Harmonized Landsat/Sentinel-2 Products for Land Monitoring

The Harmonized Landsat-8 and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a seamless, harmonized surface reflectance record from the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat-8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, geographic co-registration and common gridding, bidirectional reflectance distribution function normalization and bandpass adjustment. As of version 1.3, the HLS v1.3 data set covers 9.12 million km2 and spans from first Landsat-8 data (2013) to present. HLS products provide near-daily surface reflectance information with a common geometric framework, and are suitable for a variety of agricultural and vegetation monitoring tasks, including analysis of crop type, condition, and phenology

The Sentinel-2 MSI Can Increase the Temporal Resolution of 30m Satellite-Derived LAI Estimates

The successful launch of the European Space Agency (ESA) Sentinel-2A (S2-A) on 23 June 2015 with its MultiSpectral Instrument (MSI) provides an important means to augment Earth-observation capabilities following the legacy of Landsat. After the three-month satellite commissioning campaign, the MSI onboard S-2A is performing very well (ESA, 2015). By 3 December 2015, the sensor data records have achieved provisional maturity status and have been accessed in level-1C Top-Of-Atmosphere (TOA) reflectance by the remote sensing community worldwide. Near-nadir observations by the MSI onboard S-2A and the Operational Land Imager (OLI) onboard Landsat 8 were collected during Simultaneous Nadir Overpasses as well as nearly coincident overpasses. This paper presents a processing chain using harmonized S-2A MSI and Landsat 8 OLI sensors to obtain increased temporal resolution in Leaf Area Index (LAI) estimates using the red-edge band B8A of MSI to replace the NIR band B08. Results demonstrate that LAI estimates from the MSI and OLI are comparable, and, given sufficient preprocessing for atmospheric correction and geometric rectification, can be used interchangeably to improve the frequency with which low LAI canopies can be monitored

Harmonized Landsat/Sentinel-2 Reflectance Products for Land Monitoring (Invited)

Many land applications require more frequent observations than can be obtained from a single 'Landsat class' sensor. Agricultural monitoring, inland water quality assessment, stand-scale phenology, and numerous other applications all require near-daily imagery at better than 1ha resolution. Thus the land science community has begun expressing a desire for a '30-meter MODIS' global monitoring capability. One cost-effective way to achieve this goal is via merging data from multiple, international observatories into a single virtual constellation. The Harmonized Landsat/Sentinel-2 (HLS) project has been working to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2. Harmonization in this context requires a series of radiometric and geometric transforms to create a single surface reflectance time series agnostic to sensor origin. Radiometric corrections include a common atmospheric correction using the Landsat-8 LaSRC/6S approach, a simple BRDF adjustment to constant solar and nadir view angle, and spectral bandpass adjustments to fit the Landsat-8 OLI reference. Data are then resampled to a consistent 30m UTM grid, using the Sentinel-2 global tile system. Cloud and shadow masking are also implemented. Quality assurance (QA) involves comparison of the output 30m HLS products with near-simultaneous MODIS nadir-adjusted observations. Prototoype HLS products have been processed for approximately 7% of the global land area using the NASA Earth Exchange (NEX) compute environment at NASA Ames, and can be downloaded from the HLS web site (https://hls.gsfc.nasa.gov). A wall-to-wall North America data set is being prepared for 2018. This talk will review the objectives and status of the HLS project, and illustrate applications of high-density optical time series data for agriculture and ecology. We also discuss lessons learned from HLS in the general context of implementing virtual constellations

A General Method to Normalize Landsat Reflectance Data to Nadir BRDF Adjusted Reflectance

The Landsat satellites have been providing spectacular imagery of the Earth\u27s surface for over 40 years. However, they acquire images at view angles ±7.5° from nadir that cause small directional effects in the surface reflectance. There are also variations with solar zenith angle over the year that can cause apparent change in reflectance even if the surface properties remain constant. When Landsat data from adjoining paths, or from long time series are used, a model of the surface anisotropy is required to adjust all Landsat observations to a uniform nadir view (primarily for visual consistency, vegetation monitoring, or detection of subtle surface changes). Here a generalized approach is developed to provide consistent view angle corrections across the Landsat archive. While this approach is not applicable for generation of Landsat surface albedo, which requires a full characterization of the surface bidirectional reflectance distribution function (BRDF), or for correction to a constant solar illumination angle across a wide range of sun angles, it provides Landsat nadir BRDF-adjusted reflectance (NBAR) for a range of terrestrial monitoring applications. The Landsat NBAR is derived as the product of the observed Landsat reflectance and the ratio of the reflectances modeled using MODIS BRDF spectral model parameters for the observed Landsat and for a nadir view and fixed solar zenith geometry. In this study, a total of 567 conterminous United States (CONUS) January and July 2010 Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+) images that have swath edge overlapping paths sensed in alternating backscatter and forward scattering orientations were used. The average difference between Landsat 5 TM and Landsat 7 ETM+ surface reflectance in the forward and backward scatter directions at the overlapping Landsat scan edges was quantified. The CONUS July view zenith BRDF effects were about 0.02 in the Landsat visible bands, and about 0.03, 0.05 and 0.06, in the 2.1 μm, 1.6 μm and nearinfrared bands respectively. Comparisons of Landsat 5 TM and Landsat 7 ETM+ NBAR derived using MODIS BRDF spectral model parameters defined with respect to different spatial and temporal scales, and defined with respect to different land cover types, were undertaken. The results suggest that, because the BRDF shapes of different terrestrial surfaces are sufficiently similar over the narrow 15° Landsat field of view, a fixed set of MODIS BRDF spectral model parameters may be adequate for Landsat NBAR derivation with little sensitivity to the land cover type, condition, or surface disturbance. A fixed set of BRDF spectral model parameters, derived from a global year of highest quality snow-free MODIS BRDF product values, are provided so users may implement the described Landsat NBAR generation method

Continental-scale validation of MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods

The potential of Landsat data processing to provide systematic continental scale products has been demonstrated by several projects including the NASA Web-enabled Landsat Data (WELD) project. The recent free availability of Landsat data increases the need for robust and efficient atmospheric correction algorithms applicable to large volume Landsat data sets. This paper compares the accuracy of two Landsat atmospheric correction methods: a MODIS-based method and the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) method. Both methods are based on the 6SV radiative transfer code but have different atmospheric characterization approaches. The MODIS-based method uses the MODIS Terra derived dynamic aerosol type, aerosol optical thickness, and water vapor to atmospherically correct ETM+ acquisitions in each coincident orbit. The LEDAPS method uses aerosol characterizations derived independently from each Landsat acquisition and assumes a fixed continental aerosol type and uses ancillary water vapor. Validation results are presented comparing ETM+ atmospherically corrected data generated using these two methods with AERONET corrected ETM+ data for 95 10 km×10 km 30 m subsets, a total of nearly 8 million 30 m pixels, located across the conterminous United States. The results indicate that the MODIS-based method has better accuracy than the LEDAPS method for the ETM+ red and longer wavelength bands

The Suitability of Multi-temporal Web-enabled Landsat Data NDVI for Phenological Monitoring – a Comparison with Flux Tower and MODIS NDVI

Three years of flux-tower-derived normalized difference vegetation index (NDVI) data were compared with contemporaneous 30 m web-enabled Landsat data (WELD) and with 500 m Moderate-Resolution Imaging Spectroradiometer (MODIS) nadir bidirectional reflectance distribution function-adjusted reflectance (NBAR) NDVI data to assess the relative suitability of these different resolutions of freely available satellite data for phenological monitoring. Comparisons were made at two flux tower sites in the United States with average to above average cloud cover. The WELD 30 m NDVI data were found to have higher correlation with the flux tower NDVI data than the MODIS 500 m NBAR NDVI data. The dates of vegetation green-up onset and maximum-greenness onset, derived using an established phenological metric extraction methodology, were generally closer between the flux tower and WELD NDVI data than between the flux tower and MODIS NBAR data. These results indicate that the WELD NDVI time series is suitable for 30 m scale phenological monitoring

Development of an Approach for Generation of Temporally Complete Daily Nadir MODIS Reflectance Time Series

Consistent, spatially and temporally complete reflectance time series are required for reliable terrestrial monitoring. The Moderate Resolution Imaging Spectroradiometer (MODIS), like other polar-orbiting wide field of view satellite sensors, can provide global observations on a nearly daily basis, but the sparseness of valid observations due to cloud, residual atmospheric effects, and sensor anomalies, may result in gaps in the derived reflectance time series. This paper presents an approach for the generation of temporally complete daily MODIS 500 m nadir view BRDF-adjusted reflectance (NBAR) time series. The research is illustrated and assessed quantitatively using two years of cloud and snow screened, daily MODIS Terra and Aqua reflectance data at four sites in Africa, and demonstrated for phenology monitoring using NBAR derived NDVI time series. The components of the approach include: 1) an outlier detection algorithm to remove residual anomalous daily observations undetected in the upstream processing, 2) the dynamic generation of NBAR time series on a daily basis when seven or more observations are available for a day under consideration over a 16-day period, and 3) the means to gap fill the NBAR time series where less than 7 observations are available. The MODIS Ross-Thick/Li-Sparse-Reciprocal BRDF model is used with a rolling approach whereby a 16-day BRDF inversion window is moved on a daily overlapping basis to provide more reliable outlier detection and daily NBAR. NBAR gap filling in periods of missing observations is investigated using static land cover specific archetype BRDF parameters and using BRDF parameters defined adaptively from the temporally closest 16-day periods with 7 or more observations. Scaling factor estimators using ordinary least squares (OLS) and median-based robust least squares regression are investigated, and the robust method is demonstrated to provide on average temporally more coherent gap filled NBAR values. For regions with persistent clouds, the utility of the adaptive NBAR gap filling method is demonstrated to be severely limited due to the decreased likelihood that the surface BRDF at each gap can be described reliably. The reliability of the NBAR gap filling methodology is evaluated statistically using a cross-validation approach. For the small number of study site considered, the adaptive method is shown to provide more accurate results than the archetype method when there are more than an average of ~4–5 observations per 16-day window, or when a gap day is on average less than about 30 days from a 16-day period with 7 or more observations. The resulting gap free daily NBAR time series and derived daily NBAR NDVI generated by the approach is shown to capture phenological variations in a coherent temporally consistent manner, suggesting that it is a fruitful avenue for future research and validation

Continental-Scale Validation of MODIS-Based and LEDAPS Landsat ETM+ Atmospheric Corrections Methods

The potential of Landsat data processing to provide systematic continental scale products has been demonstrated by several projects including the NASA Web-enabled Landsat Data (WELD) project. The recent free availability of Landsat data increases the need for robust and efficient atmospheric correction algorithms applicable to large volume Landsat data sets. This paper compares the accuracy of two Landsat atmospheric correction methods: a MODIS-based method and the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) method. Both methods are based on the 6SV radiative transfer code but have different atmospheric characterization approaches. The MODIS-based method uses the MODIS Terra derived dynamic aerosol type, aerosol optical thickness, and water vapor to atmospherically correct ETM+ acquisitions in each coincident orbit. The LEDAPS method uses aerosol characterizations derived independently from each Landsat acquisition and assumes a fixed continental aerosol type and uses ancillary water vapor. Validation results are presented comparing ETM+ atmospherically corrected data generated using these two methods with AERONET corrected ETM+ data for 95 10 km × 10 km 30 m subsets, a total of nearly 8 million 30 m pixels, located across the conterminous United States. The results indicate that the MODIS-based method has better accuracy than the LEDAPS method for the ETM+ red and longer wavelength bands