Satellite Pixel Research Articles

An Evaluation of Autonomous In Situ Temperature Loggers in a Coastal Region of the Eastern Mediterranean Sea for Use in the Validation of Near-Shore Satellite Sea Surface Temperature Measurements

The coastal ocean is one of the most important environments on our planet, home to some of the most bio-diverse and productive ecosystems and providing key input to the livelihood of the majority of human society. It is also a highly dynamic and sensitive environment, particularly susceptible to damage from anthropogenic influences such as pollution and over-exploitation as well as the effects of climate change. These have the added potential to exacerbate other anthropogenic effects and the recent change in sea temperature can be considered as the most pervasive and severe cause of impact in coastal ecosystems worldwide. In addition to open ocean measurements, satellite observations of sea surface temperature (SST) have the potential to provide accurate synoptic coverage of this essential climate variable for the near-shore coastal ocean. However, this potential has not been fully realized, mainly because of a lack of reliable in situ validation data, and the contamination of near-shore measurements by the land. The underwater biotechnological park of Crete (UBPC) has been taking near surface temperature readings autonomously since 2014. Therefore, this study investigated the potential for this infrastructure to be used to validate SST measurements of the near-shore coastal ocean. A comparison between in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra SST data is presented for a four year (2014–2018) in situ time series recorded from the UBPC. For matchups between in situ and satellite SST data, only nighttime in situ extrapolated to the sea surface (SSTskin) data within ±1 h from the satellite’s overpass are selected and averaged. A close correlation between the in situ data and the MODIS SST was found (squared Pearson correlation coefficient-r2 > 0.9689, mean absolute error-Δ < 0.51 both for Aqua and Terra products). Moreover, close correlation was found between the satellite data and their adjacent satellite pixel’s data further from the shore (r2 > 0.9945, Δ < 0.23 for both Aqua and Terra products, daytime and nighttime satellite SST). However, there was also a consistent positive systematic difference in the satellite against satellite mean biases indicating a thermal adjacency effect from the land (e.g., mean bias between daytime Aqua satellite SST from the UBPC cell minus the respective adjacent cell’s data is δ = 0.02). Nevertheless, if improvements are made in the in situ sensors and their calibration and uncertainty evaluation, these initial results indicate that near-shore autonomous coastal underwater temperature arrays, such as the one at UBPC, could in the future provide valuable in situ data for the validation of satellite coastal SST measurements.

Monitoring Mega-Crown Leaf Turnover from Space

Spatial and temporal patterns of tropical leaf renewal are poorly understood and poorly parameterized in modern Earth System Models due to lack of data. Remote sensing has great potential for sampling leaf phenology across tropical landscapes but until now has been impeded by lack of ground-truthing, cloudiness, poor spatial resolution, and the cryptic nature of incremental leaf turnover in many tropical plants. To our knowledge, satellite data have never been used to monitor individual crown leaf phenology in the tropics, an innovation that would be a major breakthrough for individual and species-level ecology and improve climate change predictions for the tropics. In this paper, we assessed whether satellite data can detect leaf turnover for individual trees using ground observations of a candidate tropical tree species, Moabi (Baillonella toxisperma), which has a mega-crown visible from space. We identified and delineated Moabi crowns at Lopé NP, Gabon from satellite imagery using ground coordinates and extracted high spatial and temporal resolution, optical, and synthetic-aperture radar (SAR) timeseries data for each tree. We normalized these data relative to the surrounding forest canopy and combined them with concurrent monthly crown observations of new, mature, and senescent leaves recorded from the ground. We analyzed the relationship between satellite and ground observations using generalized linear mixed models (GLMMs). Ground observations of leaf turnover were significantly correlated with optical indices derived from Sentinel-2 optical data (the normalized difference vegetation index and the green leaf index), but not with SAR data derived from Sentinel-1. We demonstrate, perhaps for the first time, how the leaf phenology of individual large-canopied tropical trees can directly influence the spectral signature of satellite pixels through time. Additionally, while the level of uncertainty in our model predictions is still very high, we believe this study shows that we are near the threshold for orbital monitoring of individual crowns within tropical forests, even in challenging locations, such as cloudy Gabon. Further technical advances in remote sensing instruments into the spatial and temporal scales relevant to organismal biological processes will unlock great potential to improve our understanding of the Earth system.

Generation of chilling hours maps using surface observations and satellite data

Abstract In complex terrain, the topography influences the air temperature near the surface since cold air pooling and thermally-generated winds might respectively enhance or diminish the nocturnal cooling rate. The characterization of the temperature patterns in agricultural areas is important because many crops need a certain amount of time under cold temperatures. This is usually given by the Chilling Hours (CH, number of hours below a certain temperature threshold). In this work the number of CH has been estimated in the island of Mallorca using in-situ and satellite Meteosat data for the period 2008–2018, taking a threshold value of 7 °C, applying corrections for missing data in both cases, including cloudiness for the satellite data. CH values per year obtained are small for stations near or at the coast (CH below 400) and large at mountain tops (CH above 1500), whereas at the foothills and central plain the values vary between 500 and 1000. Cold events contributing to the number of CH happen mostly between December and March and they may be related to cold synoptic advection (with clear or cloudy skies) or mostly to clear nights with weak local winds that may end in late night fog. Satellite-derived CH maps indicate that the coldest areas are located in the central lowlands between the two major mountain ranges and the elevated area in the center of the island, where cold air pools are usually found. Significant differences between in-situ and satellite CH may exist due to the topographical local features compared to a surface-averaged value from the satellite pixel. Furthermore, existing CH parameterizations making use of daily extreme temperatures are checked and adjusted coefficients are proposed.

Detecting nighttime fire combustion phase by hybrid application of visible and infrared radiation from Suomi NPP VIIRS

An accurate estimation of biomass burning emissions is partially limited by the lack of knowledge of fire burning phase (smoldering vs. flaming). In recent years, several fire detection products have been developed to provide information of fire radiative power (FRP), location, size, and temperature of fire pixels, but no information regarding fire burning phase is retrieved. The Day-Night band (DNB) aboard Visible Infrared Imaging Radiometer Suite (VIIRS) is sensitive to visible light from flaming fires in nighttime scenes. In contrast, VIIRS 4 μm moderate resolution band #13 (M13), though capable of detecting fires at all phases, has no direct sensitivity for discerning fire phase. However, the hybrid usage of VIIRS DNB and M-bands data is hampered by their different scanning technology and spatial resolution. In this study, we present a novel method to rapidly and accurately resample DNB pixel radiances to the footprint of M-band pixels, accounting for onboard detector aggregation schemes and bowtie effect removals. The visible energy fraction (VEF) is subsequently introduced as an indicator of fire burning phase. VEF is calculated as the ratio of visible light power (VLP) to FRP for each fire pixel retrieved from the VIIRS 750 m active fire product. A global distribution of VEF values is quantitatively obtained, showing smaller VEF values in regions with mostly smoldering wildfires, such as peatland fires in Indonesia, larger VEF values in regions with flaming wildfires over grasslands and savannas in the sub-Sahelian region, and the largest VEF values associated with gas flaring in the Middle East. Mean VEF for different land cover types or regions is highly correlated with modified combustion efficiency (MCE). These results, together with a case study of the 2018 California Camp Fire, show that the VEF has the potential to be an indicator of fire combustion phase for each fire pixel, appropriate for estimating emission factors at the satellite pixel level.

Investigation of wildfire impacts on land surface phenology from MODIS time series in the western US forests

Land surface phenology (LSP) characterizes the timing and greenness of seasonal vegetation growth in satellite pixels and it has been widely used to associate with climate change. However, wildfire, causing considerable land surface changes, exerts abrupt changes on the LSP magnitudes and great influences on the LSP long-term trends, which are poorly investigated. This study for the first time conducted a systematic analysis of the wildfire impacts on LSP by investigating 838 forest wildfires occurred from 2002 to 2014 across the western United States. Specifically, we derived three LSP timing metrics that are the start (SOS), end (EOS), and length (LOS) of growing season and two LSP greenness metrics that are seasonal greenness maximum (GMax) and minimum (GMin) from daily time series of 250-m MODIS two-band enhanced vegetation index (EVI2) during 2001–2015. Burned area and burn severity were obtained from the Monitoring Trends in Burn Severity project. The results showed GMax and GMin were decreased at an extent of 0.063 and 0.074 EVI2, respectively. LSP timings presented diverse responses to wildfire occurrences. Absolute abrupt shift of >2 days in SOS appeared in 73% of burned areas with 40% advances and 33% delays, the shift in EOS occurred in 80% of burned areas with 33% advances and 47% delays, and the shift in LOS occurred in 85% of the burned areas with 36% shortening and 49% lengthening. Moreover, the LSP changes were significantly influenced by burn severity with the largest impact on LSP timing at the moderate burn severity and on LSP greenness at the high burn severity. Finally, the phenological trends from 2001 to 2015 differed significantly between burned and unburned reference areas and the trend difference varied with the wildfire occurrence year. Overall, this study demonstrated that wildfires exert complex and diverse impacts on LSP timing and greenness metrics and significantly influence LSP trends associating with climate change. The approach developed in this study provides a prototype to investigate LSP responses to other land disturbances associated with natural processes and human activities on the landscape.

Urban air temperature model using GOES-16 LST and a diurnal regressive neural network algorithm

An urban air temperature model is presented using the enterprise GOES-16 land surface temperature product. The model is constructed by fitting the difference between ground-truth air temperature data against satellite LST using a Gaussian function. A time-match algorithm aligns the ground and satellite measurements within 5-min of one another, and the resulting matched values are compared over ten months to investigate their correlation. Land cover, latitude, longitude, local time, and elevation are input to a regressive neural network to fit each unique GOES-16 pixel according to ground-based properties. Over 150 ground stations and satellite pixels throughout the continental U.S. are used near urban areas to construct the diurnal Gaussian relationship and approximate air temperature. Statistics from a five month validation period generates an RMSE of 2.6 K, a bias of 0.8 K, and R2 of 0.86, which are in strong competition with other studies at lower resolution, less geographic integration, and less temporal resolvability. The algorithm also produced strong spatial correlations with a high resolution numerical model, resulting in a mean RMSE value of 2.1 K for nearly 7000 pixels. The overall presentation of this model aims to simplify the calculation of air temperature from satellite LST and create a successful model that performs well in heterogeneous environments. The improvement of urban air temperature calculations will also result in improved satellite land surface products such as relative humidity and heat index.

Assessment of absorbing aerosols on austral spring snow albedo reduction by several basins in the Central Andes of Chile from daily satellite observations (2000–2016) and a case study with the WRF-Chem model

Changes in snow albedo (SA) on the Limari, Choapa, Aconcagua and Maipo basins of the Central Andes of Chile (CAC) are associated with the possible deposition of light-absorbing particles in the austral spring. We correlate SA with daily data of snow cover, aerosol optical depth (AOD) and land surface temperature (LST) available from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the NASA Terra satellite between 2000 and 2016, and other derived parameters such as days after albedo (DAS) and snow precipitation (SP). We used satellite pixels with 100% snow cover to obtain monthly average value of SA, LST, AOD, DAS and SP from September to November performing multiple regression analysis. We show that in Maipo, after considering LST, AOD represents an important role in changes induced to SA. The multiple regression model illustrates that AOD increases can reduce the SA during spring months by 13.59, 0.01, 0.77 and 3.8% in Limari, Choapa, Aconcagua and Maipo, respectively. In addition, we used a numerical prediction Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), showing that the black carbon distribution and average daily AOD are associated with the SA decrease of 0.15 in the Maipo basin between September 29 and 30, 2016. The WRF-Chem output showed aerosols are transported mainly with dominating westerly winds to the Limari and Maipo basins. Our results further suggest that SA decrease due to AOD may be originated in the largest industrial and urban areas in Chile, producing a negative impact on the hydrological resource, generated in the CAC.

Sensitivity of Simulated GMI Brightness Temperatures to Variations in Particle Size Distributions in a Severe Hailstorm

AbstractGlobal Precipitation Measurement (GPM) Microwave Imager (GMI) brightness temperatures (BTs) were simulated over a case of severe convection in Texas using ground-based S-band radar and the Atmospheric Radiative Transfer Simulator. The median particle diameter Do of a normalized gamma distribution was varied for different hydrometeor types under the constraint of fixed radar reflectivity to better understand how simulated GMI BTs respond to changing particle size distribution parameters. In addition, simulations were conducted to assess how low BTs may be expected to reach from realistic (although extreme) particle sizes or concentrations. Results indicate that increasing Do for cloud ice, graupel, and/or hail leads to warmer BTs (i.e., weaker scattering signature) at various frequencies. Channels at 166.0 and 183.31 ± 7 GHz are most sensitive to changing Do of cloud ice, channels at ≥89.0 GHz are most sensitive to changing Do of graupel, and at 18.7 and 36.5 GHz they show the greatest sensitivity to hail Do. Simulations contrasting BTs above high concentrations of small (0.5-cm diameter) and low concentrations of large (20-cm diameter) hailstones distributed evenly across a satellite pixel showed much greater scattering using the higher concentration of smaller hailstones with BTs as low as ~110, ~33, ~22, ~46, ~100, and ~106 K at 10.65, 18.7, 36.5, 89.0, 166.0, and 183.31 ± 7 GHz, respectively. These results suggest that number concentration is more important for scattering than particle size given a constant S-band radar reflectivity.

Aggregation of area-averaged evapotranspiration over the Ejina Oasis based on a flux matrix and footprint analysis

The determination of area-averaged evapotranspiration (ET) over a heterogeneous land surface at the satellite pixel scale/model grid scale (103–104 m) is crucial to the evaluation of the remote sensing ET products and development of the parameterization schemes of general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) flux matrix of multi-site eddy-covariance (EC) and large aperture scintillometers (LASs) provided an unprecedented opportunity to establish an aggregation scheme for determining area-averaged surface fluxes. Based on the HiWATER flux matrix datasets and high-resolution land-cover map, a new flux aggregation method was developed by using footprint analysis and multiple-linear regression. The area-averaged sensible heat fluxes aggregated from the multi-site EC flux measurements using the developed method were compared with the LAS measured fluxes. The results showed that the new method produced reasonable estimates of sensible heat flux with a mean absolute percentage error (MAPE) value of around 20–30%. The new aggregation method also significantly outperformed other methods being applied commonly, such as the rather simple area-weighted method. The daily ET of various land-cover types and the area-averaged ET over the key area of the Populus euphratica forest reserve in the Ejina Oasis were separately estimated using the established disaggregation and aggregation schemes of the new method. The area-averaged ET over this key area ranged between 1 and 5 mm d−1 for the 68 days growing period in 2014. Results of this study can be used for the evaluation of relevant remote sensing models and land surface process models.

Impacts of Partly Cloudy Pixels on Shortwave Broadband Irradiance Computations

AbstractBecause of the limitation of the spatial resolution of satellite sensors, satellite pixels identified as cloudy are often partly cloudy. For the first time, this study demonstrates the bias in shortwave (SW) broadband irradiances for partly cloudy pixels when the cloud optical depths are retrieved with an overcast and homogeneous assumption, and subsequently, the retrieved values are used for the irradiance computations. The sign of the SW irradiance bias is mainly a function of viewing geometry of the cloud retrieval. The bias in top-of-atmosphere (TOA) upward SW irradiances is positive for small viewing zenith angles (VZAs) <~60° and negative for large VZAs >~60°. For a given solar zenith angle and viewing geometry, the magnitude of the bias increases with the cloud optical depth and reaches a maximum at the cloud fraction between 0.2 and 0.8. The sign of the SW surface net irradiance bias is opposite of the sign of TOA upward irradiance bias, with a similar magnitude. As a result, the bias in absorbed SW irradiances by the atmosphere is smaller than the biases in both TOA and surface irradiances. The monthly mean biases in SW irradiances due to partly cloudy pixels are <1.5 W m−2 when cloud properties are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua.

A Novel Approach for the Detection of Developing Thunderstorm Cells

This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms have usually relied on accurate Atmospheric Motion Vectors (AMVs) for the estimation of the cooling rates of convective clouds, which correspond to the updraft strengths of the cloud objects. In this study, we present a method for the estimation of the updraft strength that does not rely on AMVs. The updraft strength is derived directly from the satellite observations in the SEVIRI water vapor channels. For this purpose, the absolute value of the vector product of spatio-temporal gradients of the SEVIRI water vapor channels is calculated for each satellite pixel, referred to as Normalized Updraft Strength (NUS). The main idea of the concept is that vertical updraft leads to NUS values significantly above zero, whereas horizontal cloud movement leads to NUS values close to zero. Thus, NUS is a measure of the strength of the vertical updraft and can be applied to distinguish between advection and convection. The performance of the method has been investigated for two summer periods in 2016 and 2017 by validation with lightning data. Values of the Critical Success Index (CSI) of about 66% for 2016 and 60% for 2017 demonstrate the good performance of the method. The Probability of Detection (POD) values for the base case are 81.8% for 2016 and 89.2% for 2017, respectively. The corresponding False Alarm Ratio (FAR) values are 22.6% (2016) and 36.4% (2017), respectively. In summary, the method has the potential to reduce forecast lead time significantly and can be quite useful in regions without a well-maintained radar network.

Surface Water Temperature Heterogeneity at Subpixel Satellite Scales and Its Effect on the Surface Cooling Estimates of a Large Lake: Airborne Remote Sensing Results From Lake Geneva

AbstractThe dynamics of spatial heterogeneity of lake surface water temperature (LSWT) at subpixel satellite scale O(1 m) and its effect on the surface cooling estimation at typical satellite pixel areas O(1 km2) were investigated using an airborne platform. The measurements provide maps that revealed spatial LSWT variability with unprecedented detail. The cold season data did not show significant LSWT heterogeneity and hence no surface cooling spatial variability. However, based on three selected daytime subpixel‐scale maps, LSWT patterns showed a variability of >2 °C in the spring and >3.5 °C in the summer, corresponding to a spatial surface cooling range of >20 and >40 W/m2, respectively. Due to the nonlinear relationship between turbulent surface heat fluxes and LSWT, negatively skewed LSWT distributions resulted in negatively skewed surface cooling patterns under very stable or predominantly unstable atmospheric boundary layer (ABL) conditions and positively skewed surface cooling patterns under predominantly stable ABL conditions. Implementing a mean spatial filter, the effect of area‐averaged LSWT on the surface cooling estimation up to a typical satellite pixel was assessed. The effect of the averaging filter size on the mean spatial surface cooling values was negligible, except for predominantly stable ABL conditions. In that situation, a reduction of ~3.5 W/m2 was obtained when moving from high O(1 m) to low O(1 km) pixel resolution.

Evaluation of Normalized Difference Water Index as a Tool for Monitoring Pasture Seasonal and Inter-Annual Variability in a Mediterranean Agro-Silvo-Pastoral System

Extensive animal production in Iberian Peninsula is based on pastures, integrated within the important agro-silvo-pastoral system, named “montado” in Portugal and “dehesa” in Spain. Temperature and precipitation are the main driving climatic factors affecting agricultural productivity and, in dryland pastures, the hydrological cycle of soil, identified by soil moisture content (SMC), is the main engine of the vegetation development. The objective of this work was to evaluate the normalized difference water index (NDWI) based on Sentinel-2 imagery as a tool for monitoring pasture seasonal dynamics and inter-annual variability in a Mediterranean agro-silvo-pastoral system. Forty-one valid NDWI records were used between January and June 2016 and between January 2017 and June 2018. The 2.3 ha experimental field is located within the “Mitra” farm, in the South of Portugal. Soil moisture content, pasture moisture content (PMC), pasture surface temperature (Tir), pasture biomass productivity and pasture quality degradation index (PQDI) were evaluated in 12 satellite pixels (10 m × 10 m). The results show significant correlations (p < 0.01) between NDWI and: (i) SMC (R2 = 0.7548); (ii) PMC (R2 = 0.8938); (iii) Tir (R2 = 0.5428); (iv) biomass (R2 = 0.7556); and (v) PQDI (R2 = 0.7333). These findings suggest that satellite-derived NDWI can be used in site-specific management of “montado” ecosystem to support farmers’ decision making.

Sub-metre mapping of surface soil moisture in proglacial valleys of the tropical Andes using a multispectral unmanned aerial vehicle

Surface soil moisture is a critical but often neglected component of the hydrologic budget. Within mountain environments, surface soil moisture is highly heterogeneous and challenging to measure. Point measurements are often poorly representative of larger areas, while satellite pixels are generally too coarse in these topographically varied landscapes. In the Cordillera Blanca, Peru, rapid glacier recession is impacting downstream water supply in timing, quantity and quality. Recent research has shown that these proglacial valleys provide vital ecosystem services and store considerable amounts of water within the groundwater systems. However, the spatial and temporal variability of soil water storage is poorly understood. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared, thermal infrared) sensors to map sections of two proglacial valleys in the Cordillera Blanca, Peru at sub-metre resolution. We use the Temperature Vegetation Dryness Index (TVDI) (Sandholt et al., 2002), and apply it for the first time to UAV borne imagery to estimate absolute surface soil moisture through calibration with in-scene field measurements. Resulting surface soil moisture maps have 50 cm spatial resolution with an R2 value of 0.55 and 0.76 for the two study sites, Pachacoto and Llanganuco respectively. We analyse the multispectral orthomosaics and soil moisture maps to improve our understanding of the controls on spatial variability in surface soil moisture within the proglacial valleys of the Cordillera Blanca. The maps permit us to identify both important groundwater spring sources and areas where domestic grazing impacts observed soil moisture estimates. The high resolution observations provided by the UAV facilitate a greater understanding of fine scale heterogeneity within these environments.

All-Sky Radiance Assimilation of ATMS in HWRF: A Demonstration Study

Abstract Satellite all-sky radiances from the Advanced Technology Microwave Sounder (ATMS) are assimilated into the Hurricane Weather Research and Forecasting (HWRF) Model using the hybrid Gridpoint Statistical Interpolation analysis system (GSI). To extend the all-sky capability recently developed for global applications to HWRF, some modifications in HWRF and GSI are facilitated. In particular, total condensate is added as a control variable, and six distinct hydrometeor habits are added as state variables in hybrid GSI within HWRF. That is, clear-sky together with cloudy and precipitation-affected satellite pixels are assimilated using the Community Radiative Transfer Model (CRTM) as a forward operator that includes hydrometeor information and Jacobians with respect to hydrometeor variables. A single case study with the 2014 Atlantic storm Hurricane Cristobal is used to demonstrate the methodology of extending the global all-sky capability to HWRF due to ATMS data availability. Two data assimilation experiments are carried out. One experiment uses the operational configuration and assimilates ATMS radiances under the clear-sky condition, and the other experiment uses the modified HWRF system and assimilates ATMS radiances under the all-sky condition with the inclusion of total condensate update and cycling. Observed and synthetic Geostationary Operational Environmental Satellite (GOES)-13 data along with Global Precipitation Measurement Mission (GPM) Microwave Imager (GMI) data from the two experiments are used to show that the experiment with all-sky ATMS radiances assimilation has cloud signatures that are supported by observations. In contrast, there is lack of clouds in the initial state that led to a noticeable lag of cloud development in the experiment that assimilates clear-sky radiances.

Evaluation of SMAP Soil Moisture Relative to Five Other Satellite Products Using the Climate Reference Network Measurements Over USA

Satellite platforms provide a unique opportunity to retrieve global soil moisture. The Level 3 soil moisture (L3SMP), enhanced Level 3 soil moisture (L3SMP-E) and Level 4 surface soil moisture (L4SM) products of the latest soil moisture satellite mission, Soil Moisture Active Passive (SMAP), are evaluated relative to five other satellite products using the Climate Reference Network (CRN) with more than 110 stations (each with three in situ probes) over USA from 2009 to present. This large number of stations allows for the categorization of SMAP performance based on land cover types, complementing prior efforts based on the few core validation areas with many in situ observations within a single satellite pixel. The SMAP as well as Aquarius products clearly outperform the other products. Over all land cover types, L3SMP, L3SMP-E, and L4SM are better in the summer than in the winter and they perform best over short vegetation. L4SM has higher correlations compared with CRN than L3SMP over tall and short vegetation whereas L3SMP has higher correlations over crops. On average, L3SMP-E performs as well as L3SMP. There is a mismatch between the point in situ measurements and satellite pixel retrievals driven by subpixel precipitation variability, and its impact on these results is also assessed.

Radiometric homogenisation of aerial images by calibrating with satellite data

ABSTRACTThe use of very high resolution (VHR) aerial imagery for quantitative remote sensing has been limited by unwanted radiometric variation over temporal and spatial extents. In this paper we propose a simple yet effective technique for the radiometric homogenisation of the digital numbers of aerial images. The technique requires a collocated and concurrent, well-calibrated satellite image as surface reflectance reference to which the aerial images are calibrated. The bands of the reference satellite sensor should be spectrally similar to those of the aerial sensor. Using radiative transfer theory, we show that a spatially varying local linear model can be used to approximate the relationship between the surface reflectance of the reference image and the digital numbers of the aerial images. The model parameters for each satellite pixel location are estimated using least squares regression inside a small sliding window. The technique was applied to a set of aerial images captured over multiple days with an Intergraph Digital Mapping Camera (DMC) system. A near-concurrent Moderate Resolution Imaging Spectroradiometer (MODIS) nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance image was used as the reflectance reference dataset. The resulting DMC mosaic was compared to a near-concurrent Satellite Pour l’Observation de la Terre (SPOT) 5 reflectance image of a portion of the same area, omitting the blue channel from the DMC mosaic due to its absence in the SPOT 5 data. The mean absolute reflectance difference was found to be 3.43% and the mean coefficient of determination (R2) over the bands was 0.84. The technique allows the production of seamless mosaics corrected for coarse scale atmospheric and BRDF effects and does not require the manual acquisition (or provision) of ground reflectance references. The accuracy of corrections is limited by the resolution of the reference image, which is generally significantly coarser than VHR imagery. The method cannot correct for small scale BRDF or other variations not captured at the reference resolution. Nevertheless, results show a significant improvement in homogeneity and correlation with SPOT 5 reflectance.

Preliminary assessment of 20-m surface albedo retrievals from sentinel-2A surface reflectance and MODIS/VIIRS surface anisotropy measures

Satellite-based retrievals of land surface albedo at 20-m resolution are generated by coupling the surface reflectances from the recently launched Sentinel-2A satellite with surface anisotropy information (as described by Bidirectional Reflectance Distribution Function, BRDF) from either the MODerate-resolution Imaging Spectroradiometer (MODIS) or the Visible Infrared Imaging Radiometer Suite (VIIRS). The intrinsic black-sky albedo (BSA) and white-sky albedo (WSA) values of the surface are derived at the six shortwave spectral bands of Sentinel-2A's Multi Spectral Instrument (MSI). A specific set of narrow-to-broadband conversion coefficients is derived from radiative transfer simulations and presented for the generation of broadband albedos. Initial evaluation uses well-calibrated ground-based albedo measurements by pyranometers mounted on the towers at seven sites of the Surface Radiation Network (SURFRAD). Over those sites where pyranometer measurement footprints are not spatially representative of the landscape covered by the satellite pixels (i.e., spatially nonrepresentative) at the grid scales (500 m to 1 km) of the MODIS and VIIRS products, the finer-resolution Sentinel-2A albedos manifested a pronounced decrease in root mean squared error (RMSE) and mean bias in the evaluation against the ground-based data as compared to the coarser resolution albedo products of MODIS and VIIRS. This decrease occurs because the 20-m Sentinel-2A albedo values are better able to resolve the spatial details of surface albedo within the ground-based instrument footprints than the coarser resolution sensors. This preliminary evaluation also demonstrates the consistency of the Sentinel-2A albedo results whether using the MODIS BRDF or the VIIRS BRDF products for the surface anisotropy information. The RMSEs and mean biases of the Sentinel-2A albedos over all the seven validation sites are both within the accuracy requirement of ±0.05 absolute albedo units for satellite derived albedo products. This study, to generate Sentinel-2A MSI albedo with either MODIS or VIIRS BRDFs, extends previous efforts of Landsat TM, ETM+ and OLI albedo and enhances the continuity of finer-resolution albedo data. Such long-term and higher resolution records of surface albedo improve the investigations into the changes and drivers of local/regional surface energy balance over heterogeneous regions and increasingly fragmented landscapes across the globe due to natural and human-induced land cover changes.

Towards an along‐track validation of HOAPS precipitation using OceanRAIN optical disdrometer data over the Atlantic Ocean

The Hamburg Ocean Atmosphere Parameters and fluxes from Satellite data (HOAPS) passive‐microwave precipitation scan product is compared with the Ocean Rainfall And Ice‐phase precipitation measurement Network (OceanRAIN) surface‐based precipitation reference dataset over the global ocean. For the first time, we apply statistical point‐to‐area adjustments and along‐track averaging to ship‐based OceanRAIN precipitation data over the Atlantic Ocean to represent collocated precipitation rates better within a HOAPS satellite pixel. The statistical adjustments reduce the HOAPS–OceanRAIN root‐mean‐square error strongly from 2.65 to 1.01 mm/hr. Overall, the point‐to‐area effect impacts HOAPS–OceanRAIN differences more strongly than the precipitation regime. Higher‐resolution satellite data indicate that these adjustments work best for the most convective‐like precipitation cases, while some rather stratiform‐like precipitation cases would need no adjustment. Excluding precipitation rates below the HOAPS sensitivity threshold of 0.3 mm/hr reduces the difference in average precipitation rates between HOAPS hits and false detections combined and OceanRAIN hits and misses combined to 2%. This precipitation‐rate difference lies below the uncertainty obtained from resampling of about 10%. Without false detections, the HOAPS precipitation rate of hits‐only exceeds that of OceanRAIN by 50%. Most of the HOAPS false detections follow from cases when precipitation occurs within the HOAPS pixel but off the ship track. Consequently, these apparent false detections lead to an overestimation of HOAPS precipitation rates compared with OceanRAIN, particularly in the inner Tropics and partly the midlatitudes, where clustered convective precipitation occurs most frequently. Misses cause underestimated HOAPS precipitation rates, mainly in mid and high latitudes. However, for a HOAPS validation, apparent false detections need to be considered in addition to hits of OceanRAIN precipitation rates, to which we successfully applied statistical point‐to‐area adjustments.

Intercomparison of Six Upscaling Evapotranspiration Methods: From Site to the Satellite Pixel

AbstractLand surface evapotranspiration (ET) is an important component of the surface energy budget and water cycle. To solve the problem of the spatial‐scale mismatch between in situ observations and remotely sensed ET, it is necessary to find the most appropriate upscaling approach for acquiring ground truth ET data at the satellite pixel scale. Based on a data set from two flux observation matrices in the middle stream and downstream of the Heihe River Basin, six upscaling methods were intercompared via direct validation and cross validation. The results showed that the area‐weighted method performed better than the other five upscaling methods introducing auxiliary variables (the integrated Priestley‐Taylor equation, weighted area‐to‐area regression kriging [WATARK], artificial neural network, random forest [RF], and deep belief network methods) over homogeneous underlying surfaces. Over moderately heterogeneous underlying surfaces, the WATARK method performed better. However, the RF method performed better over highly heterogeneous underlying surfaces. A combined method (using the area‐weighted and WATARK methods for homogeneous and moderately heterogeneous underlying surfaces, respectively, and using the RF method for highly heterogeneous underlying surfaces) was proposed to acquire the daily ground truth ET data at the satellite pixel scale, and the errors in the ground truth ET data were evaluated. The Dual Temperature Difference (DTD) and ETMonitor were validated using ground truth ET data, which solve the problem of the spatial‐scale mismatch and quantify uncertainties in the validation process.