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NASA NTRS2018

Investigations of Hail Damage Swaths Using Various Satellite Remote Sensing Platforms

Severe thunderstorms that bring damaging winds and large hail can cause significant damage to agricultural crops. Severe thunderstorms can cause upwards of several hundreds of millions of dollars in damage to agricultural areas. Formal ground surveys are not conducted on these areas of damage, like they are for suspected tornado damaged areas. IF ground surveys were conducted, they would likely be time and resources consuming due to their large spatial extent. Satellite remote sensing has been frequently used in identification and analysis of these hail damage swaths. Previous analysis have looked at the simple change in damaged vegetation to looking at the damage areas in satellite imagery with varying spatial resolutions. One study has even looked at the impacts that these damage swaths can have on the land surface, associated fluxes and how they affect numerical weather prediction. Previous studies have focused on using optical remote (VIS, NIR, SWIR) sensing instruments and derived indices, such as Normalized Difference Vegetation Index (NDVI) for analysis. NDVI is used to monitor the health (greenness) of the vegetation. Optical sensors however are limited by sky conditions over the areas they are imaging and certain bands are further limited by the diurnal cycle. These limitations can lead to sometimes upwards of 7 to 10 day gaps of the surface not being imaged, especially during the height of summer convection. One way to obtain more views of the surface, regardless of the sky conditions or time of day is through the use of synthetic aperture radar (SAR). SAR sensors are active instruments that transmit in the microwave portion of the EM spectrum. The surface and its characteristics will determine the amount of energy scattered back to the sensor. The SAR sensors then measure amplitude and phase of wavelength coming back from surface.

Bell, Jordan R.↗
NASA NTRS2019

Updated MISR Over-Water Research Aerosol Retrieval Algorithm - Part 2: A Multi-Angle Aerosol Retrieval Algorithm for Shallow, Turbid, Oligotrophic, and Eutrophic Waters

Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources, and many are the sites for upwelling of nutrient rich, deep water; they are also some of the most biologically productive on Earth. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., chlorophyll alpha concentration) to in situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the "atmospheric correction" needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (R (sub rs)) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity and turbidity index (PTI), calculated from retrieved spectral R (sub rs), that distinguished water types (similar to the the normalized difference vegetation index, NDVI, over land). We also validate the new algorithm by comparing spectral AOD and Angstrom exponent (ANG) results with 2419 collocated AErosol RObotic NETwork (AERONET) observations. For AERONET 558 nanometer-interpolated AOD less than 1.0, the root-mean square error (RMSE) is 0.04 and linear correlation coefficient is 0.95. For the 502 cloud-free MISR and AERONET collocations with an AERONET AOD greater than 0.20, the ANG RMSE is 0.25 and r is 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, the MISR-RA-retrieved Angstrom exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sunglint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view-angle retrieval algorithms struggle to separate atmospheric from oceanic features.

Satellite Remote Sensing↗
NASA NTRS2018

A Comparative Study of Available Water in the Major River Basins of the World

Numerous large river basins of the world have few and irregular observations of the components of the terrestrial hydrological cycle with the exception of stream gauges at a few locations and at the outlet along with sparsely distributed rain gauges. Using observations from satellite sensors and output from global land surface models, it is possible to study these under-observed river basins. With populations greater than a billion people, some of these rivers (e.g., the Ganga-Brahmaputra, the Yangtze, the Nile and the Mekong) are the economic engines of the countries they transect, yet thorough assessment of their flow dynamics and variability in regard to water resource management is still lacking. In this paper, we use soil moisture (0-2m) and surface runoff from the NASA Global Land Data Assimilation System (GLDAS), evapotranspiration, and Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and rainfall from the Tropical Rainfall Measuring Mission (TRMM) and total water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) to examine variability of individual water balance components. To this end, understanding the inter-annual and intra-seasonal variability and the spatial variability of the water balance components in the major river basins of the world will help to plan for improved management of water resources for the future.

GLDAS↗
NASA NTRS2019

Improving the AVHRR Long Term Data Record BRDF Correction

The Long Term Data Record (LTDR) project has the goal of developing a quality and consistent surface reflectance product from coarse resolution optical sensors. This paper focuses on the Advanced Very High Resolution Radiometer (AVHRR) part of the record, using the Moderate Resolution Imaging Spectrometer (MODIS) instrument as a reference. When a surface reflectance time series is acquired from satellites with variable observation geometry, the directional variation generates an apparent noise which can be corrected by modeling the bidirectional reflectance distribution function (BRDF). The VJB (Vermote, Justice and Bréon, 2009) method estimates a target’s BRDF shape using 5 years of observation and corrects for directional effects maintaining the high temporal resolution of the measurement using the instantaneous Normalized Difference Vegetation Index (NDVI). The method was originally established on MODIS data but its viability and optimization for AVHRR data have not been fully explored. In this study we analyze different approaches to find the most robust way of applying the VJB correction to AVHRR data, considering that high noise in the red band (B1) caused by atmospheric effect makes the VJB method unstable. Firstly, our results show that for coarse spatial resolution, where the vegetation dynamics of the target don’t change significantly, deriving BRDF parameters from 15+ years of observations reduces the average noise by up to 7% in the Near Infrared (NIR) band and 6% in the NDVI, in comparison to using 3-year windows. Secondly, we find that the VJB method can be modified for AVHRR data to improve the robustness of the correction parameters and decrease the noise by an extra 8% and 9% in the red and NIR bands with respect to using the classical VJB inversion. We do this by using the Stable method, which obtains the volumetric BRDF parameter (V) based on its NDVI dependency, and then obtains the geometric BRDF parameter (R) through the inversion of just one parameter.

LTDR↗
NASA NTRS2019

Diurnal and Seasonal Solar Induced Chlorophyll Fluorescence and Photosynthesis in a Boreal Scots Pine Canopy

Solar induced chlorophyll fluorescence has been shown to be increasingly an useful proxy for the estimation of gross primary productivity (GPP), at a range of spatial scales. Here, we explore the seasonality in a continuous time series of canopy solar induced fluorescence (hereafter SiF) and its relation to canopy gross primary production (GPP), canopy light use efficiency (LUE), and direct estimates of leaf level photochemical efficiency in an evergreen canopy. SiF was calculated using infilling in two bands from the incoming and reflected radiance using a pair of Ocean Optics USB2000+ spectrometers operated in a dual field of view mode, sampling at a 30 min time step using custom written automated software, from early spring through until autumn in 2011. The optical system was mounted on a tower of 18 m height adjacent to an eddy covariance system, to observe a boreal forest ecosystem dominated by Scots pine. (Pinus sylvestris) A Walz MONITORING-PAM, multi fluorimeter system, was simultaneously mounted within the canopy adjacent to the footprint sampled by the optical system. Following correction of the SiF data for O2 and structural effects, SiF, SiF yield, LUE, the photochemicsl reflectance index (PRI), and the normalized difference vegetation index (NDVI) exhibited a seasonal pattern that followed GPP sampled by the eddy covariance system. Due to the complexities of solar azimuth and zenith angle (SZA) over the season on the SiF signal, correlations between SiF, SiF yield, GPP, and LUE were assessed on SZA <50° and under strictly clear sky conditions. Correlations found, even under these screened scenarios, resulted around ~r2 = 0.3. The diurnal responses of SiF, SiF yield, PAM estimates of effective quantum yield (ΔF/Fm′), and meteorological parameters demonstrated some agreement over the diurnal cycle. The challenges inherent in SiF retrievals in boreal evergreen ecosystems are discussed.

photosynthetic↗
NASA NTRS2019

Changes in Vegetation Cover of the Arctic National Wildlife Refuge Estimated from MODIS Greenness Trends, 2000 to 2018

The Arctic National Wildlife Refuge (ANWR) was established in 1980 and covers 19 million acres (77,000 km2) in northeast Alaska. Wildlife habitats in the ANWR are vulnerable to long-lasting effects from any disturbance, in part because short growing seasons in the arctic provide limited time for species to recover. Trends and transitions in the growing season MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in all ecoregions of the ANWR. Statistical analysis of changes in each MODIS pixel NDVI time series was conducted using the "Breaks for Additive Seasonal and Trend" method (BFAST) to map regional change rates. Results suggested that most negative NDVI anomalies in the tundra-covered river drainages of the Brooks Range Mountains and coastal plain have been associated with early spring thawing and elevated levels of surface moisture in low elevation drainages of the northern ANWR ecoregions.

Arctic National Wildlife Refuge↗
NASA NTRS2019

Changes in Vegetation Cover of Yukon River Drainages in Interior Alaska Estimated from MODIS Greenness Trends, 2000 to 2018

Trends and transitions in the growing season MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in ecosystems of the Yukon River basin in interior Alaska. Statistical analysis of changes in the NDVI time series was conducted using the "Breaks for Additive Seasonal and Trend" method (BFAST). This structural change analysis indicated that NDVI breakpoints and negative 18-yr trends in vegetation greenness over the years since 2000 could be explained in large part by the impacts of severe wildfires, commonly affecting shrubland and forested ecosystems at relatively low elevations (< 300 m).

Potter, Christopher↗
NASA NTRS2018

Multi-Sensor Integration of Vegetation Index Products for Long-Term Monitoring of Vegetation Dynamics: A Case Study from MODIS to VIIRS

Spectral vegetation index (VI) time series data from moderate resolution sensors, such as Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), have widely been used to identify "hot spot" areas of vegetation changes and to characterize long-term trends of vegetation changes. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor series of the Joint Polar Satellite System (JPSS) program is slated to continue the highly calibrated data stream initiated with MODIS. The first VIIRS sensor has operationally been acquiring Earth-reflected radiation since March 2012. The second VIIRS sensor (NOAA-20) was launched in November 2017, which is to continue the measurements made by the first VIIRS sensor. In this study, we examined an integrated use of MODIS and VIIRS VI time series data on capturing vegetation dynamics in the Asia-Pacific region using their overlapped period of observations (2013-2017). Three VIs, the "top-of-canopy (TOC)" normalized difference vegetation index (NDVI), TOC enhanced vegetation index (EVI), and TOC two-band enhanced vegetation index (EVI2), were investigated. For all the three VIs, VIIRS VIs were systematically higher than the MODIS counterparts due mainly to their spectral bandpass differences. However, both VIIRS and MODIS VIs showed the comparable spatial patterns in their temporal variations. Empirical spectral corrections allowed to merge the two data streams, which slightly improved the temporal resolution of the VI temporal profiles. These results suggest the suitability of VIIRS data to extend and merge into the MODIS VI record for long-term vegetation dynamics studies.

MODIS↗
NASA NTRS2019

Evaluating the Operational Application of SMAP for Global Agricultural Drought Monitoring

Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regionalagricultural drought monitoring is one of the most logicalapplication areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimetersof the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entireroot zone. The assimilation of remotely sensed soil moisture productsinto continuous soil water balance models provides a way ofaddressing this shortcoming. Here, we describe the assimilationof NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impactof SMAP on USDA FAS drought monitoring capabilities. Theassimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for randomerrors inherent in its rainfall forcing data. The performanceof this SMAP-based assimilation system is evaluated using two approaches.At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moistureand the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ-based soil moisture estimatesis carried out at seven of the SMAP core Cal/Val sites located in theUSA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.

Mladenova, Iliana E.↗
NASA NTRS2019

Downscaling and Validation of SMAP Radiometer Soil Moisture in CONUS

The SMAP (Soil Moisture Active/Passive) satellite provides global soil moisture (SM) estimates that can be used for scientific research and applications (such as the hydrological cycle, agriculture, ecology, and land atmosphere interactions). Currently, SMAP provides the enhanced radiometer-only SM product (L2SMP) at 9 km grid resolution. However, this spatial resolution is still not enough to satisfy the needs of some studies that require a finer spatial resolution SM product, particularly in agricultural and watershed applications. This study applied a downscaling algorithm to the SMAP 9 km SM product to produce a 1 km resolution over the CONUS (Contiguous United States). The downscaling algorithm is based on the relationship between temperature change and SM modulated by Normalized Difference Vegetation Index (NDVI) of a given time period. This relationship was modeled using variables derived from NLDAS (North America Land Data Assimilation System) and NASA's LTDR (Land Long Term Data Record) between 1981-2018. The algorithm was implemented uses the 1 km MODIS Aqua LST (Land Surface Temperature) product. The downscaled SMAP 1 km SM was validated using in situ SM measurements from the ISMN (International Soil Moisture Network). The validation metrics show an improved overall accuracy of the downscaled SM.

downscaling↗
NASA NTRS2019

The Use of Landsat 8 and Sentinel-2 Data and Meterological Observations for Winter Wheat Yield Assessment

This study focuses on winter wheat yield assessment from NASA's Harmonized Landsat Sentinel-2 (HLS) product and meteorological observations through phenological fitting. Vegetation indices (VIs), namely difference vegetation index (DVI), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI2), extracted from satellite optical data, are fitted per pixel against accumulated growing degree days (AGDD) using a quadratic function. Accumulated VIs are correlated against winter wheat yields. Results show a better performance from DVI compared to NDVI and EVI2.

Skakun, S.↗
NASA NTRS2015

Reductions in Productivity Due to Land Degradation in the Drylands of the Southwestern United States

Dryland degradation has long been recognized at regional, national, and global scales, yet there are no objective assessments of its location and severity. An assessment of reductions in net primary production (NPP) due to dryland degradation in the southwestern United States is reported. The local NPP scaling (LNS) approach was applied to map the extent and magnitude of degradation. LNS seeks to identify reference sites in which there is no degradation that can be used as a standard for comparison with other sites that share the same environment, except for degradation. Twelve years were analyzed (2000–2011), using Normalized Difference Vegetation Index (NDVI) data (250 m) from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite‐borne multispectral sensor. The results indicated that the total NPP reductions in the study area were about 35.9 ± 4.7 Tg C/yr, which equates to 0.31 ± 0.04 Mg C/ha·y. The NPP reductions in grassland‐savanna and livestock grazing areas were large and mostly consistent between years in spite of large variations in overall NPP caused by differences in land‐use, interannual variations in rainfall, and other aspects of weather. In comparison with other cover types, forested land generally had higher NPP reduction per unit area. The maps also enable attribution of degradation from the finest management units to entire agencies, such as the Bureau of Land Management, which had 50% less production per unit area than the U.S. Forest Service. The degradation within Native American land was low with total NPP reduction of about 2.41 ± 0.24 Tg C/yr and unit area reduction of productivity of just 0.21 ± 0.02 Mg C/ha·yr, yet the percent reduction from potential was in equivalence with other land management agencies.

Noojipady, Praveen↗
NASA NTRS2020

TPSAS-NF1676L-36113-DND

Concerns about the effects of extreme heat and poor air quality are increasing in North America’s largest urban centers. In Philadelphia, environmental and public health groups are concerned about how these phenomena disproportionality affect marginalized communities and populations, which often have extensive impervious surfaces and little access to green space. In order to address these concerns, the Philadelphia Department of Public Health and the Office of Sustainability seek to effectively prioritize cooling initiatives to reduce urban heat and decrease air pollutants. We evaluated land surface temperature (LST) and the Normalized Difference Vegetation Index (NDVI), as a measure of overall greenness, obtained from NASA Earth observations Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). These analyses we recombined with local tree inventory, air quality, and socioeconomic data through a multivariate analysis to identify areas where new trees or cooling adaptations are most needed. The results and data of this project can be used by our partners to inform both short-term heat relief planning and a long-term, multi-agency heat response.

Spencer Nelson↗
NASA NTRSDate not supplied

TPSAS-NF1676L-13211-DND

On March 11, 2011 the Great Tohoku Earthquake occurred approximately 70 kilometers off the coast of Japan. This magnitude 9.0 earthquake was closely followed by a massive tsunami that reached 7 meters in height. Using NASA Earth Observation Systems (EOS), we will assess the damage to the area impacted by this disaster. Landsat 5 Thematic Mapper (TM) will be used to create normalized difference vegetation index (NDVI) maps and MODIS on Aqua and Terra will be used to produce enhanced vegetation index (EVI) maps. CALIPSO and HYSPLIT will also be used to track smoke released from the Fukushima Daiichi Nuclear Power Plant to assess where the radioactive steam traveled to. An analysis of the economic impact of this disaster will be conducted to help policy makers in the United States and in Japan. This research will highlight the ability of NASA EOS in providing support for disaster response around the world.

Malcom Jones, Jr.↗
NASA NTRS2020

Widespread Mangrove Damage Resulting from the 2017 Atlantic Mega Hurricane Season

Comprised of 17 named tropical storms, 6 of which were major hurricanes, the 2017 Atlantic hurricane season ranked as one of the most damaging and costly hurricane seasons on record. In addition to socio-economic impacts, many previous studies have shown that important coastal ecosystems like mangroves are shaped by severe storms. However, little is known about how the cumulative effects of storms over entire hurricane seasons affect mangroves across large regions. We used satellite imagery from the entire Caribbean and Gulf of Mexico region to show that 2017 resulted in disproportionate mangrove damage compared to baseline responses over the previous 8 years. Specifically, we observed 30 times more mangrove damage, via a reduction in the normalized difference vegetation index (NDVI), during 2017 compared to any of the eight previous hurricane seasons, and most (72%) of this damage persisted throughout the 7 month post-hurricane season period as indicated by no NDVI recovery. Furthermore, wind speed, rainfall, and canopy height data showed that mangrove damage primarily resulted from high maximum wind speeds, but flooding (cumulative rainfall), previous storm history, and mangrove structure (canopy height) were also important predictors of damage. While mangroves are known to be resilient to hurricane impacts, our results suggest that increasingly frequent mega-hurricane seasons in the Caribbean region will dramatically alter mangrove disturbance dynamics.

Temilola Elisabeth Fatoyinbo Agueh↗
NASA NTRS2020

Changes in Vegetation Phenology and Productivity in Alaska Over the Past Two Decades

Understanding trends in vegetation phenology and growing season productivity at a regional scale is important for global change studies, particularly as linkages can be made between climate shifts and the vegetation’s potential to sequester or release carbon into the atmosphere. Trends and geographic patterns of change in vegetation growth and phenology from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the state of Alaska over the period 2000 to 2018. Phenology metrics derived from the MODIS Normalized Difference Vegetation Index (NDVI) time-series at 250 m resolution tracked changes in the total integrated greenness cover (TIN), maximum annual NDVI (MAXN), and start of the season timing (SOST) date over the past two decades. SOST trends showed significantly earlier seasonal vegetation greening (at more than one day per year) across the northeastern Brooks Range Mountains, on the Yukon-Kuskokwim coastal plain, and in the southern coastal areas of Alaska. TIN and MAXN have increased significantly across the western Arctic Coastal Plain and within the perimeters of most large wildfires of the Interior boreal region that burned since the year 2000, whereas TIN and MAXN have decreased notably in watersheds of Bristol Bay and in the Cook Inlet lowlands of southwestern Alaska, in the same regions where earlier-trending SOST was also detected. Mapping results from this MODIS time-series analysis have identified a new database of localized study locations across Alaska where vegetation phenology has recently shifted notably, and where land cover types and ecosystem processes could be changing rapidly.

Vegetation↗
NASA NTRS2020

DEVELOP Poster: Louisville Urban Development

Greenness and land surface temperature (LST) have been extensively correlated to urban public health. Chronic diseases such as diabetes, asthma, and cardiovascular illnesses have been linked to regions of high LST and areas lacking urban green spaces. One of our partners, the University of Louisville Envirome Institute, champions the importance of urban green spaces. Their research has pioneered efforts in increasing the area and accessibility of urban parks and greenspaces as a way to improve public health and combat environmental inequality. Louisville, Kentucky, is currently working to plant and manage urban trees. Our team used NASA Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery to calculate the Normalized Difference Vegetation Index (NDVI) in Louisville at the census tract level and automated the process for use in other cities. The New York University School of Medicine’s City Health Dashboard displays public health metrics across 500 US cities and will use our methods to complete the NDVI analysis for all of these locations plus an additional 293 cities they are adding to their dashboard in the near future. Our NDVI and LST calculations will allow cites across the US to make informed decisions about reducing environmental inequality by focusing on areas with low NDVI and high LST.

Urban Development↗
NASA NTRS2020

Evaluation and Validation of a High Spatial Resolution Satellite Soil Moisture Product over the Continental United States

The soil moisture (SM) data retrieved from the Soil Moisture Active and Passive (SMAP) satellite are available at a 9 km grid spacing since April 2015. This product can provide valuable information for research and applications in hydrology and other related fields. However, the resolution may be too coarse for applications at catchment or field scale. In this study, an established downscaling methodology, which had a major modification regarding its application on the SMAP 33 km domain, was implemented to develop a 1 km soil moisture product based on the SMAP 9 km data. The algorithm proposed here is based on the thermal inertia principle and developed by modeling the relationship between surface temperature difference and SM for different Normalized Difference Vegetation Index (NDVI) classes. The model functions were established and tuned using data from the NASA’s Land Information System (LIS) North America Land Data Assimilation System (NLDAS) and remotely sensed VISible/InfRared (VIS/IR) reflectance data from Long Term Data Record (LTDR) AVHRR (Advanced Very High Resolution Radiometer) for the growing season months of April-September 1981–2018. These were then implemented using the MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Continental United States (CONUS) domain. Validation activities were carried out using in situ measurements distributed through the International Soil Moisture Network (ISMN). The validation results computed using the 1 km SM data showed that the R2, unbiased RMSE (root mean square error) and bias were improved relative to the 9 km SMAP product by 0.045, 0.018m3/m3 and 0.001m3/m3, respectively. The 1 km SM also exhibited a strong time-series autocorrelation. Further accuracy assessment analyses indicated that precipitation might contribute to the uncertainties in both the 9 km SMAP and 1 km downscaled SMAP SM products.

SMAP↗