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NASA NTRS2018

Assessing Global Surface Water Inundation Dynamics Using Combined Satellite Information from SMAP, AMSR2 and Landsat

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw(sub LBand)) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency (Tb) observations from AMSR2. The resulting (fw(sub LBand)) global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The (fw(sub LBand)) annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly (fw(sub LBand)) averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable (fw(sub LBand)) performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) (fw(sub LBand)) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m (fw(sub LBand)) retrievals showed favourable spatial accuracy for water (70.71%) and land (98.99%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new (fw(sub LBand)) algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk.

WOD↗
NASA NTRS2018

Atmospheric Correction Inter-Comparison eXercise

The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate the experiment, the inter-comparison protocol, and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products, and sites, indicating their strengths and weaknesses. Indeed, this first implementation of processor inter-comparison was proven to be a good lesson for the developers to learn the advantages and limitations of their approaches. Various algorithm improvements are expected, if not already implemented, and the enhanced performances are yet to be assessed in future ACIX experiments.

Processors inter-comparison↗
NASA NTRS2018

Toward Long-Term Aquatic Science Products from Heritage Landsat Missions

This paper aims at generating a long-term consistent record of Landsat-derived remote sensing reflectance (Rrs) products, which are central for producing downstream aquatic science products (e.g., concentrations of total suspended solids). The products are derived from Landsat-5 and Landsat-7 observations leading to Landsat-8 era to enable retrospective analyses of inland and nearshore coastal waters. In doing so, the data processing was built into the SeaWiFS Data Analysis System (SeaDAS) followed by vicariously calibrating Landsat-7 and -5 data using reference in situ measurements and near-concurrent ocean color products, respectively. The derived Rrs products are then validated using (a) matchups using the Aerosol Robotic Network (AERONET) data measured by in situ radiometers, i.e., AERONET-OC, and (b) ocean color products at select sites in North America. Following the vicarious calibration adjustments, it is found that the overall biases in Rrs products are significantly reduced. The root-mean-square errors (RMSE), however, indicate noticeable uncertainties due to random and systematic noise. Long-term (since 1984) seasonal Rrs composites over 12 coastal and inland systems are further evaluated to explore the utility of Landsat archive processed via SeaDAS. With all the qualitative and quantitative assessments, it is concluded that with careful algorithm developments, it is possible to discern natural variability in historic water quality conditions using heritage Landsat missions. This requires the changes in Rrs exceed maximum expected uncertainties, i.e., 0.0015 [1/sr], estimated from mean RMSEs associated with the matchups and intercomparison analyses. It is also anticipated that Landsat-5 products will be less susceptible to uncertainties in turbid waters with Rrs(660) > 0.004 [1/sr], which is equivalent of ~1.2% reflectance. Overall, end-users may utilize heritage Rrs products with "fitness-for-purpose" concept in mind, i.e., products could be valuable for one application but may not be viable for another. Further research should be dedicated to enhancing atmospheric correction to account for non-negligible near-infrared reflectance in CDOM-rich and extremely turbid waters.

Pahlevan, Nima↗
NASA NTRS2019

The Legacy and Future of the International Earth Science Constellation (ESC)

The most recent Decadal Survey placed high value on continuing constellation science. The ESC has evolved by seeing new missions joining and old missions retiring. Most recently, GCOM-W1, Landsat-8, and OCO-2 joined during 2012-2014. Landsat-9 is set to join in 2020. Each new mission provides new and improved suite of sensors. The new sensors also benefit both from the multitude of other existing on-orbit sensors as well as from the long-term cross-calibrated climate observations from the sensors that preceded them. At the same time, existing missions leave the constellation due to low fuel reserves or aging spacecraft subsystems. For example, CloudSat and CALIPSO left the ESC orbits in 2018, although they plan to continue making coordinated science observations at their new lower altitudes. This ESC evolution is expected to continue and this paper will discuss the opportunities for other new missions to join the ESC.

afternoon constellation↗
NASA NTRS2018

Directional Reflectance Studies in Support of the Radiometric Calibration Test Site (RadCaTS) at Railroad Valley

The Radiometric Calibration Test Site (RadCaTS) is a suite of commercial and custom instruments used to make measurements of the surface reflectance and atmosphere throughout the day at Railroad Valley, Nevada. It was developed in response to the need for daily radiometric calibration data for the vast array of Earth-observing sensors on orbit, which is continuously increasing as more nations and private companies launch individual environmental satellites as well as large constellations. The current suite of instruments at RadCaTS includes five ground-viewing radiometers (GVRs), four of which view the surface in a nadir-viewing configuration. Many sensors such as those on Landsat-7 and Landsat-8 view Railroad Valley within 3° of nadir, while others such as those on Sentinel-2A and -2B, RapidEye, Aqua, Suomi NPP, and Terra can view Railroad Valley at off-nadir angles. Past efforts have shown that the surface bidirectional reflectance distribution function (BRDF) has minimal impact on vicarious calibration uncertainties for views <10°, but the desire to use larger view angles has prompted the effort to develop a BRDF correction for data from RadCaTS. The current work investigates the application of Railroad Valley BRDF data derived from a BRF camera developed at the University of Arizona in the 1990s (but is no longer in use) to the current RadCaTS surface reflectance measurements. Also investigated are early results from directional reflectance studies using a mobile spectro-goniometer system during a round-robin field campaign in 2018. This work describes the preliminary results, the effects on current measurements, and the approach for future measurements.

RadCalNet↗
NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗
NASA NTRS2019

Assessing the Use of SAR/Optical Data Fusion and TensorFlow for Improved Mangrove Mapping

Mangrove forests are found in intertidal zones of tropical regions around the world and provide important ecological and economic benefits – they are considered carbon sequesters, habitats for flora and fauna, and natural barriers to hurricanes and tsunamis. Wood from mangrove forests are used as fuel and building materials in surrounding coastal communities, therefore promoting local livelihoods. Despite the importance of these ecosystems, mangrove forests have historically been degraded in natural processes such as severe weather, and anthropogenic factors like conversion to agriculture and aquaculture. This study assesses change in mangrove forests in Nigeria and Mozambique from 2015 to 2018 using SAR and optical data fusion. Due to frequent cloud cover over the study area, SAR and optical data is fused to obtain gap-free imagery without clouds. Landsat-8 OLI and Sentinel-1 imagery is fused with TensorFlow, an open source platform used in developing machine learning models. The resulting images are classified to discriminate mangrove forest cover from other land cover types, and change is estimated using image differencing. Understanding the rates and magnitude of mangrove change across space and time can aid in identifying priority areas for forest regeneration, and can help construct sustainable management practices for the future.

Strattman, Katherine↗
NASA NTRS2021

A Method for Landsat and Sentinel 2 (HLS) BRDF Normalization

The Harmonized Landsat/Sentinel-2 (HLS) project aims to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2 remote sensing satellites. These satellites’ sampling characteristics provide nearly constant observation geometry and low illumination variation through the scene. However, the illumination variation throughout the year impacts the surface reflectance by producing higher values for low solar zenith angles and lower reflectance for large zenith angles. In this work, we present a model to derive the bidirectional reflectance distribution function (BRDF) normalization and apply it to the HLS product at 30m spatial resolution. It is based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (M{O,Y}D09) at 1 km spatial resolution using the VJB method (Vermote et al., 2009). Unsupervised classification (segmentation) of HLS images is used to disaggregate the BRDF parameters to the HLS spatial resolution and to build a BRDF parameters database at HLS scale. We first test the proposed BRDF normalization for different solar zenith angles over two homogeneous sites, in particular one desert and one Peruvian Amazon forest. The proposed method reduces both the correlation with the solar zenith angle and the coefficient of variation (CV) of the reflectance time series in the red and near infrared bands to 4% in forest and keeps a low CV of 3% to 4% for the deserts. Additionally, we assess the impact of the view zenith angle (VZA) in an area of the Brazilian Amazon forest close to the equator, where impact of the angular variation is stronger because it occurs in the principal plane. The directional reflectance shows a strong dependency with the VZA. The current HLS BRDF correction reduces this dependency but still shows an under-correction, especially in the near infrared, while the proposed method shows no dependency with the view angles. We also evaluate the BRDF parameters using field surface albedo measurements as a reference over seven different sites of the US surface radiation budget observing network (SURFRAD) and five sites of the Australian OzFlux network.

Belen Franch↗
NASA NTRS2020

Landsat 9: Empowering Open Science and Applications Through Continuity

The history of Earth observation from space is well reflected through the Landsat program. With data collection beginning with Landsat-1 in 1972, the program has evolved technical capabilities while maintaining continuity of land observations. In so doing, Landsat has provided a critical reference for assessing long-term changes to Earth's land environment due to both natural and human forcing. Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. Landsat-9 will maintain the current data acquisition rate of up to 740 scenes per day, with these scenes available from the Landsat archive at no cost to users. In this communication, we provide background and rationale for the Landsat 9 mission, describe the instrument payloads and ground system, and discuss data products available from the Landsat 9 mission through USGS.

Jeffrey G. Masek↗
NASA NTRS2020

Satellite and airborne remote sensing of gross primary productivity in boreal Alaskan lakes

In terrestrial and marine ecosystems, remote sensing has been used to estimate gross primary productivity (GPP) for decades, but few applications exist for shallow freshwater ecosystems.Here we show field-based GPP correlates with satellite and airborne lake color across a range of optically and limnologically diverse lakes in interior Alaska. A strong relationship between in situ GPP derived from stable oxygen isotopes (δ18O) and space-based lake color from satellites (e.g. Landsat-8, Sentinel-2 and CubeSats) and airborne imagery (AVIRIS-NG) demonstrates the potential power of this technique for improving spatial and temporal monitoring of lake GPP when coupled with additional field validation measurements across different systems. In shallow waters clear enough for sunlight to reach lake bottoms, both submerged vegetation (macrophytes and algae) and phytoplankton likely contribute to GPP. The stable isotopes and remotely sensed shallow lake color used here integrate both components. These results demonstrate the utility of lake color as a feasible means for mapping lake GPP from remote sensing. This novel methodology estimates GPP from remote sensing in shallow lakes by combining field measurements of oxygen isotopes with airborne, satellite and CubeSat imagery. This use of lake color for providing insight into ecological processes of shallow lakes is recommended, especially for remote arctic and boreal landscapes.

airborne remote sensing↗
NASA NTRSDate not supplied

Thermal Performance Comparison and Lessons Learned for the Thermal Infrared Sensor Instruments 1 & 2

The Thermal Infrared Sensor (TIRS-1) is one of two instruments on the Landsat-8 Mission, which launched in February of 2013 and remains operational. The TIRS-2 instrument was developed for the Landsat-9 Mission, which is scheduled to launch in September of 2021. The TIRS-2 instrument design was adjusted to account for requirements changes and to include key lessons learned from its predecessor. The overall thermal subsystem design of the TIRS Sensor Unit remained comparable from mission to mission, but there were some areas that needed modifications. The general design is comprised of five thermal zones which range in temperature from less than 43 Kelvin to 320 Kelvin. Most zones are proportional heater controlled. A two-stage cryocooler provided by Ball Aerospace is used to cool the cryogenic subsystem, and excess power dissipation is rejected via ammonia transport heat pipes to a dedicated cryocooler radiator with embedded ammonia heat pipes. The cryogenic subsystem includes a series of shells used to radiatively and conductively isolate the cold stage from the warmer surroundings. The Optical System (telescope) is passively cooled to 180-195 Kelvin using a dedicated radiator with embedded dual-bore ethane heat pipes. The warmer end of the instrument includes a Scene Select Mechanism, an on-board Black Body Calibrator with a dedicated radiator, and a Focal Plane Electronics Box, all of which are attached to the Sensor Unit primary structure. The TIRS-1 thermal subsystem design was successfully verified during an extended testing campaign and during more than 8years of on-orbit operations. The TIRS-2 thermal subsystem design was successfully verified during months of ground testing prior to delivery in August of 2019and at the Observatory level in the Spring of 2021. A detailed comparison of the thermal performance of the two instruments has been made with a focus on key lessons learned during each instrument development, and with the benefit of on-orbit data acquired for the TIRS-1 instrument.

Veronica Otero↗
NASA NTRS2020

ICESat-2 Meltwater Depth Estimates: Application to Surface Melt on Amery Ice Shelf, East Antarctica

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of meltwater stored has been difficult to quantify due to a lack of accurate depth estimates. NASA’s ICESat-2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat-2 sampled Amery Ice Shelf on 2 January 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat-2 and two from coincident Landsat-8 and Sentinel-2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat-2 produced the most accurate depths; the image-based algorithms underestimated depths (by 30-70%). This implies that ICESat-2 depths can be used to tune image-based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland.

ICESat-2↗
NASA NTRS2021

Combining Satellite Data and Spatial Analysis to Assess the UHI Amplitude and Structure within Urban Areas: The Case of Moroccan Cities

Landsat-8 surface temperature and the European Space Agency land cover are used to assess the impact of land cover on the Urban Heat Island (UHI) and Urban Heat Sink (UHS). We analyzed five Moroccan cities selected for their different local climate, size, and typology during summer at three different spatial scales. The results show multiple causes defining the different forms and amplitudes of the UHI, namely: the ambient climate, the proximity to the sea, the presence of landscaped areas, and the color of building roofs and walls. Contrary to what was expected, the vegetation was not systematically an island of coolness, either because of its typology or its irrigation status. In the coastal cities of Tangier and Casablanca, UHIs around 20°C are observed on the seaside, whereas a UHS of up to 11°C is observed between the city center and the southern periphery of Casablanca. A moderate amplitude UHI of 7°C is formed in the mountainous city of Ifrane. For cities built in desert-like environments, well-defined UHSs between 9°C and 12°C are observed in Smara and Marrakech, respectively. At a finer scale, towns recorded lower temperatures than their immediate surroundings, which are attributed to evaporation from irrigated plants.

Laila El Ghazouani↗
NASA NTRS2020

SNPP and NOAA-20 VIIRS On-Orbit Geolocation Trending and Improvements

Two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors have been in operations for more than 8.5 and 2.5 years since they were launched in October 2011 on SNPP satellite and in November 2017 on NOAA-20 satellite, respectively. These are two satellites in the Join Polar Satellite System (JPSS) constellation, of which Suomi National Polar-orbiting Partnership (SNPP) is a risk reduction satellite and NOAA-20 is the first of four JPSS satellites(JPSS-1 became NOAA-20 after launch). Accurate geolocation is a critical element in data calibration for accurate retrieval of global biogeophysical parameters. In this paper, we describe the latest trends in the continuously improved geolocation accuracy in VIIRS Collection-1 (C1) and C2 re-processing. We implemented a VIIRS instrument geometric model update (VIGMU)for both sensors that correct for geolocation error oscilations in the scan direction. We borrowed code from Moderate Resolution Imaging Spectroradiometer (MODIS) geolocation software to correct for time-dependent pointing variations, that are particularly acute in NOAA-20 VIIRS, and some pointing anomalies in SNPP VIIRS. We developed a Kalman Filter using gyrodata to correct for attitude errors due to the degradation of the star trackers performance from the SNPP satellite. We also present an improved ground control point matching (CPM) tool, in which the ground control point (GCP) chips library is refreshed using recently launched Landsat-8 images.

SNPP↗
NASA NTRSDate not supplied

Generative Framework Approach to Match Landsat and Sentinel-2 Data

Linear regression and histogram matching based techniques have been widely used to minimize the surface reflectance difference between two similar satellite observations such as Landsat-8/9 and Sentinel-2A/B products [1]. However, regionally or globally derived conversion factors may not be suitable for all land cover types and locations, resulting in noticeable residual differences between the sensors. Generative Adversarial Network (GAN) has shown promise in the field of image processing for domain or style transfer[2]. In this work we aim to minimize the surface reflectance difference between Landsat and Sentinel-2 products based on GAN.

Sujit Roy↗
NASA NTRSDate not supplied

Quantitative Assessment of the HLS Surface Reflectance Consistency

The Harmonized Landsat and Sentinel-2 (HLS) project produces compatible surface reflectance (SR) from observations acquired by Landsat-8/9 OLI and Sentinel-2A/2B MSI. The HLS harmonization procedures include atmosphere correction, cloud masking, view angle normalization, and bandpass adjustment. The objective of this study is to quantitatively assess the reflectance consistency between Landsat and Sentinel-2 within the Version 2 HLS data. We collected 545 pairs of same-day Landsat/Sentinel-2 images across the globe to represent a wide range of vegetation types and climate regimes. The mean absolute difference (MAD) in reflectance between Landat and Sentinel-2 was calculated as a consistency indicator for each harmonization step. The MAD generally increased after the atmosphere correction, and then greatly decreased after the BRDF and bandpass adjustments, to smaller than the top-of-atmosphere MAD values. The MAD ranged from 0.0048 to 0.0093 for the six common spectral bands (blue, green, red, NIR, SWIR1, and SWIR2) in the final products, only slightly greater than the difference between Landsat and Sentinel-2 calibrations.. An evaluation on a few commonly used vegetation indices also showed good agreement between Landsat and Sentinel-2 reflectance. All these evaluations demonstrate that the HLS project produces a consistent SR dataset from Landsat-OLI and Sentinel-MSI, which will be a valuable resource for a wide range of remote sensing applications.

Qiang Zhou↗
NASA NTRS2024

Prelaunch Spectral Characterization of the Operational Land Imager-2

The Landsat-9 satellite, launched in September 2021, carries the Operational Land Imager-2 (OLI-2) as one of its payloads. This instrument is a clone of the Landsat-8 OLI and its mission is to continue the operational land imaging of the Landsat program. The OLI-2 instrument is not significantly different from OLI though the instrument-level pre-launch spectral characterization process was much improved. The focal plane modules used on OLI-2 were manufactured as spares for OLI and much of the spectral characterization of the components was performed for OLI. However, while the spectral response of the fully assembled OLI was characterized by a double monochromator system, the OLI-2 spectral characterization made use of the Goddard Laser for Absolute Measurement of Radiance (GLAMR). GLAMR is a system of tunable lasers that cover 350–2500 nm which are fiber-coupled to a 30 in integrating sphere permanently monitored by NIST-traceable radiometers. GLAMR allowed the spectral characterization of every detector of the OLI-2 focal plane in nominal imaging conditions. The spectral performance of the OLI-2 was, in general, much better than requirements. The final relative spectral responses (RSRs) represent the best characterization any Landsat instrument spectral response. This paper will cover the results of the spectral characterization from the component-level to the instrument-level of the Landsat-9 OLI-2.

Landsat-9↗
NASA NTRS2023

Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges

Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra () from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, -fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

algal blooms↗