Surface Temperature MODIS(MOD11A1) data for the period May to November, 2006 fromEOS Gateway website, Sea Surface Temperature data from theMODIS Ocean Color website for the period May to November,2006 and observed or in situ measurements of temperature takenaround Monkey Bay area over the period May to November, 2006(Table 1).

4. Methods

Firstly, we carried out in situ measurements of lake surface tem-perature at three selected sampling stations over Lake Malawi(Fig. 2) over the period May to November, 2006 concurrent withsatellite image acquisitions. Secondly, we downloaded and pro-cessed corresponding AVHRR and MODIS/Terra satellite imagery.AVHRR data covered the period September 1997 to February1998, corresponding with field sampling done by Bootsma and oth-ers; whereas MODIS/Terra data covered the period May to Novem-ber, 2006 corresponding with our field sampling. For MODIS data,both MODIS Land Surface Temperature (MOD11A1) and Ocean Col-or Sea Surface Temperature (SST) were downloaded and processedinto lake surface temperature. This was done using ERDAS ImagineGIS software.

Observed temperature data were collected from the south east-ern arm of Lake Malawi at three sampling stations around MonkeyBay area (Fig. 2), on clear days between 11:00 and 12:30 pm (±2 h),when the MODIS/Terra satellite is overhead Malawi after crossingthe equator at 10:30 am. The area was chosen because of its prox-imity to Monkey Bay Fisheries Research Station, where we werebased during the field research. The south eastern arm of Lake Ma-lawi is exposed to prevailing south easterly trade winds (locallyknown as Mwera), which may influence upwelling in this part ofthe lake and promote primary production.

Two glass thermometers were used to measure temperature di-rectly from the lake surface at a depth of 0–7.0 cm, and the averageof the two readings was reported as the lake surface temperatureat a particular sampling station.

Harvey Bootsma supplied us with in situ temperature data thatwe compared with concurrent AVHRR satellite imagery for the cor-responding period 1997–1998 for sampling station at 13o300 0S and34o44.070 0E over Lake Malawi. Satellite data was downloaded fromNOAA’s website, and then ‘sub-setted’ for the Lake Malawi basin.Digital number (DN) values for bands 3, 4, and 5 were convertedto radiance using the equation (NOAA, 1998):

Ei ¼ SiC þ Ii ð1Þ

where Ei is the radiance value in mW/(m2 sr cm�1), C is the inputdata value (ranging from 0 to 1023 counts) for the sampling pointfrom the image, and Si and Ii are the corresponding scaled slopeand intercept values read from the imagery, respectively. Note thatthe scaled slope and intercept are calibration variables for theAVHRR sensor critical in the computation of irradiance emergingfrom the target object. Slope and intercept values were read fromthe satellite image, while the number of counts corresponding witheach sampling was obtained from the image using ERDAS Imagineby simply reading off the value corresponding with the point ofinterest. The conversion from radiance to brightness temperaturewas performed using the inverse of Planck’s radiation equation(NOAA, 1998):

TðEÞ ¼ C2m

ln 1þ C1m3

E

� � ð2Þ

where T(E) is the temperature (K) for the radiance value E (deter-mined from Eq. (1)), m is the central wave number of the channel

Table 1Datasets for lake surface temperature estimation.

Satellite sensor and band no. Wavelength (lm) Duration (months) Data final format Range

AVHHRBand 4 (B4) 10.3–11.3 September, 1997 Brightness temperature (top of atmosphere) 160–340Band 5 (B5) 11.5–12.5 September, 1997 Brightness temperature (top of atmosphere) 160–340

MODIS/TerraMOD11A1, day and night temperature May to November, 2006Bands 31 10.78–11.28Band 32 11.77–12.27MODIS-SST May to November, 2006(Ocean Color)

‘GT–Data’LST measured over May to November, 2006Lake Malawi

Secondary dataTemp. from existingStationsWind speed dataFrom met. stations

Fig. 2. Location of field sampling stations for chlorophyll-a concentration. Stations are shown by three dots on the Landsat image with corresponding station number.

G. Chavula et al. / Physics and Chemistry of the Earth 34 (2009) 749–754 751

(cm�1), and C1 and C2 are constants (C1 = 1.1910659 � 10�5 mW/(m2 sr cm�4) and C2 = 1.438833 cm K).

Finally, the brightness temperatures for bands 3, 4, and 5 (rep-resented here as T3, T4, and T5, respectively) were converted tolake surface temperatures using Eqs. (3) and (4), and comparedwith in situ data.

LST ¼ 0:9115T3þ 0:9191T4� 0:8246T5� 273:21 ð3ÞTs ¼ T4þ 3:3ðT4� T5Þ ð4Þ

Wooster et al. (2001) developed the triple window algorithmfor Lake Malawi represented in Eq. (3), while Thenkabail (2003)used Eq. (4) for land surfaces in various studies he carried out inAsia.

752 G. Chavula et al. / Physics and Chemistry of the Earth 34 (2009) 749–754

For MODIS/Terra (MOD11A1) Land Surface Temperature data,the intrinsic split window Eq. (5) was applied to provide the calcu-lated values of LST from bands 31 and 32 using ERDAS Imagine.

Ts ¼ A1 þ A21� e

eþ A3

Dee2

� �T31 þ T32

2

þ B1 þ B21� e

eþ B3

Dee2

� �ðT31 � T32Þ þ C ð5Þ

where e = 0.5(e31 + e32) and De = e31�e32 (Wan, 1999). Note that A1,A2, A3, B1, B2, B3, and C are constants; T31 and T32 are correspondingtemperature values for bands 31 and 32, respectively; and e is anemissivity term.

y = 1.0709x - 1.6771R2 = 0.9095

20

21

22

23

24

25

26

27

28

29

20 21 22 23 24 25 26 27 28 29

Obseved lake surface temperature at Sampling Station No. 1, oCObs

erve

d te

mpe

ratu

re a

t Sam

plin

g St

atio

n N

o. 2

, o C

Observed Temp-2 Linear (Observed Temp-2)

Fig. 3. Relationship between observed temperature for sampling Station 1 andStation 2.

Fig. 5. Temperature distribution maps fo

The procedure entailed downloading MOD11A1 daily (day)temperature data at 1 km resolution from EOS Gateway website,determining the count corresponding with the sampling pointand then converting it to temperature in Kelvin by multiplyingthe count value by a scale factor of 0.02 as recommended by NASA.Thereafter, regression analysis between in situ and MOD11A1 datawas conducted, and the value of the correlation coefficient (r2) wasassessed.

Contouring of lake surface temperature using an interpolationprocedure was done in ER Mapper in order to determine the circu-lation pattern of the lake through the location of upwelling (warm)and downwelling (cold) zones over the lake surface.

y = 1.031x + 0.683R2 = 0.68

21

22

23

24

25

26

27

28

29

21 22 23 24 25 26 27 28 29 30

MODIS11A1-500m-resolution-temperature (oC)

Ob

serv

ed t

emp

erat

ure

(oC

)

Observed Linear (Observed)

Fig. 4. Relationship between MODIS Land Surface Temperature (MOD11A1) andobserved LST.

r Lake Malawi, MOD11A1 imagery.

y = 1.2194x - 5.9044R2 = 0.7468

22

23

24

25

26

27

28

29

22 23 24 25 26 27 28 29 30MODIS-SST (oC)

Obs

erve

d la

ke s

urfa

ce te

mpe

ratu

re (o C

)

Observed Linear (Observed)

Fig. 6. Relationship between MODIS Sea Surface Temperature (SST) and observedLST.

G. Chavula et al. / Physics and Chemistry of the Earth 34 (2009) 749–754 753

For MODIS SST, Level 3 temperature data recorded by MODIS/Terra satellite were downloaded from the Ocean Color websiteand processed into lake surface temperature using the Smigen rou-tine in the SeaDAS software. The satellite data had a re-sampledresolution of 2-km. Regression analysis between in situ and MODISSST data was conducted, and the value of r2 was assessed. Note thatOcean Color data is already processed into temperature values, andthese values were compared with in situ data in order to test theircompatibility with ground observations.

5. Results and discussion

5.1. Observed temperature measurements

Over the period May to November, 2006 lake surface tempera-ture varied from a minimum of 22 oC to a maximum value of 28 oC.

Fig. 7. Temperature distribution map for La

This agrees with findings from earlier studies which reported thatsurface temperatures of Lake Malawi fall in the range 22–28 oC(Patterson and Kachinjika, 1995).

A graph of in situ temperature at sampling stations 1 and 2 showa linear relationship with a slope close to 1.0 and an r2 value of 0.91(Fig. 3). This implies that the two sampling stations receive similaramounts of solar radiation.

5.2. AVHRR

Satellite derived temperature values obtained from AVHRRimagery using Eqs. (2)–(4) did not yield results that were compat-ible with in situ data. In short, there were big disparities betweentemperature estimates obtained from Eqs. (3) and (4) with fieldmeasurements done by Bootsma and others. The values obtainedwere either very high or very low and thus considered inaccuratefor further analysis. Sea Surface Temperature values for AVHRR’sthermal bands 3, 4, and 5 normally lie within the range 270–310 K (NOAA, 1998). The likely source of error may have beenthe computation of the intercept and slope values from the AVHRRimagery. Hence, Wooster et al. (2001) triple window equation wasnot assessed for its applicability to the entire lake; and neither wasequation used by Thenkabail assessed for its usefulness in deter-mining the surface temperature of Lake Malawi from AVHRR imag-ery. However, a qualitative assessment of the variation oftemperature distribution of AVHRR data using ERDAS Imagine col-or-plots with band four satellite imagery was carried out for fivesampling days, using data supplied to us by Bootsma and others.Temperature distribution maps indicated that the lake becomescool between May and October. This assertion may be supportedby the prevalence of the strong south easterly (Mwera) windswhich blow over Lake Malawi during this period, causing upwell-ing of cold water from the bottom of the lake. Temperature distri-bution maps also showed that the location of cold water zones overthe lake is not fixed but rather changes with season, with no clearpattern.

ke Malawi (�C), Ocean Color (SST) data.

754 G. Chavula et al. / Physics and Chemistry of the Earth 34 (2009) 749–754

5.3. MODIS

Temperature data obtained from Terra/MODIS satellite for sam-pling Station 1 using the Land Surface Temperature (i.e.,MOD11A1) imagery are plotted against observed temperature inFig. 4. The r2 value of 0.7 suggests a relatively strong linear rela-tionship between satellite data and in situ lake surface tempera-ture. Therefore, the equation for estimating lake surfacetemperature is:

LT ¼ 1:031 � Sþ 0:683 ð6Þ

where LT is the lake surface temperature, and S is the satellite de-rived (MOD11A1) temperature.

The range of satellite derived lake surface temperature wasfrom 22 to 25.9 oC.

Presented in Fig. 5 are temperature maps derived fromMOD11A1 imagery using a GIS software known as ER Mapper.In general, it may be said that the lake cools off from May toNovember, as noted by using AVHRR imagery. These images alsoshow that the location of upwelling zones is not static butchanges with seasons, with no clear pattern. However, in the caseof the MODIS data there is a persistent cold water zone extendingfrom Salima to the north of Nkhotakota on the western shore ofLake Malawi (Fig. 1). The upwelling zones are fully developed inNovember, with cold water centers located in the northern tipof the lake as well as at the middle section to the north ofNkhotakota.

A plot of temperature values derived from Terra/MODISOcean Color satellite data against observed temperature is pre-sented in Fig. 6. The r2 value of 0.74 is a slight improvementover the Land Surface Temperature value. Therefore, the equa-tion for estimating lake surface temperature using MODIS/AQUAimagery is:

LT ¼ 1:2194 � S� 5:9044 ð7Þ

Presented in Fig. 7 is the lake surface distribution map devel-oped from the regression equation by making use of routines avail-able in the SeaDAS software.

The lake surface temperature maps for all the three differenttypes of satellite imagery, have shown that although upwellingzones may be easily identified over the lake surface, the circulationpattern of Lake Malawi is complex, with no clear pattern of upwell-ing and downwelling zones. In such a situation, it may be difficultto make general statements about the circulation patterns.

6. Conclusions

Findings of the study show that the Ocean Color data, which en-tails the use of regression Eq. (7) of in situ and satellite data, is abetter estimator of lake surface temperature than MOD11A LandSurface Temperature imagery, Eq. (6). It should be noted that thelatter has been extensively used in studying temperatures of inlandwater bodies. Therefore, the algorithms developed for MODIS satel-lite imagery could be used for estimating surface temperature ofLake Malawi.

The upwelling zone extending from Salima to the northern ofNkhotakota may be an ideal area for further development of fisher-ies, particularly in the months of June to November when upwell-ing appears to be strong. In addition to the location of potentialfishing grounds, the study has shown that the location of upwellingzones over Lake Malawi is not fixed, but changes with seasons.

The lake surface temperature maps for all the three differenttypes of satellite imagery, have shown that although upwellingzones may be easily identified over the lake surface, the circulationpattern of Lake Malawi is complex. In such a situation, it may bedifficult to make general statements about the circulation patterns.

Acknowledgement

The authors would like to thank the International Water Man-agement Institute (IWMI), University of Minnesota, and STARTfor providing funding for the study. The technical support providedby James Kuyper of NASA, John Sapper of NOAA, Ye Myint of Leica,Ministry of Irrigation and Water Development (Malawi), and theMonkey Bay Fisheries Research Station is highly appreciated.

References

Bland, S.J.R., Donda, S.J., 1995. Common property and poverty: fisheries co-management in Malawi. Fisheries Bulletin 30, 1–16.

Bootsma, H.A., Jorgensen, S.E., 2006. Lake Malawi Nyasa: Experience and LessonsLearned Brief. Available from: <http://www.ilec.or.jp/lbmi2/reports/16_Lake_Malawi_Nyasa_27February2006.pdf>.

NOAA, 1998. NOAA Polar Orbiter Data (POD) User’s Guide, NOAA.Patterson, G., Kachinjika, O., 1995. Limnology and phytoplankton ecology. In: Menz,

A. (Ed.), The Fishery Potential and Productivity of the Pelagic Zone of the LakeMalawi/Niassa. Natural Resources Institute, UK.

Thenkabail, P.S., 2003. Global Monthly 0.1 degree (�10 km) AVHRR NDVI Datasetsfor 1981–2001. International Water Management Institute (IWMI), Colombo,Sri Lanka.

Wan, Z., 1999. MODIS Land Surface Temperature Algorithm Theoretical BasisDocument (LST ATBD) Version 3.3, Contract Number: NAS5-31370.

Wooster, M., Patterson, G., Loftie, R., Sear, C., 2001. Derivation and validation of theseasonal thermal structure of lake Malawi using multi-satellite AVHRRobservations. International Journal of Remote Sensing 22 (15), 2953–2972.