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Angular Effects and Correction for Medium Resolution Sensors to Support Crop Monitoring Feng Gao, Tao He, Jeffrey G. Masek, Yanmin Shuai, Crystal B. Schaaf, and Zhuosen Wang

Abstract—Remote sensing imagery at medium spatial resolutions (20–60 m) such as Landsat, the advanced wide field sensor (AWiFS) and the disaster monitoring constellation (DMC) have been broadly used in mapping crop types and monitoring crop conditions. This paper examines the influence of viewing and illumination angular effects on surface reflectance of typical surface and crop types for both narrow swath (e.g., Landsat) and wide swath (e.g., AWiFS) sensors. Three types of angular effects: 1) view angle effect; 2) day of year effect; and 3) mean local time drift effect were analyzed based on both field and satellite bi-directional reflectance distribution function (BRDF) measurements. In order to correct these angular effects, a BRDF look-up map (LUM) for major cover types was built using the cropland data layer (CDL) and the Moderate-Resolution Imaging Spectroradiometer (MODIS) BRDF products. The BRDF LUM was applied to an AWiFS image to correct view angle effects in an agricultural area in central Illinois. The resulting nadir BRDFadjusted reflectance (NBAR) provides a consistent data source for intra-annual crop condition monitoring and inter-annual timeseries analysis. Index Terms—Advanced wide field sensor (AWiFS), bi-directional reflectance distribution function (BRDF), crop condition, directional reflectance, Landsat, time-series analysis.

I. I NTRODUCTION

M

EDIUM SPATIAL resolution (20–60 m) remote sensing imagery from narrow or wide swath sensors have been widely used in mapping crop types and monitoring crop conditions at regional, continental, and global scales. A near-nadir, narrow swath sensor such as Landsat (185-km

Manuscript received November 15, 2013; revised March 31, 2014; accepted April 17, 2014. This work was supported in part by the NASA Land Cover Project Science Office, in part by the NASA Terrestrial Ecology program, and in part by the U.S. Geological Survey (USGS) Landsat Science Team program. USDA and NASA are equal opportunity providers and employers. F. Gao is with the Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service (ARS), Beltsville, MD 20705 USA (e-mail: [email protected]). T. He is with the Department of Geographical Sciences, University of Maryland, College Park, MD 20742 USA (e-mail: [email protected]). J. G. Masek is with the NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA (e-mail: [email protected]). Y. Shuai is with the Earth Resources Technology Inc. and the NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA (e-mail: shuaiym@ gmail.com). C. B. Schaaf is with the School for the Environment, University of Massachusetts Boston, Boston, MA 02125 USA (e-mail: Crystal.Schaaf@ umb.edu). Z. Wang is with the NASA Postdoctoral Program Fellow, NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA (e-mail: zhuosen.wang@ nasa.gov). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTARS.2014.2343592

swath) provides an effective spatial resolution (30 m) for crop type mapping [1]. In the last several years, as Landsat-5 reached its end of operation and Landsat-7 suffered from its scan-line-corrector problem, the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) began using medium resolution wide swath sensor data such as the Indian Remote Sensing (IRS) Satellite, advanced wide field sensor (AWiFS) (740-km swath, 56-m spatial resolution), and the disaster monitoring constellation (DMC) Deimos-1/UK2 (640-km swath, 22-m spatial resolution) for crop type classification in addition to Landsat [2]. Since additional angular effects have been observed in these wide swath data [3], it is desirable to understand and correct the angular effects for many quantitative applications that rely on remote sensing data acquired from multiple dates with different viewing and solar angles. In addition, with the advent of free medium resolution imagery (including Landsat and the upcoming ESA Sentinel-2 mission), more applications are relying on the use of dense time series and per-pixel compositing approaches [4]. It is important to understand the reflectance variability due to mixing off-nadir observations within time series or composites. Angular effects arising from variable viewing and solar geometry can cause significant variation in retrieved directional reflectance, even in the absence of changes in vegetation type, condition, or soil background. These effects on surface reflectance have been observed for many decades from field measurements [5]–[10] and satellite observations [11]–[15]. However, the angular effects on medium resolution sensors have not been fully explored and understood. Three major causes for angular effects in medium resolution data can be considered. The first arises from variations in view angle across a single remote sensing image frame or from one image frame to an adjacent, overlapping frame (“view angle effect”). The second arises from variations in solar geometry associated with different anniversary acquisition dates (“day of year effect”). The third arises from drift in mean local time (MLT) throughout the life of the mission (“MLT drift effect”). In general, all three effects are caused by inconsistent sun-targetsensor geometry, which results in inconsistent sampling of the directional reflectance bi-directional reflectance distribution function (BRDF) in space and time. As surface type mapping requires consistent reflectance for the same surface type across the image frame, and crop condition monitoring requires dense time series of imagery to map changes through time, it becomes important to understand that observed temporal changes are due to vegetation

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and crop condition, and not simply to changes in suntarget-sensor geometry. Although directional observations that sample BRDF variability have been found useful in retrieving vegetation structure and biophysical parameters [16], [17], this study focuses on understanding and normalizing angular effects for medium resolution sensors in order to generate more consistent time series. There are several approaches available to normalize BRDF effects for medium resolution sensors. Roy et al. [18] used Moderate-Resolution Imaging Spectroradiometer (MODIS) BRDF parameters at MODIS pixel level to correct BRDF effects for Landsat imagery. Li et al. [19] utilized regional BRDF information from MODIS BRDF product and corrected angular effect for Landsat scenes. Shuai et al. [20] developed an approach to extract BRDF parameters for different surface types based on MODIS pure pixels and used this information to correct angular effects for the corresponding surface types. In this paper, we evaluated the magnitude of each of the three angular effects using BRDF parameters derived from in situ measurements for various surface and crop types. BRDF look-up maps (LUMs) for major crop types at different growth conditions were established using the MODIS BRDF products and the cropland data layer (CDL) from USDA NASS [2]. The correction of angular effects based on these BRDF LUMs was then applied to an AWiFS image in central Illinois to illustrate a practical application. II. E XPERIMENTAL A PPROACH Directional reflectance can be described using the BRDF, in that the reflectance depends on the sun-target geometry (solar zenith and azimuth) as well as the target-sensor geometry (view zenith and azimuth) [21]. We have used a semi-empirical kernel-driven BRDF model, the RossThickLiSparse-Reciprocal model [22], [23], to investigate the influence of angular effects for crop and typical natural vegetation types. The RossThick-LiSparse-Reciprocal model is a linear BRDF model that has been used as the main algorithm in the operational MODIS BRDF/Albedo production [11], [24]. In this paper, we have first used BRDF parameters derived from in situ and airborne measurements to quantify the magnitude of the angular effects associated with medium resolution sensors. Two groups of measurements were organized for the analysis. The first group included 73 BRDF in situ measurements which were primarily obtained from the Portable Apparatus for Rapid Acquisition of Bidirectional Observations of the Land and Atmosphere (PARABOLA) radiometer data [5]–[8] and radiometer measurements obtained by Daniel Kimes at NASA Goddard Space Flight Center [9], [10]. Airborne polarization and directionality of the Earth’s reflectances (POLDER) data [12]–[14] were also included in the collection. These high quality measurements were sampled over various surface properties such as land cover type and leaf area index. Among these frequently used 73 measurements [23]–[28], about a third of them were related to croplands (field, bare soil, grasses, and various crop types). The second group included 10 typical surface types (four crops) chosen from the first group which were measured by the PARABOLA and radiometer on the ground for detailed analysis. Table I lists

TABLE I L IST OF S ELECTED I N SITU M EASUREMENTS

the canopy LAI values, the ranges of solar zenith angles and the white-sky albedos (or bi-hemispheric reflectance) for the second group calculated from the original measurements. Using the BRDF parameters derived from these field measurements, we assessed the magnitude of angular effects on surface reflectance for Landsat and AWiFS. The view angle effect was calculated using view zenith angles varying from ±8◦ and ±30◦ from nadir view, corresponding to the maximum view angles of the Landsat and AWiFS sensors, respectively. The day of year effect was examined by considering solar zenith angle of each day at the sensor overpass time. The MLT drift effect was tested using the actual data acquisition time based on the historical Landsat-5 record. Our second objective in this paper was to demonstrate the correction of Landsat and AWIFS imagery to nadir view using a crop-specific BRDF LUM based on MODIS BRDF data combined with the USDA CDL map. MODIS sensors aboard the Terra and Aqua platforms provide directional global observations from space at least twice per day. The accumulation of bi-directional observations over a short time period (16 days for the Collection V005 MODIS BRDF/Albedo product with 8 days overlap) provides a set of angular observations for BRDF retrieval. Depending on the number and distribution of clear looks and data quality, MODIS BRDF/albedo algorithm uses different strategies to retrieve the BRDF. The highest quality retrievals (main algorithm, full inversion) require at least seven clear looks with a well-distributed angular sampling [11], [24]. We selected the highest quality retrievals from MODIS BRDF/Albedo products to build BRDF LUMs. Since the MODIS BRDF/Albedo spatial resolution of 500 m is too coarse for crop applications at field scales, the MODIS BRDF parameters need to be extracted from large patches of pure and homogeneous land cover types. Pure and homogeneous land cover patches including both agricultural and natural classes were determined by the medium resolution CDL data (30–56 m). The CDL data were first reprojected to the MODIS sinusoidal projection. Each MODIS pixel (500 m) includes many CDL pixels (∼238 for 30-m resolution and ∼68 for 56-m resolution). The percentages of surface/crop types from the CDL within the MODIS spatial resolution were created. Only pixels recording a high percentage (>70%) of a single surface type were considered as candidate “pure” pixels and used in building the BRDF LUMs. The mean values of MODIS spectral BRDF parameters with the highest quality from the candidate pure pixels were obtained every 8 days on a spatial resolution of 1◦ × 1◦

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for major crops over the United States where the CDL is available. The BRDF parameters of various crops could be significantly different due to the differences in crop structure, leaf property, growing stage, and planting spatial arrangement. In this paper, we used crop type as a primary variable for BRDF archetypes. The same crop type generally has similar growth stages within a scene. However, differences may exist due to different planting dates, water availability, the local weather conditions or misclassification of crop types in the CDL. To consider these effects on surface BRDF, we also separated the MODIS pixels for each crop type into five NBAR normalized difference vegetation index (NDVI) levels (0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, and 0.8–1) to serve as a secondary variable in addition to the crop type in the BRDF LUM. In the most cases, the same crop type has close NDVI values and thus NDVI functions more like a filter to handle “outliers.” Therefore, in the end, the BRDF LUM is a function of location, surface or crop type, date, spectral band, and vegetation index. Nonagricultural classes in the CDL map were also included. The BRDF LUM was organized as a map of BRDF parameters so that additional spatial variations (e.g., crop types, ecological, and weather condition) could be considered. A global BRDF look-up table (LUT) averaged from LUMs was also maintained to fill the gaps where BRDF values were missing from a crop-specific LUM. We used a magnitude inversion approach (the backup algorithm in MODIS BRDF algorithm) [11] to normalize directional reflectance to nadir view using the BRDF LUMs and the CDL map from the same year. Since there is only a single directional reflectance for each date from the medium resolution sensor, we can easily compute the nadir BRDFadjusted reflectance (NBAR, ρ0,λ ) using ρ0,λ = Rc,λ (θv = 0, θs , φ)/Rc,λ (θv , θs , φ) × ρλ (θv , θs , φ) (1) where ρλ (θv , θs , φ) represents actual directional reflectance for band λ at viewing zenith (θv ), solar zenith angles (θs ), and relative azimuth angle (φ); Rc,λ (θv , θs , φ) represents the modeled directional reflectance using BRDF parameters from LUM for surface type c and band λ at same viewing and solar geometries; Rc,λ (θv = 0, θs , φ) is the modeled nadirview reflectance for the same surface type and band. The MODIS RossThick-LiSparse Reciprocal BRDF model is used to compute the modeled directional [Rc,λ (θv , θs , φ)] and nadir-view reflectances [Rc,λ (θv = 0, θs , φ)] in the following form: Rc,λ (θv , θs , φ) = fiso,c,λ + fvol,c,λ Kvol (θv , θs , φ) + fgeo,c,λ Kgeo (θv , θs , φ)

(2)

where Kvol and Kgeo are band-independent kernel functions that describe volumetric and geometric scattering components; and fiso , fvol , and fgeo [22] are the surface type and spectrally dependent BRDF parameters taken from the BRDF LUMs. III. R ESULTS A. View Angle Effect The view angle effect magnitude was evaluated by comparing the off-nadir and nadir reflectance using the 73 in situ

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TABLE II R ANGES (M IN AND M AX ) OF D IFFERENCE IN R EFLECTANCE (S CALE FACTOR : 0.01) B ETWEEN O FF -NADIR AND NADIR V IEW F ROM 73 BRDF I N SITU M EASUREMENTS (F IRST G ROUP ) FOR (A) R ED AND (B) NIR BAND

vzn, view zenith angle; szn, solar zenith angle.

measurements (first group) at different solar zenith angles. Table II summarizes the minimum and maximum values of differences between off-nadir view (−30◦ to 30◦ ) and nadir view reflectance at five different solar zenith angles (0◦ –60◦ ). Generally, with increasing solar zenith angle, the difference becomes more significant. From the 73 in situ measurements, the maximum absolute differences for Landsat (view zenith angle or vzn: ±8◦ ) are 0.022 for red band (bare field) and 0.027 for NIR band (roughest soil) both at solar zenith angle of 45◦ . For AWiFS sensor (vzn: ±30◦ ), the maximum absolute differences are 0.105 (bare field, szn = 45◦ ) and 0.138 (irrigated wheat, szn = 60◦ ) for red and NIR band, respectively. The angular effects of 10 selected in situ measurements (second group, Table I) are illustrated in Fig. 1. The differences between angular reflectance and nadir view reflectance (off nadir minus nadir) were computed for the principal plane at 45◦ of solar zenith angle. In Fig. 1, wheat values show stronger BRDF effects in both bands while corn measurements show weaker BRDF effects, which may be due to different crop conditions during the measurements. Corn was still in the early vegetative growth stage while wheat was near the peak vegetative growth stage, and thus volumetric scattering (bowlshape) dominated the wheat BRDF. Among natural vegetation types, shrub shows a stronger BRDF effect in the red band and aspen in the NIR band. Within a Landsat scene (vzn: ±8◦ ), the differences between off-nadir and nadir reflectance are in the range of ±0.01 and ±0.03 for red and NIR band, respectively (or ±15% and ±10% in relative to nadir view reflectance). Within a full AWiFS scene (vzn: ±30◦ ), the differences could be as high as 0.03 and 0.12 for red and NIR band, respectively (or 60% and 40% relatively). The median range of difference for a full AWiFS scene is within (−0.006, 0.014) for red band and within (−0.023, 0.054) for NIR band (or −10% to 25% relatively).

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adjacent simulated scenes are most obvious around the scene boundary and the mean absolute differences near the stitch line in Fig. 2 are about 0.003 for red band and 0.018 for NIR band. The mean relative difference in reflectance around the stitch line from the two Landsat scenes are about 7.7% and 5.1% for red and NIR band, respectively. B. Day of Year Effect

Fig. 1. Differences in reflectance (*0.01) between the off-nadir and nadir views for selected surface types (second group) in (a) red and (b) NIR spectral bands in the principal plane (solar zenith angle = 45◦ ) computed from BRDF models.

As shown in Fig. 1 and Table II, the forward (−30 to 0◦ ) and backward (0–30◦ ) scattering directions show opposite angular effects. In most cases, measurements from backward scattering see fewer shadows and show higher reflectance than forward scattering. Therefore, the most severe BRDF influence for Landsat/AWiFS scenes (with morning equatorial crossing times) can be observed when including observations from two adjacent, overlapping Landsat/AWiFS paths. In this case, the overlapped area is observed from two opposite view directions, one from forward and the other from the backward scattering direction. Fig. 2 shows the composite images before [Fig. 2(a)] and after [Fig. 2(b)] BRDF correction for a Landsat mosaic from two adjacent scenes (WRS-2 p12r31 and p13r31). In this example, we used MODIS BRDF parameters (collection 5, 500-m resolution) from one period (2005-193) to simulate the Landsat reflectance for two adjacent WRS-2 scenes. Reflectances for each Landsat scene were generated using the solar zenith angle and the simulated view zenith angle from Landsat. As the BRDF parameters come from the same time period, seasonal/phenology variations from two scenes can be neglected, thus the differences in the stitched image are mainly due to the view angle effect. As illustrated in Fig. 2, although the differences between two composite images [Figs. 2(a) and (b)] are small, the relative difference between simulated Landsat reflectances and nadir view reflectances for red [Fig. 2(c)] and NIR [Fig. 2(d)] band are obvious. The changes in the backward (p12r31) and forward (p13r31) scattering in the overlapped area magnified the influence of angular effects from positive to negative compared to the nadir view reflectance. The view angular effects from the two

The later Landsat series satellites (after Landsat-4) acquired images with a 16-day repeat cycle at approximately 10–11 A . M. local time. Thus solar zenith and azimuth angles vary through the year in Landsat time-series images. Fig. 3 shows the changes in solar zenith angle in 1 year from different latitudes (0◦ , 20◦ , 40◦ , and 60◦ ) in the Northern Hemisphere at 10 A . M. local time. In the middle latitudes (40◦ ), solar zenith angles vary from 30◦ (summer) to 70◦ (winter) at the Landsat acquisition time. To estimate the magnitude of the day of year BRDF effect, we computed nadir view reflectance for a whole year using fixed BRDF parameters (“frozen” land cover) using the specific solar geometry of each acquisition day, and then compared these values to the nadir view reflectance for the middle day of the year (day 183). Table III lists the range of differences in nadir view reflectance over a whole year from different latitudes. To ensure the quality of angular reflectance, we only included values obtained with solar zenith angles less than 75◦ . The maximum variability in reflectance associated with day of year generally increase with latitude and with shrub cover, showing the maximum variation in both red and NIR bands during a year. Among crops, soybean shows large BRDF effects in both bands, and wheat also shows large variation in the NIR band. However, corn was measured in the early growing stage and shows small variations from either view angle effect (Fig. 1) or the day of year effect. At high latitudes (50◦ N), the annual variation of reflectance due to day of year effect alone can be as large as 0.053 for red and 0.065 for NIR. In some extreme cases among the 73 data set collections, the maximum differences could be as large as 0.145 (corn from POLDER) for red and 0.220 (vine from POLDER) for NIR band, respectively. However, the POLDER measurements were collected from a narrow range of solar zenith angles and may have high uncertainties when used in evaluating solar angle or day of year effect. C. MLT Drift Effect Throughout the life of the satellite mission, the local acquisition time may vary depending on procedures for orbital station keeping. Fig. 4 shows the differences of solar zenith angle between the actual acquisition time and the nominal 10 A . M. local time for Landsat 5 for WRS-2 row 29 (about 45◦ N). From 1984 to 2010, the solar zenith angles for Landsat 5 varied from +2.5◦ to −5.5◦ . Although this change in solar angles has been accounted for in the radiometric calibration, the resultant BRDF effects have not. The BRDF effects caused by MLT drift alone were examined using three Landsat rows (WRS-2 rows 60, 29, and 18), which correspond to three different latitudes

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Fig. 2. (a) Landsat view and (b) nadir-adjusted view composite (NIR, red, and green composite) from two simulated adjacent Landsat scenes (p13r31 and p12r31) using MODIS BRDF product show very small differences visually. However, the relative differences ((off nadir−nadir)/nadir ∗ 100%) between the Landsat view and nadir view reflectance are more obvious for the overlapped area when pixels are viewed from two opposite view directions (backward for p12r31 and forward for p13r31) for (c) red and (d) NIR band.

TABLE III M AXIMUM A BSOLUTE D IFFERENCES OF NADIR V IEW R EFLECTANCE (S CALE FACTOR : 0.01) F ROM A W HOLE Y EAR C OMPARED TO THE DAY 183 (M IDDLE DAY OF Y EAR ) AT D IFFERENT L ATITUDES

Fig. 3. Annual variations of solar zenith angle from different latitude locations at Landsat acquisition time in northern hemisphere.

(0◦ , 45◦ N, and 60◦ N, approximately). The effects were evaluated by comparing nadir view reflectance at actual Landsat5 overpass times with those obtained using a fixed 10 A . M. local time assuming frozen BRDF parameters. The maximum differences for the selected surface types and for all 73 types are listed in Table IV. Across the Landsat-5 record, the year 1996 shows the strongest departure from the 10 A . M. reference (in agreement with Fig. 4). For all years from 1984 to 2010, the shrub type shows the highest variation of nadir view reflectance for both bands (about 0.015 for red and 0.018 for NIR, or 5.8% and 5.5% in relative differences). The maximum variations from all BRDF data sets are 0.047 for red band (corn from POLDER) and 0.063 for NIR band (vine from POLDER). The greater BRDF effects from POLDER airborne measurements are also observed in the Section III-B (day of year effect).

D. BRDF LUM for Crops The BRDF LUM was organized as a function of location, surface/crop type, date, spectral band, and vegetation index. Fig. 5 shows a BRDF LUM in near infra-red band for corn and soybeans during late June to early July for crops with a NDVI value between 0.8 and 1.0. The three BRDF parameters that characterize isotropic (fiso ), volumetric (fvol ), and geometric (fgeo ) scattering are shown in Fig. 5. Black represents the area

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Fig. 4. Variation of solar zenith angle between Landsat 5 acquisition time and the fixed 10 A . M. local time for Landsat WRS-2 row 29 (∼45◦ N). TABLE IV M AXIMUM D IFFERENCES (S CALE FACTOR : 0.01) OF NADIR V IEW R EFLECTANCE B ETWEEN ACTUAL L ANDSAT-5 ACQUISITION T IME AND R EFLECTANCE C ALCULATED AT THE F IXED 10 A . M . L OCAL T IME W ITHIN THE S ELECTED Y EARS AND THE A LL Y EARS C OMBINED F ROM 1984 TO 2011

that does not have BRDF values in the LUM due to the lack of enough “pure” and similar condition crop samples. Each valid BRDF parameter was also associated with a standard deviation of the BRDF parameter derived from the 1◦ cell. For each crop type, we generated 230 BRDF LUMs (46 MODIS 8-day production periods times five NBAR NDVI bins). Within each LUM, there were 21 layers (3 parameters × 7 spectral bands) that described the BRDF archetypes for this crop. E. BRDF Correction Example The accumulated BRDF archetypes in the BRDF LUMs were tested for correcting the view angle effect for an AWiFS image. In this example, we applied magnitude inversions to adjust the directional reflectances of the AWiFS image to a nadir view. Due to the lack of aerosol information

(there is no blue band in AWiFS [29]), atmospheric correction was not performed for the AWiFS image. The top-of-atmosphere (TOA) reflectance was used instead for demonstration. Fig. 6(a) shows an AWiFS TOA NIR band reflectance (p274r039D) in central Illinois, USA (central location: 40.75◦ N and 88.81◦ W) acquired in July 31, 2006. In the acquired area, around 60% of the land area was cropland. Since AWiFS is a push-broom system with two sensors covering the entire swath, the AWiFS D scene shown here has nadir view on its west edge and the view zenith angle increases to ∼25◦ on its east edge [29]. All pixels in this image come from forward scattering (solar and viewing directions come from opposite sides). After the BRDF correction [Fig. 6(b)], the reflectance generally increases. As shown before (Fig. 1), the reflectance from forward scattering is normally lower than the backward scattering since more surface shadowing can be observed from the forward scattering angles. Therefore, after correcting the directional reflectance to a nadir view, we generally see an increase of reflectance from the forward directions and a decrease of reflectance from the backward directions in the NBAR image. In this example, the magnitude of the difference before and after BRDF correction increases with the view zenith angle; it reaches about 0.03 in absolute reflectance value (abs diff = nadir−off nadir) [Fig. 6(c)] or about 10% relative to nadir reflectance (rel diff = abs diff/nadir ∗ 100%) [Fig. 6(d)] for the NIR band near the east edge of the image. The stripes (small variations) in the difference images (c) and (d) were caused by the simulated view zenith angles that were approximated in 1◦ steps. The effect of BRDF correction for a specific crop type (soybeans) was further examined and illustrated in Fig. 7. A scan line (west-east) profile of reflectance was extracted for both the original reflectance [Fig. 7(a)] and the NBAR [Fig. 7(b)]. We limited soybeans samples to those with similar growth conditions (NDVI = 0.80±0.05) in the plots so that we could expect similar reflectance values along the profile. Before applying the BRDF correction, the reflectance for soybeans in the original AWiFS image generally decreased with an increase of view zenith in forward scattering direction [Fig. 7(a)]—as we have already demonstrated in Figs. 1, 2, and 6. After applying the BRDF correction, the negative slope of the reflectance with view zenith was reduced from −0.0016 to −0.0004 per degree. The reflectance values after the BRDF correction were less dependent on the view zenith angle, which indicates the effectiveness of the proposed angular correction method in this study. IV. D ISCUSSION In this study, we evaluated the angular influence on reflectance associated with variability of three factors common to moderate-resolution imaging: view angle, solar angle, and sensor drift (overpass time). As we altered only one of the factors and fixed the other two, our results from three cases illustrate BRDF effects caused by that individual factor. The combined BRDF effects from the three factors may be raised or sometimes reduced depending on the satellite scene location and acquisition date. This study focused on evaluations of the

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Fig. 5. Illustration of the near-infrared BRDF LUM (three parameters) for (a) corn and (b) soybeans during the period June 26–July 11 (MODIS day of year production period: 177) for a specific range of NDVI (0.8–1.0) and at a 1◦ spatial resolution: (a1) fiso , corn; (a2) fvol , corn; (a3) fgeo , corn; (b1) fiso , soybeans; (b2) fvol , soybeans; and (b3) fgeo , soybeans.

Fig. 6. TOA reflectance of NIR band for an AWIFS image (central Illinois, p274r039D) obtained on July 31, 2006 (a) before [original (directional)] and (b) after BRDF correction [BRDF corrected (nadir)]. (c) Absolute difference (nadir minus directional reflectance; nadir-org.) and (d) the percentage of relative difference to nadir shows the increased reflectance toward the eastern edge after BRDF correction.

BRDF effects for especially the red and NIR spectral bands. It is worth noting that the angular influences on vegetation indices such as NDVI are smaller due to the cancellation of BRDF effects from red and NIR bands as observed in our previous study [30]. In all cases, these angular effects are partly governed by observation location. Identical surface cover types may display

Fig. 7. Scan line profiles of TOA reflectance in NIR band for soybeans from (a) the original directional AWiFS image and (b) the BRDF corrected nadirview reflectance image. All soybeans pixels in the plots have similar growth conditions with the NDVI values in the range of 0.75 to 0.85.

a different reflectance if observed at a different latitude as the solar zenith angles will vary between latitudes (location effect). As shown in Fig. 3, the solar zenith angle for different latitude locations (0◦ –60◦ ) may vary as much as 15◦ in summer and almost 50◦ in winter. The dynamic range of solar zenith angles for the same location from different days

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(the day of year effect) varies from 10◦ near the equator to over 40◦ near 60◦ latitude. Therefore, we can expect that the location BRDF effect would have a similar magnitude as the day of year effect. Fortunately, most applications (e.g., land cover classification) using medium resolution sensors use scene based processing, i.e., each Landsat scene is processed separately and the results are mosaiced afterward. Since Landsat scenes extend across about 1.5◦ in latitude, the assumption that a surface type at different locations within the scene has the same reflectance is acceptable for these applications. However, extra attention may be needed for processing large Landsat scene mosaics, as this assumption may not be true in these cases. Since crop fields are relatively small, we only used field and airborne measurements to evaluate angular effects for medium resolution sensors. We recognize that the BRDF samples used in this paper cannot represent all surface types and conditions, and the BRDF measurements themselves are not perfect in terms of accuracy and sampling distribution over the viewing and illumination hemisphere. Normally, angular effects are already considered when biophysical parameters are retrieved using physical remote sensing models. Many empirical models, however, assume Landsat is a nadir view sensor and neglect angular effects. Depending on the requirements and usage, some applications should consider angular effects in medium resolution remote sensing data processing. For example, the view angle effect may need to be corrected for mosaics from adjacent scenes even for “nadir-view” sensors like Landsat. Solar angle effects require attention when time-series images from multiple seasons are used. Since BRDF effects vary for different land surface types, some surface types such as shrubs, sparse forests, and horizontally heterogeneous agriculture may exhibit stronger angular effects and thus need more consideration. For land cover classifications, such as the CDL mapping, the BRDF effects may not be significant if training samples are uniformly available from the entire scene. However, more care is needed if training samples are not well distributed. For example, training samples collected from the backward looking direction (especially from the edge) may not be able to represent data features from the forward direction and this could lead to misclassification. There are several approaches available for correcting angular effects for medium resolution sensors. Each correction approach has its advantages and disadvantages. The pixelbased approach considers local BRDF variation, but may also imprint MODIS pixel boundaries in heterogeneous areas [18]. The regional BRDF approach is easier to implement but only suitable for a large and stable area [19]. The recently developed Landsat-MODIS concurrent approach by Shuai et al. [20] can provide BRDF information for major surface types in the study area and is appropriate for the Landsat scale, but is more complex and requires pure MODIS pixels (from large homogeneous area) in the study area during the specific time period. Our proposed crop angular correction approach relies on BRDF LUMs built from the historical MODIS BRDF product and the CDL classification map. This approach provides the BRDF information based on pure MODIS pixels

for various crops at different growth conditions. Though we expect the approach can correct BRDF effects effectively, the approach requires a crop type map beforehand which may be a challenge when applying to an area outside the United States. The BRDF LUMs include BRDF archetypes for all major crop types within each 1◦ cell. However, there are gaps due to the cloud contamination and the small patch sizes of crop fields. BRDF LUMs from multiple years could help to fill these gaps not only by increasing the number of clear observations from different years, but also by providing direct observation of crop rotation through different years. These advantages would increase the likelihood of building a complete BRDF LUM. A combination of the Landsat-MODIS concurrent approach [20] and the CDL approach may be feasible for operational applications. The BRDF archetypes for large agricultural fields can be extracted from the concurrent MODIS BRDF product. For small fields where BRDF archetypes cannot be extracted from the concurrent MODIS BRDF product, the historical BRDF LUMs could be used instead. A recent study shows that the global BRDF could be summarized into a few BRDF archetypes [28], which may suggest that our detailed crop BRDF LUM could include redundancy. Further investigation could reduce the data redundancy in the BRDF LUM and thus simplify the BRDF correction processing. V. C ONCLUSION The influence of angular effects on reflectance for medium resolution sensors depends on the sensor swath width, the image acquisition time, and applications. For Landsat-like narrow swath sensors, the major BRDF effect arises from the day of year effect, and can cause variations of 0.04–0.06 reflectance compared to mid-summer observations. Therefore, for applications that require image time series over different seasons, adjustments need to be made for changes in solar zenith angle. For AWiFS-like wide-swath sensors, both view angle effect and day of year effect can impact applications. When the variability of solar zenith angle during the growing season is small, the view angle effect becomes a major effect for agricultural applications, and can cause variations of about (−0.01, 0.02) for a red band and about (−0.03, 0.06) for a NIR band within a full AWiFS scene. The Landsat 5 MLT drift also caused observable variation in reflectance. The strongest deviation in MLT occurred in 1996, and is associated with a change in nadir view reflectance of about 0.01–0.02 for the red and NIR bands. The NBAR provides a consistent reflectance spatially and temporally. A BRDF LUM built on the MODIS BRDF product and USDA’s CDL crop-type classification provides a priori information for BRDF correction to support crop condition monitoring. R EFERENCES [1] C. Boryan, Z. Yang, R. Mueller, and M. Craig, “Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program,” Geocarto Int., vol. 25, no. 5, pp. 341–358, 2011, doi: 10.1080/10106049.2011.562309. [2] USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) [Online]. Available: http://www.nass.usda.gov/research/ Cropland/SARS1a.htm, accessed on Aug. 5, 2014.

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[3] S. N. Goward et al., “Complementarity of ResourceSat-1 AWiFS and Landsat TM/ETM+ sensors,” Remote Sens. Environ., vol. 123, pp. 41–56, 2012. [4] M. Wulder, J. G. Masek, W. B. Cohen, T. R. Loveland, and C. E. Woodcock, “Opening the archive: How free data has enabled the science and monitoring promise of Landsat,” Remote Sens. Environ., vol. 122, pp. 2–10, 2012. [5] D. W. Deering and A. Leone, “A sphere-scanning radiometer for rapid directional measurement of sky and ground radiance,” Remote Sens. Environ., vol. 19, pp. 1–24, 1986. [6] D. W. Deering, T. F. Eck, and T. Grier, “Shinnery oak bidirectional reflectance properties and canopy model inversion,” IEEE Trans. Geosci. Remote Sens., vol. 30, no. 2, pp. 339–348, Mar. 1992. [7] D. W. Deering et al., “Prairie grassland bidirectional reflectance measured by different instruments at the FIFE site,” J. Geophys. Res., vol. 97, pp. 18,887–18,903, 1992. [8] D. W. Deering, E. M. Middleton, and T. F. Eck, “Reflectance anisotropy for a spruce-hemlock forest canopy,” Remote Sens. Environ., vol. 47, pp. 242–260, 1994. [9] D. S. Kimes, “Dynamics of directional reflectance factor distribution for vegetation canopies,” Appl. Opt., vol. 22, pp. 1364–1372, 1983. [10] D. S. Kimes, W. W. Newcomb, R. F. Nelson, and J. B. Schutt, “Directional reflectance distributions of hardwood and pine forest canopy,” IEEE Trans. Geosci. Remote Sens., vol. 24, no. 2, pp. 281–293, Mar. 1986. [11] C. B. Schaaf et al., “First operational BRDF albedo nadir reflectance products from MODIS,” Remote Sens. Environ., vol. 83, pp. 135–148, 2002. [12] M. Leroy and F. M. Breon, “Surface reflectance angular signatures from airborne POLDER data,” Remote Sens. Environ., vol. 57, pp. 97–107, 1996. [13] M. Leroy et al., “Retrieval of atmosphere properties and surface bidirectional reflectance over land from POLDER ADEOS,” J. Geophys. Res., vol. 102, pp. 17023–17037, 1997. [14] P. Bicheron and M. Leroy, “Bidirectional reflectance distribution function signatures of major biomes observed from space,” J. Geophys. Res., vol. 105, no. D21, pp. 26,669–26,681, 2000. [15] D. J. Diner et al., “Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview,” IEEE Trans. Geosci. Remote Sens., vol. 36, no. 4, pp. 1072–1087, Jul. 1998. [16] M. Schlerf and C. Atzberger, “Vegetation structure retrieval in beech and spruce forests using spectrodirectional satellite data,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 1, pp. 8–17, Feb. 2012. [17] W. A. Dorigo, “Improving the robustness of cotton status characterisation by radiative transfer model inversion of multi-angular CHRIS/PROBA data,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 1, pp. 18–29, Feb. 2012. [18] D. P. Roy et al., “Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data,” Remote Sens. Environ., vol. 112, pp. 3112–3130, 2008. [19] F. Li et al., “An evaluation of the use of atmospheric and BRDF correction to standardize Landsat data,” IEEE J. Sel. Topics Appl. Earth Observ., vol. 3, no. 3, pp. 257–270, Sep. 2010. [20] Y. Shuai, J. G. Masek, F. Gao, and C. B. Schaaf, “An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF,” Remote Sens. Environ., vol. 115, pp. 2204–2216, 2011. [21] G. Schaepman-Strub, M. E. Schaepman, T. H. Painter, S. Dangel, and J. V. Martonchik, “Reflectance quantities in optical remote sensing—Definitions and case studies,” Remote Sens. Environ., vol. 103, pp. 27–42, 2006. [22] J. L. Roujean, M. Leroy, and P. Y. Deschamps, “A bidirectional reflectance model of the earth’s surface for the correction of remote sensing data,” J. Geophys. Res., vol. 97, no. D18, pp. 20,455–20,468, 1992. [23] W. Wanner, X. Li, and A. H. Strahler, “On the derivation of kernels and kernel-driven models of bidirectional reflectance,” J. Geophys. Res., vol. 100, no. D10, pp. 21077–21089, 1995. [24] C. Schaaf, J. Liu, F. Gao, and A. H. Strahler, “MODIS albedo and reflectance anisotropy products from Aqua and Terra,” in Land Remote Sensing and Global Environmental Change: NASA’s Earth Observing System and the Science of ASTER and MODIS, vol. 11, Remote Sensing and Digital Image Processing, B. Ramachandran, C. Justice, and M. Abrams, Eds. Berlin, Germany: Springer-Verlag, 2011, p. 873. [25] B. Hu, W. Lucht, X. Li, and A. H. Strahler, “Validation of kernel-driven models for the BRDF of land surfaces,” Remote Sens. Environ., vol. 62, pp. 201–214, 1997.

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[26] J. L. Privette, T. F. Eck, and D. W. Deering, “Estimating spectral albedo and nadir reflectance through inversion of simple BRDF models with AVHRR/MODIS-like data,” J. Geophys. Res., vol. 102, no. D24, pp. 29,529–29,542, 1997. [27] X. Li, F. Gao, J. Wang, and A. H. Strahler, “A priori knowledge accumulation and its application to constrain inversion of kernel-driven linear BRDF models,” J. Geophys. Res., vol. 106, no. D11, pp. 11,925– 11,936, 2001. [28] Z. Jiao et al., “An anisotropic flat index (AFX) to derive BRDF archetypes from MODIS,” Remote Sens. Environ., vol. 141, pp. 168– 187, 2014. [29] G. Chander, M. J. Coan, and P. L. Scaramuzza, “Evaluation and comparison of the IRS-P6 and the Landsat sensors,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 1, pp. 209–221, Jan. 2008. [30] F. Gao, Y. Jin, X. Li, C. B. Schaaf, and A. H. Strahler, “Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 6, pp. 1269– 1278, Jun. 2002.

Feng Gao received the B.A. degree in geology and the M.E. degree in remote sensing from Zhejiang University, Hangzhou, China, in 1989 and 1992, respectively, and the Ph.D. degree in geography from Beijing Normal University, Beijing, China and the M.S. degree in computer science from Boston University, Boston, MA, USA, in 1998 and 2003, respectively. He has held previous positions with Chinese Academy of Science, Beijing, China, Boston University, and the NASA Goddard Space Flight Center, Greenbelt, MD, USA, contracted through the Earth Resources Technology, Inc., Laurel, MD, USA. Since 2011, he has been a Research Scientist with the Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service (ARS), Beltsville MD, USA. His recent research interests include remote sensing modeling, multisensor data fusion, and vegetation biophysical parameter retrieving for crop and ecosystem condition monitoring. Dr. Gao has been a member of Landsat Science Team since 2006 and a member of MODIS Science Team since 2014.

Tao He received the B.E. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China and the Ph.D. degree in geography from the University of Maryland, College Park, MD, USA, in 2006 and 2012, respectively. He has been a Research Associate with the Department of Geographical Sciences, University of Maryland. His research interests include surface anisotropy and albedo modeling, data fusion of satellite products, and long-term regional and global surface radiation budget analysis.

Jeffrey G. Masek received the B.A. degree in geology from Haverford College, Haverford, PA, USA, and the Ph.D. degree in geological sciences from Cornell University, Ithaca, NY, USA, in 1989 and 1994, respectively. He is a Research Scientist with the Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA. He has held previous positions with the University of Maryland, College Park, MD, USA, Hughes Information Systems, and Cornell University. Since 2010, he has served as the NASA Landsat-7 Project Scientist. His research interests include mapping landcover change in temperate environments, application of remote sensing to terrestrial ecology, and satellite remote sensing techniques.

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Yanmin Shuai received the Ph.D. degree in geography and in remote sensing from Beijing Normal University, Beijing, China and Boston University, Boston, MA, USA, respectively, in 2008 and 2009, with support from the joint doctoral program between Beijing Normal University and Boston University. After graduation, she became a Research Scientist with the Earth Resources Technology Inc., Laurel, MD, USA, working on NASA GSFC projects. Her research interests include the development of routine direct broadcast anisotropic and radiative products for MODIS as well as approaches for Landsat albedo, the detection and monitoring of phenological events over agriculture and forested regions, and the radiative evolution of terrestrial ecosystems disturbed by fires, harvesting, and insect epidemics.

Crystal B. Schaaf (M’92) received the S.B. and S.M. degrees in meteorology from Massachusetts Institute of Technology, Cambridge, MA, USA, the M.L.A. degree in archaeology from Harvard University, Cambridge, MA, USA, and the Ph.D. degree in geography from Boston University, Boston, MA, USA, in 1982, 1988, and 1994, respectively. She is a Science Team Member working on the development of operational products from NASAs MODerate Resolution Imaging Spectrometer (MODIS) to monitor the Earths environments from the Terra and Aqua polar-orbiting space platforms. She is also a Science Team Member for the Landsat-8 satellite program and for the VIIRS (Visible Infrared Imaging Radiometer Suite) sensor on board the Suomi National Polarorbiting Partnership (NPP). Currently, she is a Professor in the Environmental, Earth and Ocean Sciences Department, University of Massachusetts, Boston, MA, USA. Her early research interests include the use of remote sensing in automated cloud analyses and the detection of initiating convective clouds. Her current research interests include modeling reflectance anisotropy and albedo and using remote sensing data to reconstruct and monitor the reflectance characteristics of various land surfaces, including vegetation phenology and land surface change. More recently, she has also been involved in the development and use of ground-based lidar systems to characterize biomass and vegetation structure.

Zhuosen Wang received the B.S. degree in geography from Beijing Normal University, China and the Ph.D. degree in geography from Boston University, Boston, MA, USA, in 2003 and 2011, respectively. He was a Research Fellow in the Environment, Earth, and Ocean Sciences Department, University of Massachusetts, Boston, MA, USA from 2012 to 2013 and has been a NASA Post Doctoral Fellow with NASA Goddard Space Flight Center, Greenbelt, MD, USA, since 2013. His research interests include monitoring the high-latitude snow melt and energy balance, modeling and evaluation of the anisotropic characteristics and albedo of the land surface, the monitoring of the vegetation phenology and climate change, canopy structure estimation using optical remote sensing and Lidar, carbon and energy cycle and fluxes simulation using a regional land surface model in coastal areas.