Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used for the blending ofLandsat and MODIS data. Specifically, the 30 m Landsat-7 ETM+ (Enhanced Thematic Mapper plus) sur-face reflectance was predicted for a period of 10 years (2000–2009) as the product of observed ETM+ andMODIS surface reflectance (MOD09A1) on the predicted and observed ETM+ dates. A pixel based analysisfor six observed ETM+ dates covering winter and summer crops showed that the prediction method wasmore accurate for NIR band (mean r2 = 0.71, p ≤ 0.01) compared to green band (mean r2 = 0.53; p ≤ 0.01).A recently proposed chlorophyll index (CI), which involves NIR and green spectral bands, was used toretrieve gross primary productivity (GPP) as the product of CI and photosynthetic active radiation (PAR).The regression analysis of GPP derived from closet observed and synthetic ETM+ showed a good agree-ment (r2 = 0.85, p ≤ 0.01 and r2 = 0.86, p ≤ 0.01) for wheat and sugarcane crops, respectively. The differencebetween the GPP derived from synthetic and observed ETM+ (prediction residual) was compared with thedifference in GPP values from observed ETM+ on the two dates (temporal residual). The prediction resid-uals (mean value of 1.97 g C/m2 in 8 days) was found to be significantly lower than the temporal residuals(mean value of 4.46 g C/m2 in 8 days) that correspondence to 12% and 27%, respectively, of GPP values
2
(mean value of 16.53 g C/m in 8 days) from observed ETM+ data, implying that the prediction methodwas better than temporal pixel substitution. Investigating the trend in synthetic ETM+ GPP values over agrowing season revealed that phenological patterns were well captured for wheat and sugarcane crops.A direct comparison between the GPP values derived from MODIS and synthetic ETM+ data showed agood consistency of the temporal dynamics but a systematic error that can be read as bias (MODIS GPPover estimation). Further, the regression analysis between observed evapotranspiration and synthetic
gree
ETM+ GPP showed good a
. Introduction
There is a growing interest in monitoring the gross primary pro-uctivity (GPP) of crops due mostly to their carbon sequestrationotential. Both within and between field variability are importantomponents of crop GPP monitoring, particularly for the estimationf carbon budgets. However, the 16-day revisit cycle of Landsat hasong limited its use especially over tropical areas because of prevail-ng cloudy conditions. In contrast, Moderate-resolution Imagingpectroradiometer (MODIS) has a high temporal resolution, cover-ng the earth up to multiple times per day. The MODIS instrument
ffers new possibilities for large area crop mapping by providingnear-daily global coverage of science-quality, intermediate reso-
ution (250 m) data since February 2000 at no cost to the end userJustice and Townshend, 2002). The moderate 250 m spatial reso-
lution of MODIS is appropriate for classifying cropping patterns inthe U.S. Central Great Plains given the region’s relatively large fieldsizes, which are frequently 32.4 ha or larger and would spatiallycorrespond to five or more 250 m pixels. Indian agriculture, in gen-eral, is characterized by the fragmentation of land and small landholdings. About 56% of the land holdings in India are less than 1 hasize and the size is going to diminish further, in the future. Of late,intensive cultivation by introduction of mixed cropping systems isa practice in several ecozones in India for maximizing the profits.As a result, agro ecosystems present a higher level of complexitydue to small field sizes, diversified cropping pattern and field-to-field variability in crop phenology and management practices. Thusobtaining reliable information on the various crops grown in mixedcropping is of paramount importance in determination of the total
area under crop; prediction of the yield per unit area and crop con-dition assessment. One solution is to combine the spatial resolutionof Landsat with the temporal frequency of coarse-resolution sen-sors, such as MODIS. The Terra platform crosses the equator at about10:30 A.M. local solar time, roughly 30 min later than Landsat-7
quality of the synthetic ETM+ surface reflectance has been carriedout, by comparing these predicted surface reflectance from real
TS
P
Data frequency Daily 16 days
TM+. Their orbital parameters are equal, and as such the viewingnear-nadir) and solar geometries are close to those of the cor-esponding Landsat acquisition. The Terra/Aqua MODIS providesrequent coarse-resolution observations, revisiting the globe onceo twice per day. The MODIS observations include 250-m spatialesolution for red (band 1) and near-infrared (band 2) wavebandsnd 500-m spatial resolution for other five MODIS land bands (band–7). The MODIS land bands have corresponding bandwidths to theandsat ETM+ sensor except their bandwidths are narrower thanTM+ (see Table 1).
Several approaches describing existing fusion techniques areummarized in Table 2. An early example of a fusion model wasllustrated by Carper et al. (1990) who combined 10 m spatial res-lution SPOT panchromatic imagery with 20 m spatial resolutionultispectral imagery by using an intensity–hue-saturation (IHS)
ransformation. The generated composite images have the spatialesolution of the panchromatic data and the spectral resolution ofhe original multispectral data. Other techniques to enhance thepatial resolution of multispectral bands include component sub-titution (Shettigara, 1992), and wavelet decomposition (Yocky,996). One of the first studies designed to increase the spatial res-lution of MODIS using Landsat was introduced by Acerbi-Juniort al. (2006) using wavelet transformations. The algorithm yieldslassified land covers types and was used for mapping the Brazilianavanna (Acerbi-Junior et al., 2006). Recently, Hansen et al. (2008)sed regression trees to fuse Landsat and MODIS data based on the00 m 16-day MODIS BRDF/Albedo land surface characterizationroduct (Roy et al., 2008; Hansen et al., 2008) to monitor forestover in the Congo Basin on a 16-day basis. While there are numer-us examples existing in the current literature that fuse data fromultiple sensors, only a few techniques yield calibrated outputs of
pectral radiance or reflectance (Gao et al., 2006), a requirement totudy vegetation dynamics or quantitative changes in reflectancever time. The Spatial and Temporal Adaptive Reflectance Fusionodel (STARFM) (Gao et al., 2006) is one such model and was
esigned to study vegetation dynamics at a 30 m spatial resolu-ion. STARFM predicts changes in reflectance at Landsat’s spatial
nd spectral resolution using high temporal frequency observationsrom MODIS. STARFM predicts reflectance at up to daily time steps,epending on the availability of MODIS data.
able 2ummary of data blending techniques found in the literature.
Technique Sensor 1 Scale 1 (m)
IHS transforms SPOT Pan 10Component SPOT XS 20Substitution Landsat TM 30Multi-resolution wavelet decomposition Landsat TM 28.5–120Wavelet transforms MODIS 1,2 250Downscaling MODIS MODIS 3–7 500
Combining medium and coarse-resolutionsatellite data
MODIS 1,2 250MODIS 250
Semi-physical data fusion approach usingMODIS BRDF/Albedo STARFM
MODIS 500MODIS 500
an = Panchromatic, Xs = multispectral.
rvation and Geoinformation 13 (2011) 59–69
Crops are the most pervasive anthropogenic biome worldwide,playing an important role in the global cycles of carbon, water, andnutrients. Most production efficiency models (PEM) are based onthe assumption of a close linear relationship between the fractionof photosynthetically active radiation (FAPAR) and the normalizeddifference vegetation index (NDVI), as well as on a constant, thoughbiome-specific, LUE (Ruimy et al., 1999). Nevertheless, it has beenshown that these assumptions do not hold in many circumstances.On the one hand, although LUE is a relatively conservative valueamong plant formations of the same metabolic type (Ruimy et al.,1999), its variability is species-specific rather than biome-specific(Ahl et al., 2004), it changes with phenological stage (Suyker et al.,2005; Gitelson et al., 2006), and it depends on environmental stressfactors (Running et al., 2004). Many models for the remote estima-tion of GPP use lookup tables of maximum light use efficiency (LUE)for a given vegetation type and then adjust those values downwardon the basis of environmental stress factors (Ruimy et al., 1999;Running et al., 2004), but they use coarse spatial resolution meteo-rological variables, which also result in significant GPP estimationerrors (Turner et al., 2005; Zhao et al., 2005). A recently devel-oped conceptual model with a solid physical background has beenwidely used for the nondestructive retrieval of various pigments indifferent media such as leaves and crop canopies (Gitelson et al.,2005, 2006). Chlorophyll index (CI) that employ bands in the near-infrared and either green or red-edge spectral regions are a specialcase of this conceptual model, and have been linearly related withcrop Chlorophyll content (Gitelson et al., 2005), as well as success-fully used for maize and soybean midday GPP estimation (Gitelsonet al., 2006). It was shown that the product of CI and incoming pho-tosynthetically active radiation (PAR) accounted for more than 98%of GPP variation in both maize and soybeans in a wide range ofPAR variation (Gitelson, 2004). This relationship (i.e., GPP versus CIx PAR) was nonspecies-specific, consistent and repeatable underrainfed and irrigated conditions (Gitelson, 2004).
The objective of this study was to investigate the suitability ofthe prediction algorithm (STARFM) for generating synthetic (pre-dicted) ETM+ surface reflectance for NIR and green bands andderivation of CI values using these synthetic surface reflectances.The CI technique is based entirely on remotely sensed data, theclose and consistent relationship between GPP and product of PARand crop chlorophyll content. Therefore, it constitutes an accu-rate surrogate measure for GPP estimation (Gitelson et al., 2008).The STARFM algorithm was used for agriculture land comprisingof winter and summer crops over a tropical area unlike previ-ous studies (Roy et al., 2008; Goa et al., 2006; Hilker et al., 2009)mainly for forest over extra tropical areas. The assessment of the
(observed) ETM+ images acquired through out two growing sea-sons over a period of 10 years (2000–2009). The synthetic ETM+
Sensor 2 Scale 2 (m) Author
Spot XS 20 Carper et al. (1990)SPOT Pan 10 Shettigara (1992)SPOT Pan 10SPOT Pan 10 Yocky (1996)Landsat TM 30 Acerbi-Junior et al. (2006)
MODIS 3–7 500 Trishchenko et al. (2006)Landsat TM 30 Busetto et al. (2008)
Landsat TM 30 Roy et al. (2008), Hansen et al. (2008)Landsat TM 30 Gao et al. (2006)
D. Singh / International Journal of Applied Earth Observation and Geoinformation 13 (2011) 59–69 61
F 185i to the
daatsv
2
2
PalfbrIhpdamS
Ft
ig. 1. Map of the study area. The study site encompasses a Landsat scene (185 km ×nterpretation of the references to color in this figure legend, the reader is referred
ata was used to study the GPP over the 10 years time period forssessing seasonal changes (i.e. changes due to vegetation green-upnd leaf senescence) in vegetation cover and vegetation status overhe study site for which the potential of acquiring frequent higherpatial resolution data (and therefore the potential for mapping ofegetation dynamics) is otherwise low.
. Methods
.1. Study area
The study area is Mawana subdivision of Meerut district of Uttarradesh state depicted by red polygon is shown in Fig. 1, coveringbout 1250 km2 area. The land cover is predominantly agricultureand with scattered trees and bushes. This area is around 75 kmrom national capital Delhi. The study area was chosen mainlyecause of the three important factors: (1) the identified locationepresents the agroclimatic conditions of large part of northernndia, where sugarcane occupies around 2 million ha and wheatas about 10 million ha area. This area comes under Indo-Gangetic
lains, where applications of organic matters in soil have goneown drastically resulting in decline of crop productivity. (2) Thevailability of continuous satellite data as the study site lies at theiddle of tiles where Landsat-7 ETM+ has no missing lines due to
LC-off. (3) The availability of continuous observed meteorological
ig. 2. (a) Shows RGB image as R (red), G (NIR), B (green) of 15 September 2009 for land co 2009. (For interpretation of the references to color in this figure legend, the reader is re
km) of 15 September 2009 as R (red),G (NIR),B (green) image near Delhi, India. (Forweb version of the article.)
parameters like temperature, rainfall, evaporation and wind over aperiod of 15 years (1995–2009) over the study site.
Fig. 2a shows the RGB with R (red), G (NIR), and B (green) bandsof Landsat-7 ETM+ image of 15th September 2009 to have an ideaabout the land cover type of study site. There are basically two typesof land cover found in the RGB (Fig. 2a) namely crop and non-cropland cover types. The pink color shows non-crop land, the Gangesriver in eastern part of region and villages in the image while thegreen colour shows the crop land cover. The maximum temperaturein the region is about 45 ◦C in summer while minimum is about5 ◦C in winter. The region receives approximately 95 cm of rainfallduring the year. Out of that about 90% rain occurs in the monsoonmonths i.e. July to middle of September (Fig. 2b). The average depthof water table is 6.0 meter in the study area. The soils characteristicsvary in the study area from sandy loam in western part of the areato highly clay loam in the eastern part of the area in khadar of theriver Ganges.
2.2. Satellite data
Two pairs of contemporary images, acquired by the sensorsTerra—MODIS and Landsat-7 ETM+, respectively, were used inthe present study. The 8 days MODIS composites (MOD09A1)of surface reflectances (green and NIR) with a spatial resolutionof 500 m for 10 years (2000–2009) time period were obtained
over types. (b) Shows the annual variation of rainfall over the study site from 2002ferred to the web version of the article.)
62 D. Singh / International Journal of Applied Earth Obse
Table 3Regression analysis of the observed versus predicted ETM+ images, whose predic-tion date was closest to the observed scenes.
Observed ETM+ scenes NIR Green
r2 a B r2 a b
3 February 2009 0.72 0.10 0.93 0.58 0.11 0.9424 April 2009 0.73 0.09 0.97 0.55 0.10 0.9226 May 2009 0.68 0.12 0.92 0.53 0.12 0.92
ftiGirjotMnast
tvafecr
3
(uaaT(mccbcfiwdfMstDsuai
sm
LUE for a particular vegetation type or in the assignment of vege-
17 October 2009 0.81 0.08 1.07 0.53 0.09 0.9118 November 2009 0.67 0.13 0.89 0.51 0.11 0.9220 December 2009 0.64 0.14 0.90 0.49 0.13 0.89
rom the EOS data gateway of NASA’s Goddard Space Flight Cen-er (http://redhook.gsfc.nasa.gov). Landsat-7 ETM+ orthorectifiedmages (path 146, row 040) were acquired through the USGSLOVIS portal (http://glovis.usgs.gov/) for the same period. Follow-
ng STARFM algorithm input requirements, the MODIS data wereeprojected to the geographical projection using the MODIS repro-ection tool (Kalvelage and Williams, 2005), clipped to the extentf the available Landsat imagery, and resampled to a 30 m spa-ial resolution with nearest-neighbor resampling to maintain the
ODIS pixel values and 30 m output pixel dimensions to reduceearest-neighbor resampling pixel shifts (i.e., position errors) (Roynd Dikshit, 1994). These datasets were forced to assume the sameize in order to make the results comparable. The basic features ofhe two sets of data are given in Table 1.
The radiometric and atmospherically corrections were appliedo Landsat-7 ETM+ scenes. The equations and parameters to con-ert calibrated Digital Numbers (DNs) to physical units, such ast-sensor radiance or TOA reflectance were utilized in this study arerom the previous studies (Chander and Markham, 2003; Chandert al., 2007; Markham et al., 2004). Images were atmosphericallyorrected using the QUAC module (Bernstein et al., 2005) of envi-onment for visualizing images (ENVI) processing package.
. Evaluation of synthetic ETM+ imagery
The Spatial and Temporal Adaptive Reflectance Fusion ModelSTARFM) developed by Gao et al. (2006) predicts pixel values basedpon a spatially weighted difference computed between the ETM+nd the MODIS scenes acquired at date T1, and the ETM+ T1-scenend one or more MODIS scenes of prediction day (T2), respectively.he input pairing (T1) criteria for the prediction of synthetic ETM+T2) was based on least amount of cloud cover (almost 0%) and
inimal temporal difference in order to reduce the likelihood forhanges in land cover resulting from harvesting or phenologicalhanges. The input pairs (T1) were selected as March and Septem-er months. Since, the study area comprises winter and summerrops. Generally, early March is the peak time (middle of season)or winter and September is peak time for summer crops. The val-dation of synthetic ETM+ with observed ETM+ surface reflectance
as carried out for the year 2009. The algorithm yielded 46 (8ays composites) 30 m spatial resolution, synthetic ETM+ imagesor the growing seasons (December to April for winter crop and
ay to December for summer crop) using a ETM+ and a MODIScene acquired on 7th March 2009 and 15th September 2009 ashe T1 images, and 8-day MODIS composites between January toecember 2009 as the T2 images for prediction. No images were
ynthesized for March and September 2009 as these imageries weresed as T1 input. Also, the validation was not done for June, Julynd August months because of non-availability of cloud-free ETM+
mages.
The statistical analysis between synthetic and observed ETM+urface reflectance for NIR and green bands for winter and sum-er crops has been carried out. The results are provided in Table 3.
rvation and Geoinformation 13 (2011) 59–69
The first column in each sub-table is showing the coefficient ofdetermination, the second column is showing the intercept, nor-malized to percent total reflectance of the observed image (forinstance a value of 0.1 means the predicted image overestimatedthe reflectance by 10%), the third column is showing the slopeof the relationship between observed and predicted. A two-sidedt-test was used to determine whether there is a statistically signifi-cant difference between observed and predicted surface reflectancevalues (i.e., whether the mean difference between observed andpredicted data varied significantly from zero). The high correlationsbetween observed and predicted pixel values were found for theNIR band (0.64 ≤ r2 ≤ 0.81, p ≤ 0.01), while the green band yieldedslightly weaker relationships (0.49 ≤ r2 ≤ 0.58, p ≤ 0.01) (Table 3). Inall cases the intercepts of the relationship between observed andpredicted images were greater than zero (Table 3) which can beinterpreted as a noise signal likely due to atmospheric and BRDFeffects. These findings are also confirmed by previous studies (Gaoet al., 2006).
Figs. 3a–f and 4a–f show a per-pixel comparison betweenobserved and predicted ETM+ NIR and green surface reflectancefor the winter and summer crops. The first row shows the scatterplots for NIR band and second row for green band. The relation-ship between observed and predicted pixel values closely followedthe 1-to-1 line (Figs. 3a–f and 4a–f) thereby showing that ETM+surface reflectances were accurately predicted by STARFM. Somedeviations from this 1-to-1 line, however, were found for the NIRtowards the end of the summer crops period (Fig. 4b and c). Theharvesting of summer crops (sugarcane) and simultaneously sow-ing of winter crops (wheat) during the month of December createsa complex mixture of land cover type, as a result the scatter plots(Fig. 4b and c) are bit noisy and the coefficient of determinationsfor NIR are also relatively low (r2 = 0.67 and r2 = 0.64) (Table 3). Thesimilar situation was also observed for winter crops (Fig. 3b andc), but less noisy compared to that of summer crops (Fig. 4b andc). The ability of 30 m resolution synthetic ETM+ images for thepredictability of changes depends upon the capacity of MODIS todetect these changes, particularly when they occur in vegetationstructure or stand composition or at sub-pixel ranges (Gao et al.,2006). Complex mixtures of land cover type are a challenge forall methods of data fusion. Therefore, it will be difficult to iden-tify or spatially define individual change events as it is not possibleto depict changes occurring in the sub-MODIS pixel range. As aresult, the algorithm in its current form seems less suited for theprediction of changes in vegetation structure (such as originatingfrom clear cut harvesting or thinning) or changes in land cover.Changes will also not be detected by STARFM when two contradict-ing changes occur within a coarse-resolution pixel simultaneouslyand compensate for each other (Gao et al., 2006).
4. Results
One of the objectives of this paper was to test the suitabil-ity of canopy chlorophyll content related vegetation indices usingsynthetic ETM+ data for GPP estimation. Many current models ofecosystem carbon exchange based on remote sensing, such as theMODIS product termed MOD17, still require considerable inputfrom ground based meteorological measurements and look uptables based on vegetation type. Since these data are often not avail-able at the same spatial scale as the remote sensing imagery, theycan introduce substantial errors (either in the original estimate of
tation type to a pixel into the carbon exchange estimates (Yang etal., 2007; Sims et al., 2008). Therefore, it is worthwhile to explorealternative methods for the estimation of GPP that may not requireas many input parameters. Gitelson et al. (2006) have success-
D. Singh / International Journal of Applied Earth Observation and Geoinformation 13 (2011) 59–69 63
F tancet
ftbcwF
4
oraScpoo
Ft
ig. 3. Per-pixel comparison between observed and predicted ETM+ surface refleche surface reflectance for the NIR and second row for green band, respectively.
ully estimated GPP with chlorophyll indices and clearly indicatedhat the product of total crop chlorophyll content and PAR coulde good indicator of GPP. The GPP used in the present study wasalculated as a product of PAR and crop chlorophyll content. PARas calculated from European Center for Medium Range Weather
orecasting (ECMWF) analysis data.
.1. Spatiotemporal variation of GPP
The spatiotemporal variation of the GPP is illustrated in Fig. 5ver the study area. The absolute difference of GPP values (tempo-al residuals) from MODIS data on 14th September 2009 (Fig. 5a)nd 16th October 2009 (Fig. 5b) and observed ETM+ data on 15th
eptember 2009 (Fig. 5d) and 17th October 2009 (Fig. 5e) wereomputed and shown in Fig. 5c and f, respectively. Similarly therediction residuals were computed from the absolute differencef GPP values from synthetic ETM+ data on 16th October 2009 andbserved ETM+ data on 17th October 2009 (Fig. 5i). The study site
ig. 4. Per-pixel comparison between observed and predicted ETM+ surface reflectance fhe surface reflectance values for the NIR and second row for green band, respectively.
for three different observed ETM+ dates for winter crops. The first row represents
is predominately agricultural land containing many small fieldsof approximately less than a hectare. Sugarcane is the dominatecrop over this area, planted during May and being harvested fromlate September to December month. Close examination of the GPPderived from predicted ETM+ reveals a faint blocky pattern that cor-responds spatially to the locations of the resampled 500 m MODISpixel dimensions. This pattern is most evident across some of thefields (Fig. 5a and b) across some of the fields (Fig. 5h). This isdue to the fact that, the dynamics of surface reflectance are dif-ferent between one field and its neighbor (e.g., due to harvestingin one field and not in an adjacent field), underlying the fact thatmethod are less likely to be valid where the Landsat reflectanceheterogeneity at the sub-MODIS pixel scale changes temporally.
The GPP derived from observed ETM+ data for 15th September(Fig. 5d) is higher than the October 2009 (Fig. 5e) with many fieldsharvested by the later acquisition date. Despite the evident landcover change complexity, the GPP derived from predicted ETM+data on 16th October 2009 captures many of the temporal changes
or three different observed ETM+ dates for summer crops. The first row represents
64 D. Singh / International Journal of Applied Earth Observation and Geoinformation 13 (2011) 59–69
Fig. 5. Illustrating spatiotemporal variation of GPP (g C m2 in 8 days); (a) and (b) GPP from MODIS (MOD09A1) on 14th September and 16th October 2009, (c) the temporalr h Sept( + on( and i2 re leg
(GrfGamTcraa
esidual (absolute value of (a) and (b)), (d) and (e) GPP from observed ETM+ on 15te)), (g) and (h) GPP from observed ETM+ on 17th October 2009 and predicted ETMd), (e), (g) and (h) Are shown with the same contrast stretch. Residual values in c, f.0 ≤ orange ≤ 2.5, red ≤ 2.5. (For interpretation of the references to color in this figu
Fig. 5h). In general, the prediction residuals (spatial mean value ofPP 1.97 g C/m2 in 8 days) was considerably lower than the tempo-
al residuals (spatial mean value of GPP was 4.46 g C/m2 in 8 daysor ETM+ data) that correspond to 12%, 27% of the spatial meanPP 16.53 g C/m2 in 8 days) from ETM+ data on 16th October 2009nd 17th October 2009, respectively, implying that the predictionethod is on an average better than temporal pixel substitution.
emporal residuals of MODIS derived GPP were significantly higherompared to ETM+ data sets (Fig. 5c and f). However, predictionesiduals were also found to be greater than the temporal residu-ls at few locations along contrast boundaries such as river flowinglong the eastern part of the study region. The higher predictions
ember and 17th October 2009, (f) the temporal residual (absolute value of (d) and16th October 2009, (i) the prediction residual (absolute value of g and h). (a), (b),
are colored as: 0 ≤ purple ≤ 0.5, 0.5 ≤ blue ≤ 1.5, 1.0 ≤ green ≤ 1.5, 1.5 ≤ yellow ≤ 2.0,end, the reader is referred to the web version of the article.)
residuals may be due to regions of textural change in addition tomisregistration and resampling impacts that are greatest in regionsof high spatial variation (Roy, 2000).
4.2. Comparisons of GPP
The majority of ETM+ images used for the prediction of NIR and
green surface reflectances were close to 100% cloud-free. However,the GPP calculated using synthetic ETM+ data still include somecontaminated pixels. MODIS and ETM+ data sets are generally well-documented, quality-controlled data sources that have been pre-processed to reduce many of these problems (Smith et al., 1997;
D. Singh / International Journal of Applied Earth Observation and Geoinformation 13 (2011) 59–69 65
Fig. 6. (a) Scatter plot of observed ETM+ GPP (x-axis) versus sensor synthetic ETM+ GPP (y-axis) from the nearest maximum value composite to Landsat acquisition date forw ta derG ODIS df
THauGss
EpvfdE
heat crop. (b) Similar to (a) but for sugarcane. (c) The seasonal dynamics of GPP daPP values over study site derived from synthetic ETM+ GPP. (d) Same as (c) for M
rom 15 years (1995–2009) observed data over study site.
ucker and Pinzon, 2005; James and Kalluri, 1994; Gutman, 1999).owever, some noise is still present in the downloadable data setsnd, therefore, CI time series need to be smoothed before beingsed. Therefore, Savitzky–Golay filtering technique (Savitzky andolay, 1964) was used in order to remove the noise from CI timeeries. This technique was applied to smooth every pixel’s timeeries profile for 10-year (2000–2009) period.
The scatter plots of GPP derived from synthetic and observedTM+ data for winter and summer crops from the nearest com-
osite period are shown in Fig. 6a and b, respectively. The GPPalues derived from observed ETM+ were close to 100% cloud-ree thus provide a reference value for the comparison of GPPerived from synthetic ETM+ data. The total number observedTM+ scenes used for winter and summer seasons were 20 and
ived from observed and synthetic ETM+ scenes. The line plot show the 8 days meanerived GPP. (e) and (f) Show monthly temperature and rainfall anomalies derived
44, respectively. A strong linear relations (winter crops, r2 = 0.85,p ≤ 0.01 and summer crops, r2 = 0.86, p ≤ 0.01) exist between GPPvalues from observed and synthetic ETM+ data. The values of coeffi-cient of determination (winter crops, r2 = 0.85, p ≤ 0.01 and summercrops, r2 = 0.86, p ≤ 0.01) were found to be almost equal for twocanopies. It means that chlorophyll content is main driver of GPPand that structure plays a small role in this relation. This result isalso consistent with the study of Gitelson et al. (2005) which showssmall differences between GPP estimation for soybeans and maize
that have very different canopy structure. Higher chlorophyll con-tent is directly correlated with increased photosynthetic capacitythrough increased light use efficiency (Field and Mooney, 1986).These conclusions imply that GPP estimation from chlorophyll-related indices and PAR is of particular significance for remote
6 h Obse
sts
(towttacidgtEGaTllffslsfwmstdtofiimiso
caMiyctE2dit2sleamnmamaa
6 D. Singh / International Journal of Applied Eart
ensing applications provided one can obtain accurate informa-ion of chlorophyll as canopy structure difference exerts a relativelymall effect.
The GPP time series from synthetic ETM+ and MODISMOD09A1) are presented in Fig. 6c and d, respectively. The GPPime series from synthetic ETM+ was generated at 30 m spatial res-lution by taking the mean of GPP (Fig. 6c) over study area for 46eeks (8-day composites) for 10 years period (2000–2009). This
ime series was resampled to 500 m spatial resolution in ordero compare with corresponding MODIS derived GPP time seriess shown in Fig. 6d. The first maxima correspondences to winterrops (wheat) while the second one for summer crops (sugarcane)n each year. All the curves of GPP for the year 2000–2009 showistinctive seasonal dynamics. It may be observed that vegetationreen-up and leaf-down at the beginning and at the end of the vege-ation period were well captured by the GPP derived from syntheticTM+ (Fig. 6c) corresponding to MODIS derived GPP (Fig. 6d). ThePP time series by and large represent crops, since only a smallrea of scattered trees and bushes are present over the study site.he GPP values are maximum around week 8 (winter crops) fol-owed by second maxima around 38 week (summer crops). Theong-term spatial mean GPP values (Fig. 6c and d, black line) wereound to be higher in the summer season than in the winter season,or instance, first maxima (15 g C/m2 in 8 days) around February andecond maxima (18 g C/m2 in 8 days) in September. The relativelyow GPP in the winter season can be attributed to the length of cropeason and as a result more accumulation of chlorophyll contentsor the crops of longer time period. The winter crops are mainlyheat crop of four month time period and sugarcane is main sum-er crop of eight month time period over the study site. This may
uggests, that synthetic ETM+ derived GPP may potentially be usedo quantify seasonal changes in vegetation, the rate of increase andecrease of the GPP, the dates of the beginning, end and peak(s) ofhe growing season, the length of the growing season, the timingf the annual maximum GPP and the GPP value at a fixed date atne spatial scales (Gao et al., 2006). Similar patterns are observed
n case of MODIS derived GPP (Fig. 6d). However, there were twoajor differences observed between these two data sets. First, the
nterannual variations were found to be more distinct in case ofynthetic ETM+ GPP compared to MODIS. Second, MODIS GPP wasverestimated.
A seasonal pattern of synthetic ETM+ GPP (Fig. 6c) showslear effects by two severe drought events during the year 2002nd 2004, when one can find considerable overestimation of theODIS GPP (Fig. 6d). The recent studies also support these find-
ngs (Susmitha et al., 2009). The mean spatial GPP (mean of 10ears) over study site is shown as black line (Fig. 6c and d), whichlearly separates the years of healthy and poor vegetation condi-ions during the summer season. The maximum value of syntheticTM+ mean spatial GPP values (Fig. 6c observed during the year002 and 2004 were significantly lower (12.5 and 13.5 g C/m2 in 8ays, respectively) compared to mean spatial GPP value (18 g C/m2
n 8 days)) and also with other years. The lower value of GPP forhe year 2002 attributed to no rainfall event during month of July002 (Susmitha et al., 2009). The year 2004 also suffered with fourhort or long monsoon breaks, but these were intermittent and ofess duration compared to the July month of year 2002 (Susmithat al., 2009). The extent of negative deviation of observed rainfallnd positive deviation of observed temperature from its long-termean for a pixel, district or region, and the duration of continuous
egative and positive deviations are powerful indicators of drought
agnitude and persistence. The monthly temperature and rainfall
nomalies were compute as a difference of value minus long-termean (1995–2009) as shown in Fig. 6e and f, respectively. Fig. 6e
nd f clearly shows that, overall, the study region was dry in 2002nd 2004 during the monsoon period (June to September) com-
rvation and Geoinformation 13 (2011) 59–69
pared to that of other years. The precipitation dynamics is a key linkbetween climate fluctuation and vegetation dynamics in space andtime. The less precipitation reduces plants water availability in thedry season. Generally, the sowing time of summer crops over thisarea starts from middle of June and therefore, July rainfall is verycritical for the growth of vegetation. The drought years 2002 and2004 are very well brought by synthetic ETM+ derived GPP at 30 mspatial resolution. Similar results were also observed for MODISGPP (Fig. 6d), but with overestimation. This finding was also foundto be in confirmation with previous study (Hwang et al., 2008).
4.3. The relationship between ETM+ GPP and evapotranspiration
The crop chlorophyll content was shown to be sensitive tochanges in vegetation conditions, since it is directly influencedby the chlorophyll’s absorption of the sun’s radiation (Gitelson etal., 2008). Because the chlorophyll status integrates the effects ofnumerous environmental factors, the GPP derived as a product ofcrop chlorophyll content and PAR could be useful parameter forcrop monitoring. However, the validation of ETM+ derived GPP at30 m resolution is problematic because there are very limited avail-able field data. Ideally, the testing sites should cover as many asbiomes types and climate regimes as possible. Eddy flux towersoffer invaluable opportunities to validate process-based ecosystemmodels and satellite data because they measure carbon, water andenergy exchange on a long-term and continuous basis (Baldocchiet al., 2001; Running et al., 1999). Unfortunately, there are no eddyflux towers over this region. In addition to CO2 flux data, H2O fluxdata from the eddy flux tower sites should be used for evaluatingthe process-based and satellite-based models, particularly in thoseflux sites where there were large numbers of missing CO2 flux datain the wet season (Saleska et al., 2003). As photosynthesis is closelycoupled with H2O flux (evapotranspiration), therefore observedevapotranspiration was used to study the relationship betweensynthetic ETM+ GPP. Atmospheric and radiation variables are con-tinuously measured at the weather station of the study site. Theevaporation measurements are made using the class A pan evapor-imeter by taking into account not only of evaporation loss but alsogains due to rainfall. A rain gauge situated nearby is used to assessthe depth of rain falling in the pan. The evaporation measurementsare done twice a day (8:30 A.M. and 5:30 P.M.) on the days withoutrain. Reference evapotranspiration by pan method was estimatedfrom measurements of daily Class A evaporation pan, based on therelationship Eo = Ep × kp, where Eo is the daily reference evapotran-spiration (mm), kp is the pan coefficient (Doorenbos and Pruitt,1977) and Ep is the pan evaporation (mm).
The coefficient of determination was obtained from the linearregression analysis in order to evaluate the performance of the rela-tionship between synthetic ETM+ GPP derived in this research andobserved evapotranspiration data. The regression analysis has beencarried out on entire data set without bifurcating into winter andsummer seasons. Fig. 7 shows the results of the statistical compar-ison of the daily reference evapotranspiration values for intervalsof 1–8 days, from the year 2000 to 2009.These two data sets arefound to be in good agreement (r2 = 0.665, p ≤ 0.01). Even thoughthis study site by and large represents agriculture land, still thereare some human land uses such as road, building and some wasteland. Therefore, when applying the spatial pattern of vegetation inthe regression analysis, these patches of different land cover typescould also be a potential source of error. These data sets also includetwo drought years 2002 and 2004 (Fig. 6c and d). The impact of
drought on GPP has been shown to vary with the intensity of thedrought (Reichstein et al., 2002; Barr et al., 2004; Kljun et al., 2006;Krishnan et al., 2006). These climatic perturbations could also haveimpact on regression analysis between observed evapotranspira-tion and GPP from synthetic ETM+ data.
D. Singh / International Journal of Applied Earth Obse
FE
5
LtCtvLToasciTltsdtaNgtatpttmgMtfts
ecttasdws
ig. 7. The scatter plot of observed evapotranspiration (ET) and GPP from predictedTM+.
. Discussions
As currently only the SLC-off Landsat ETM+ and the agingandsat-5 TM (Chander et al., 2007; Helder and Ruggles, 2004) sys-ems are acquiring data, and only a single successor Landsat Dataontinuity Mission (LDCM) sensor is scheduled for launch early inhe next decade (Irons and Masek, 2006), a potential solution to pro-ide more frequent high resolution surface observations is to fuseandsat observations with data from other remote sensing systems.his study investigated the capability of STARFM (Gao et al., 2006)ver a tropical region (India) to predict seasonal changes in winternd summer crops for a higher level of complexity due to small fieldizes, diversified cropping pattern and field-to-field variability inrop phenology and management practices, which is different thants use in previous studies (Gao et al., 2006; Hilker et al., 2009).he prediction residuals (Fig. 5i) were found to be considerablyower than the temporal residuals (Fig. 5c and f), suggesting thathe prediction method is on an average better than temporal pixelubstitution. The higher correlations between observed and pre-icted pixel values for the NIR and green band (Table 3) may suggestheir use for the derivation of the product such as green chlorophyllt 30 m spatial resolution. The validation of synthetic 8 day ETM+IR reflectance with single day observed ETM+ NIR showed a veryood agreement (Figs. 3 and 4), implying the capability of predic-ion method. In the present study, MODIS 8 day composite imagesre used rather than daily MODIS reflectance products, becausehey yielded largely cloud-free images and can therefore help toredict changes in reflectance of vegetation over tropical area ofhe earth where cloud cover prevents frequent cloud-free observa-ions. The use of MODIS composites rather than single observations
ay, however, impact the average reflectance brightness for aiven image region, depending on the MODIS scenes used in theOD09A1 product and is therefore at the same time also a limita-
ion to the applied technique as the composition of data originatingrom multiple viewing angles and the variation of vegetation withinhe 8-day production period which differs from the Landsat acqui-ition date, also provides a possible source of error.
GPP is often used as a monitoring tool for the productivity, veg-tation health and dynamics, enabling easy temporal and spatialomparisons (Myneni et al., 1997). Benefits of this work includehe successful data fusion of ETM+ and MODIS data sets, enablinghe use of the longer Landsat TM/ETM+ GPP time series for winter
nd summer seasons over a tropical area. Direct comparisons ofynthetic ETM+ GPP with MODIS GPP revealed several importantistinctions and similarities. One obvious difference was associatedith image/map resolution. Synthetic ETM+ captured much of the
patial complexity of land cover at the study site (Fig. 5f). In con-
rvation and Geoinformation 13 (2011) 59–69 67
trast, the relatively coarse-resolution of MODIS did not allow forthat level of spatial detail (Fig. 5c). The greatest difference was anover estimation by MODIS GPP (Fig. 6d). This is because MODIS datahave significantly larger grain size (500 m), which can be expectedto lower overall spatial variance (Woodcock and Strahler, 1987;Cohen et al., 1990). Furthermore, at larger grain sizes, there is lesslikelihood that class-specific relationships are practical. However,due to difference in spectral bands, temporal compositing, spatialresolution and other sensor characteristics, there has never beenthe expectation that GPP values from these two sensors (syntheticETM+ and MODIS) here will match perfectly. Empirical techniquescan be used to force agreement, yet there remains a question ofhow well the resulting time series matches reality. Comparison pro-vides evidence that GPP time series does represent what is actuallyhappening on the ground.
The regression analysis between synthetic ETM+ GPP andobserved evapotranspiration over the study site is shown in Fig. 7.The observed ET and GPP are found to be in good agreement(r2 = 0.66, p ≤ 0.01). High correlation was observed between GPPand ET, since vegetation grows well for high precipitation andtherefore, increases in resulting GPP (Korner, 1994; McMurtrie etal., 1992). Plant transpiration is controlled by canopy conductance,which further represents the average status of leaf level stomatalconductance. Therefore, a fluctuation in stomatal conductance usu-ally leads to a commensurately large fluctuation in transpiration,and hence, ET. In semiarid or arid systems, soil evaporation is amajor component of ET, yet little is known quantitatively about itover large spatial scales. Soil evaporation is reported to range froma few percent to more than 80% of the measured or estimated totalET depending on the vegetation cover (Bethenod et al., 2000; Hsiaoand Xu, 2005; Villalobos and Fereres, 1990; Wilson et al., 2000).Few studies have provided in situ seasonal measurements of leafoptical properties over plant growing seasons in the tropical area(Roberts et al., 1998). The future field work should focus on sea-sonal measurements of leaf water content, chlorophyll, dry matter,and leaf phenology (leaf fall and emergence of new leaves) overseasons, in support of temporal analyses of GPP.
Algorithms like the one used in this study are important com-ponents of current research efforts seeking to map high spatialresolution changes in vegetation cover and status with high tem-poral density, over larger areas. Data blending approaches, such asSTARFM can help in minimizing the technical limitations and trade-offs associated with information needs that require data with bothhigh spatial and high temporal resolutions. Applications such asmonitoring seasonal changes in vegetation biophysical and struc-tural attributes over tropical areas can benefit from the synergies ofmultiple data sources such as MODIS and Landsat ETM+. Advancesin data blending can also influence the design of new sensors, wherethe advantages of different spatial and temporal resolutions maybe fully realized in the creation of different sensors on differentplatforms, with the complementary nature of these systems in adata blending approach, considered from the outset of the designprocess. Tactical decision making on land management can bene-fit from immediate access to the synthetic data especially over theheterogeneous areas of very small crop field. At this stage, however,the synthetic ETM+ data should be considered only a general solu-tion. Until more detailed models are ready that begin with syntheticETM+ and add computations of species, and even of cultivar-specificdevelopmental sequences, it will lack the accuracy desired for spe-cific crops and areas.
6. Conclusions
The STARFM algorithm has been successfully used in this studyfor complex mixture of agriculture land over a tropical area to
6 h Obse
py(pmarccccTpoGps2Tostfica
A
DetSDUcffa
R
A
A
B
B
B
B
B
C
8 D. Singh / International Journal of Applied Eart
redict reflectance for NIR and green bands over a period of 10ears (2000–2009). The accuracy was found to be better for NIRmean r2 = 0.71, p ≤ 0.01) compared to green band (mean r2 = 0.53;≤ 0.01) for winter and summer crops. The prediction methodaintained a high spatial level of detail in the predicted scenes,
s the prediction residuals were significantly lower than tempo-al residuals, it seems however, less well suited to predict suddenhanges in land cover, such as induced by harvesting or sowing ofrops. Gitelson et al. (2006) have successfully estimated GPP withhlorophyll indices and clearly indicated that the product of totalrop chlorophyll content and PAR could be good indicator of GPP.hus, this study has also demonstrated the utility of canopy chloro-hyll content derived from synthetic ETM+ data in the estimationf GPP that will provide new avenue for future remote sensing ofPP. The synthetic ETM+ GPP demonstrated the capability of map-ing the seasonal and interannaul variations in vegetation at ETM+patial resolution and 8-day time intervals. The two drought years002 and 2004 were clearly brought out by synthetic ETM+ GPP.he study of the relationship of synthetic ETM+ GPP with evap-transpiration yielded good agreement (r2 = 0.66; p ≤ 0.01)). Thistudy suggests that MODIS composites can be a useful alternativeo daily observations, since the prevailing cloud conditions preventrequent clear sky observations during monsoon period. Compos-tes may however reduce the quality of STARFM predictions due tohanges in pixel brightness resulting from remaining directional ortmospheric impacts in the different MODIS images.
cknowledgements
I am very much thankful to Dr. T. Ramasami, Secretary,epartment of science and Technology, for his kind support andncouragement during the course of this study. Author would likeo thank Dr. Feng Gao, Biospheric Sciences Branch, NASA Goddardpace Flight Center, USA for providing the STARFM algorithm andr. Thomas Hilker, Department of Forest Resource Management,niversity of British Columbia, Vancouver, Canada, for useful dis-ussions. I thank the subject editor and two anonymous reviewersor comments on the manuscript. Author is also thankful to NASAor providing Landsat-7 ETM+ and MODIS data and ECMWF fornalysis data.
eferences
cerbi-Junior, F.W., Clevers, J., Schaepman, M.E., 2006. the assessment of multisensorimage fusion using wavelet transforms for mapping the Brazilian savanna. Inter-national Journal of Applied Earth Observation and Geoinformation 8, 278–288.
hl, D.E., Gower, S.T., Mackay, D.S., Burrows, S.N., Norman, J.M., Diak, G.R., 2004.Heterogeneity of light use efficiency in a northern Wisconsin forest: implicationsfor modeling net primary production with remote sensing. Remote Sensing ofEnvironment 93 (1/2), 168–178.
aldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S.W., 2001. FLUXNET:a new tool to study the temporal and spatial variability of ecosystem-scale car-bon dioxide, water vapor, and energy flux densities. Bulletin of the AmericanMeteorological Society 82, 2415–2434.
arr, A.G., Black, T.A., Hogg, E.H., Kljun, N., Morgenstern, K., Nesic, Z., 2004. Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest inrelation to net ecosystem production. Agriculture and Forest Meteorology 126,237–255.
ernstein, L.S., Adler-Golden, S.M., Sundberg, R.L., Levine, R.Y., Perkins, T.C., Berk,A., Ratkowski, A.J., Felde, G., Hoke, M.L., 2005. A new method for atmosphericcorrection and aerosol optical property retrieval for VIS-SWIR multi- and hyper-spectral imaging sensors: QUAC (QUick atmospheric correction). In: Proceedingsof IGARSS, pp. 3549–3552.
ethenod, O., Katerji, N., Goujet, R., Bertolini, J.M., Rana, G., 2000. Determinationand validation of corn crop transpiration by sap flow measurement under fieldconditions. Theoretical and Applied Climatology 67, 153–160.
usetto, L., Michele, M., Roberto, C., 2008. Combining medium and coarse spatialresolution satellite data to improve the estimation of sub-pixel NDVI time series.Remote Sensing of Environment 112 (1), 118–131.
Carper, W.J., Lillesand, T.M., Kiefer, R.W., 1990. The use of intensity–hue-saturationtransformations for merging SPOT panchromatic and multispectral image data.Photogrammetric Engineering and Remote Sensing 56, 459–467.
Cohen, W., Spies, T., Bradshaw, G., 1990. Semivariograms of digital imagery for anal-ysis of conifer canopy structure. Remote Sensing of Environment 34, 167–178.
Doorenbos, J., Pruitt, W.O., 1977, Guidelines for Predicting Crop Water Require-ments. FAO Irrigation and Drainage Paper no. 24, Rome, Italy.
Field, C., Mooney, H.A., 1986. The photosynthesis–nitrogen relationship in wildplants. In: Givnish, T.J. (Ed.), On the Economy of Plant Form and Function. Cam-bridge University Press, Cambridge, pp. 25–55.
Gao, F., Masek, J., Hall, H., Schwaller, M., 2006. On the blending of the Landsat andMODIS surface reflectance: predicting daily Landsat surface reflectance. IEEETransactions on Geosciences and Remote Sensing 44, 2207–2218.
Gitelson, A.A., Vina, A., Verma, S.B., Rundquist, R.D., Arkebauer, T.J., Keydan, g., Leav-itt, B., Ciganda, V., Burba, G.G., Suyker, A.E., 2006. Relationship between grossprimary production and chlorophyll content in crops: implications for the syn-optic monitoring of vegetation productivity. Journal of Geophysical. Research111, D08 S11.
Gitelson, A.A., Vina, A., Rundquist, D.C., Ciganda, V., Arkebauer, T.J., 2005. Remoteestimation of canopy chlorophyll content in crops. Geophysical Research Letter32, L08 403.
Gitelson, A.A., 2004. Wide dynamic range vegetation index for remote quantificationof biophysical characteristics of vegetation. Journal of Plant Physiology 161 (2),165–173.
Gitelson, A.A., Vina, A., Masek, J.G., VermaF S.B., Suyker, A.E., 2008. Synoptic monitor-ing of gross primary productivity of maize using Landsat data. IEEE Geoscienceand Remote Sensing Letters 5 (2).
Gutman, G.G., 1999. On the use of long-term global data of land reflectances andvegetation indices derived from the AVHRR. Journal of Geophysical Research104, 6241–6255.
Hansen, M.C., Roy, D.P., Lindquist, E., Adusei, B., Justice, C.O., Altstatt, A., 2008. Amethod for integrating MODIS and Landsat data for systematic monitoring offorest covers and changes in the Congo Basin. Remote Sensing of Environment112, 2495–2513.
Helder, D., Ruggles, T., 2004. Landsat thematic mapper reflective-band radiome-ter artifacts. IEEE Transactions on Geoscience and Remote Sensing 42 (12),2704–2716.
Hilker, T., Wulder, M.A., Coops, N.C., Seitz, N., White, J.C., Gao, F., Masek, J.G., Sten-house, G., 2009. Generation of dense time series synthetic Landsat data throughdata blending with MODIS using a spatial and temporal adaptive reflectancefusion model. Remote Sensing of Environment 113, 1988–1999.
Hsiao, T., Xu, L., 2005. Evapotranspiration and relative contribution by the soil andthe plant. California Water Plan Update. UC Davis.
Hwang, T., Kang, S., Kim, J., Kim, Y., Lee, D., Band, L., 2008. Evaluating drought effecton MODIS Gross Primary Production (GPP) with an eco-hydrological model inthe mountainous forest, East Asia. Global Change Biology 14, 1–20.
Irons, J., Masek, J., 2006. Requirements for a Landsat data continuity mission. Pho-togrammetic Engineering and Remote Sensing 72 (10), 1102–1108.
James, M.E., Kalluri, S.N.V., 1994. The pathfinder AVHRR land data set: an improvedcoarse resolution data set for terrestrial monitoring. International Journal ofRemote Sensing 15, 3347–3363.
Justice, C.O., Townshend, J., 2002. Special issue on the moderate resolution spec-troradiometer (MODIS); a new generation of land surface monitoring. RemoteSensing of Environment 83 (1), 1–2.
Kalvelage, T., Williams, J., 2005. Supporting users through integrated retrieval pro-cessing and distribution systems at the land processes distributed active archivecenter. Acta Astronaut 56 (7), 681–687.
Kljun, N., Black, T.A., Griffis, T.J., Barr, A.G., Gaumont-Guay, D., Morgenstern, K.,McCaughey, J.H., Nesic, Z., 2006. Response of net ecosystem productivity of threeboreal forest stands to drought. Ecosystems 9, 1128–1144.
Krishnan, P., Black, T.A., Grant, N.J., Barr, A.G., Hogg, E.T.H., Jassal, R.S., Morgenstern,K., 2006. Impact of changing soil moisture distribution on net ecosystem pro-ductivity of a boreal aspen forest during and following drought. Agriculture andForest Meteorology 139, 208–223.
Korner, Ch., 1994. Leaf diffusive conductances in the major vegetation types of theglobe. In: Schulze, E.-D., Caldwell, M.M. (Eds.), Ecophysiology of Photosynthesis.Springer, New York, pp. 463–490.
Markham, B.L., Thome, K., Barsi, J., Kaita, E., Helder, D., Barker, J., Scaramuzza, P., 2004.Landsat-7 ETM+ on orbit reflective band radiometric stability and absolute cali-bration. IEEE Transactions and Geoscience and Remote Sensing 42, 2810–2820.
McMurtrie, R.E., Leuning, R., Thompson, W.A., Wheeler, A.M., 1992. A model ofcanopy photosynthesis and water use incorporating a mechanistic formulationof leaf CO2 exchange. Forest Ecology and Management 52, 261–278.
Myneni, R.B., Keeling, C.D., Tucker, C.J., Asrar, G., Nemani, R.R., 1997. Increased plantgrowth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702.
Reichstein, M., Tenhunen, J.D., Roupsard, O., 2002. Severe drought effects on ecosys-tem CO2 and H2O fluxes at three Mediterranean evergreen sites: revision of
current hypotheses? Global Change Biology 8, 999–1017.
Roberts, D.A., Nelson, B.W., Adams, J.B., Palmer, F., 1998. Spectral changes with leafaging in Amazon caatinga. Trees-Structure and Function 12, 315–325.
Ruimy, A., Kergoat, L., Bondeau, A., 1999. Comparing global models of terrestrialnet primary productivity (NPP): analysis of differences in light absorption andlight-use efficiency. Global Change Biology 5 (1), 56–64.
h Obse
R
R
R
R
R
S
SS
S
S
D. Singh / International Journal of Applied Eart
oy, D.P., Dikshit, O., 1994. Investigation of image resampling effects upon the tex-tural information content of a high spatial resolution remotely sensed image.International Journal of Remote Sensing 15, 1123–1130.
oy, D.P., 2000. The impact of misregistration upon composited wide field of viewsatellite data and implications for change detection. IEEE Transactions on Geo-science and Remote Sensing 38, 2017–2032.
oy, P., Junchang, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M., Lindquist, E., 2008. Multi-temporal MODIS–Landsat data fusion for relative radiometric normalization,gap filling, and prediction of Landsat data. Remote Sensing of Environment 112,3112–3130.
unning, S.W., Nemani, R., Glassy, J. M., Thornton, P., 1999, MODIS daily photo-synthesis (PSN) and annual net primary production (NPP) product (MOD17).Algorithm theoretical basis document, version 3.0. http://modis.gsfc.nasa.gov.
unning, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M.S., Reeves, M., Hashimoto, H.,2004. A continuous satellite-derived measure of global terrestrial primary pro-duction. Bioscience 54 (6), 547–560.
avitzky, A., Golay, M.J.E., 1964. Analytical Chemistry 36, 1627–1639.hettigara, V.K., 1992. A generalized component substitution technique for
spatial enhancement of multispectral images using a higher resolutiondata set. Photogrammetric Engineering and Remote Sensing 58, 561–567.
mith, P.M., Kalluri, S.N.V., Prince, S.D., Defries, R., 1997. The NOAA/NASA pathfinderAVHRR 8-km land data set. Photogrammetric Engineering and Remote Sensing63, 12–31.
ims, D.A., Rahman, A.F., Cordova, V.D., El-Masri, B.Z., Baldocchi, D.D., Bolstad, P.V.,Flanagan, L.B., Goldstein, A.H., Hollinger, D.Y., Misson, L., Monson, R.K., Oechel,W.C., Schmid, H.P., Wofsy, S.C., Liukang Xu, 2008. A new model of gross primaryproductivity for North American ecosystems based solely on the enhanced veg-etation index and land surface temperature from MODIS. Remote Sensing ofEnvironment 112, 1633–1646.
rvation and Geoinformation 13 (2011) 59–69 69
Susmitha, J., Sahai, A.K., Goswami, B.N., 2009. Eastward propagating MJO duringboreal summer and Indian monsoon droughts. Climate Dynamics 32 (7–9),1139–1153.
Suyker, A.E., Verma, S.B., Burba, G.G., Arkebauer, T.J., 2005. Gross primary productionand ecosystem respiration of irrigated maize and irrigated soybean during agrowing season. Agriculture and Forest Meteorology 131 (3/4), 180–190.
Trishchenko, A.P., Luo, Y., Khlopenkov, K.V., 2006. A method for downscaling MODISland channels to 250 spatial resolution using adaptive regression and normal-ization. SPIE, paper No:6366-07, 1–8.
Tucker, C.J., Pinzon, J.E., 2005. An extended AVHRR 8-km NDVI dataset compatiblewith MODIS and SPOT vegetation NDVI data. International Journal of RemoteSensing 26, 4485–4498.
Turner, D.P., Ritts, W.D., Cohen, W.B., Maeirsperger, T.K., Gower, S.T., Kirschbaum,A.A., Running, S.W., Zhao, M.S., Wofsy, S.C., Dunn, A.L., Law, B.E., Campbell, J.L.,Oechel, W.C., Kwon, H.J., Meyers, T.P., Small, E.E., Kurc, S.A., Gamon, J.A., 2005.Site-level evaluation of satellite-based global terrestrial gross primary produc-tion and net primary production monitoring. Global Change Biology 11 (4),666–684.
Wilson, K.B., Hansonb, P.J., Baldocchi, D.D., 2000. Factors controlling evaporation andenergy partitioning beneath a deciduous forest over an annual cycle. Agriculturaland Forest Meteorology 102, 83–103.
Woodcock, C.E., Strahler, A.H., 1987. The factor of scale in remote sensing. RemoteSensing of Environment 21, 311–332.
Yang, F., Ichii, K., White, M.A., Hashimoto, H., Michaelis, A.R., Votava, P., Zhu, A.X.,Huete, A., Running, S., Nemani, R.R., 2007. Developing a continental-scale mea-sure of gross primary production by combining MODIS and Ameriflux datathrough support vector machine. Remote Sensing of Environment 110, 109–122.
Zhao, M., Heinsch, F.A., Nemani, R.R., Running, S.W., 2005. Improvements of theMODIS terrestrial gross and net primary production global data set. RemoteSensing of Environment 95 (2), 164–176.
