The MODIS (collection V006) BRDF/albedo product MCD43D: temporal course evaluated over agricultural landscape

The assessment of uncertainties in satellite-derived global surface albedo products is a critical aspect for studying the climate, ecosystem change, hydrology or the Earth's radiant energy budget. However, it is challenged by the spatial scaling errors between satellite and field measurements. This study aims at evaluating the forthcoming MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V006) Bidirectional Reflectance Distribution Function (BRDF)/albedo product MCD43D over a Mediterranean agricultural area. Here, we present the results from the accuracy assessment of the MODIS blue-sky albedo. The analysis is based on collocated comparisons with higher spatial resolution estimates from Formosat-2 that were first evaluated against local in situ measurements. The inter-sensor comparison is achieved by taking into account the effective point spread function (PSF) for MODIS albedo, modeled as Gaussian functions in the North–South and East–West directions. The equivalent PSF is estimated by correlation analysis between MODIS albedo and Formosat-2 convolved albedo. Results show that it is 1.2 to 2.0 times larger in the East–West direction as compared to the North–South direction. We characterized the equivalent PSF by a full width at half maximum size of 1920 m in East–West, 1200 m in North–South. This provided a very good correlation between the products, showing absolute (relative) Root Mean Square Errors from 0.004 to 0.013 (2% to 7%), and almost no bias. By inspecting 1-km plots homogeneous in land cover type, we found poorer performances over rice and marshes (i.e., relative Root Mean Square Error of about 11% and 7%, and accuracy of 0.011 and − 0.008, respectively), and higher accuracy over dry and irrigated pastures, as well as orchards (i.e., relative uncertainty < 3.8% and accuracy < 0.003). The study demonstrates that neglecting the MODIS PSF when comparing the Formosat-2 albedo against the MODIS one induces an additional uncertainty up to 0.02 (10%) in albedo. The consistency between fine and coarse spatial resolution albedo estimates indicates the ability of the daily MCD43D product to reproduce reasonably well the dynamics of albedo

Uncertainty assessment of surface net radiation derived from Landsat images

The net radiation flux available at the Earth's surface drives evapotranspiration, photosynthesis and other physical and biological processes. The only cost-effective way to capture its spatial and temporal variability at regional and global scales is remote sensing. However, the accuracy of net radiation derived from remote sensing data has been evaluated up to now over a limited number of in situ measurements and ecosystems. This study aims at evaluating estimates and uncertainties on net radiation derived from Landsat-7 images depending on reliability of the input surface variables albedo, emissivity and surface temperature. The later includes the reliability of remote sensing information (spectral reflectances and top of canopy brightness temperature) and shortwave and longwave incoming radiations. Primary information describing the surface is derived from remote sensing observations. Surface albedo is estimated from spectral reflectances using a narrow-to-broadband conversion method. Land surface temperature is retrieved from top of canopy brightness temperature by accounting for land surface emissivity and reflection of atmospheric radiation; and emissivity is estimated using a relationship with a vegetation index and a spectral database of soil and plant canopy properties in the study area. The net radiation uncertainty is assessed using comparison with ground measurements over the Crau–Camargue and lower Rhone valley regions in France. We found Root Mean Square Errors between retrievals and field measurements of 0.25–0.33 (14–19%) for albedo, ~ 1.7 K for surface temperature and ~ 20 W·m− 2 (5%) for net radiation. Results show a substantial underestimation of Landsat-7 albedo (up to 0.024), particularly for estimates retrieved using the middle infrared, which could be due to different sources: the calibration of field sensors, the correction of radiometric signals from Landsat-7 or the differences in spectral bands with the sensors for which the models where originally derived, or the atmospheric corrections. We report a global uncertainty in net radiation of 40–100 W·m− 2 equally distributed over the shortwave and longwave radiation, which varies spatially and temporally depending on the land use and the time of year. In situ measurements of incoming shortwave and longwave radiation contribute the most to uncertainty in net radiation (10–40 W·m− 2 and 20–30 W·m− 2, respectively), followed by uncertainties in albedo (< 25 W·m− 2) and surface temperature (~ 8 W·m− 2). For the latter, the main factors were the uncertainties in top of canopy reflectances (< 10 W·m− 2) and brightness temperature (5–7 W·m− 2). The generalization of these results to other sensors and study regions could be considered, except for the emissivity if prior knowledge on its characterization is not available

Estimation of the Relationship Between Remotely Sensed Anthropogenic Heat Discharge and Building Energy Use

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered

Comparison between Snow Albedo Obtained from Landsat TM, ETM+ Imagery and the SPOT VEGETATION Albedo Product in a Mediterranean Mountainous Site

Albedo plays an important role in snow evolution modeling quantifying the amount of solar radiation absorbed and reflected by the snowpack, especially in mid-latitude regions with semiarid conditions. Satellite remote sensing is the most extensive technique to determine the variability of snow albedo over medium to large areas; however, scale effects from the pixel size of the sensor source may affect the results of snow models, with different impacts depending on the spatial resolution. This work presents the evaluation of snow albedo values retrieved from (1) Landsat images, L (16-day frequency with 30 30 m pixel size) and (2) SPOT VEGETATION albedo products, SV (10-day frequency with 1 1 km pixel size) in the Sierra Nevada mountain range in South Spain, a Mediterranean site representative of highly heterogeneous conditions. Daily snow albedo map series were derived from both sources, and used as input for the snow module in the WiMMed (Watershed Integrated Management in Mediterranean Environment) hydrological model, which was operational at the study area for snow monitoring for two hydrological years, 2011–2012 and 2012–2013, in the Guadalfeo river basin in Sierra Nevada. The results showed similar albedo trends in both data sources, but with different values, the shift between both sources being distributed in space according to the altitude. This difference resulted in lower snow cover fraction values in the SV-simulations that affected the rest of snow variables included in the simulation. This underestimation, mainly due to the effects of mixed pixels composed by both snow and snow-free areas, produced higher divergences from both sources during the melting periods when the evapo-sublimation and melting fluxes are more relevant. Therefore, the selection of the albedo data source in these areas, where snow evapo-sublimation plays a very important role and the presence of snow-free patches is very frequent, can condition the final accuracy of the simulations of operational models; Landsat is the recommended source if the monitoring of the snowpack is the final goal of the modeling, whereas the SV product may be advantageous when water resource planning in the medium and long term is intended. Applications of large pixel size albedo sources need further assessment for short-term operational objective

Evapotranspiration mapping of commercial corn fields in Brazil using SAFER algorithm

SAFER (Simple Algorithm for Evapotranspiration Retrieving) is a relatively new algorithm applied successfully to estimate actual crop evapotranspiration (ET) at different spatial scales of different crops in Brazil. However, its use for monitoring irrigated crops is scarce and needs further investigation. This study assessed the performance of SAFER to estimate ET of irrigated corn in a Brazilian semiarid region. The study was conducted in São Desidério, Bahia State, Brazil, in corn-cropped areas in no-tillage systems and irrigated by central pivots. SAFER algorithm with original regression coefficients (a = 1.8 and b = –0.008) was initially tested during the growing seasons of 2014, 2015, and 2016. SAFER performed very poorly for estimating corn ET, with RMSD values greater than 1.18 mm d–1 for 12 fields analyzed and NSE values &lt; 0 in most fields. To improve estimates, SAFER regression coefficients were calibrated (using 2014 and 2015 data) and validated with 2016 data, with the resulting coefficients a and b equal to 0.32 and –0.0013, respectively. SAFER performed well for ET estimation after calibration, with r2 and NSE values equal to 0.91 and RMSD = 0.469 mm d–1. SAFER also showed good performance (r2 = 0.86) after validation, with the lowest RMSD (0.58 mm d–1) values for the set of 14 center pivots in this growing season. The results support the use of calibrated SAFER algorithm as a tool for estimating water consumption in irrigated corn fields in semiarid conditions

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI

Mitigating the impact of atmospheric effects on optical remote sensing data is critical for monitoring intrinsic land processes and developing Analysis Ready Data (ARD). This work develops an approach to this for the NERC NCEO medium resolution ARD Landsat 8 (L8) and Sentinel 2 (S2) products, called Sensor Invariant Atmospheric Correction (SIAC). The contribution of the work is to phrase and solve that problem within a probabilistic (Bayesian) framework for medium resolution multispectral sensors S2/MSI and L8/OLI and to provide per-pixel uncertainty estimates traceable from assumed top-of-atmosphere (TOA) measurement uncertainty, making progress towards an important aspect of CEOS ARD target requirements. A set of observational and a priori constraints are developed in SIAC to constrain an estimate of coarse resolution (500 m) aerosol optical thickness (AOT) and total column water vapour (TCWV), along with associated uncertainty. This is then used to estimate the medium resolution (10–60 m) surface reflectance and uncertainty, given an assumed uncertainty of 5 % in TOA reflectance. The coarse resolution a priori constraints used are the MODIS MCD43 BRDF/Albedo product, giving a constraint on 500 m surface reflectance, and the Copernicus Atmosphere Monitoring Service (CAMS) operational forecasts of AOT and TCWV, providing estimates of atmospheric state at core 40 km spatial resolution, with an associated 500 m resolution spatial correlation model. The mapping in spatial scale between medium resolution observations and the coarser resolution constraints is achieved using a calibrated effective point spread function for MCD43. Efficient approximations (emulators) to the outputs of the 6S atmospheric radiative transfer code are used to estimate the state parameters in the atmospheric correction stage. SIAC is demonstrated for a set of global S2 and L8 images covering AERONET and RadCalNet sites. AOT retrievals show a very high correlation to AERONET estimates (correlation coefficient around 0.86, RMSE of 0.07 for both sensors), although with a small bias in AOT. TCWV is accurately retrieved from both sensors (correlation coefficient over 0.96, RMSE &lt;0.32 g cm−2). Comparisons with in situ surface reflectance measurements from the RadCalNet network show that SIAC provides accurate estimates of surface reflectance across the entire spectrum, with RMSE mismatches with the reference data between 0.01 and 0.02 in units of reflectance for both S2 and L8. For near-simultaneous S2 and L8 acquisitions, there is a very tight relationship (correlation coefficient over 0.95 for all common bands) between surface reflectance from both sensors, with negligible biases. Uncertainty estimates are assessed through discrepancy analysis and are found to provide viable estimates for AOT and TCWV. For surface reflectance, they give conservative estimates of uncertainty, suggesting that a lower estimate of TOA reflectance uncertainty might be appropriate