scholarly journals A New Set of MODIS Land Products (MCD18): Downward Shortwave Radiation and Photosynthetically Active Radiation

2020 ◽  
Vol 12 (1) ◽  
pp. 168 ◽  
Author(s):  
Dongdong Wang ◽  
Shunlin Liang ◽  
Yi Zhang ◽  
Xueyuan Gao ◽  
Meredith G. L. Brown ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Photosynthetically Active Radiation ◽  
Process Models ◽  
Surface Radiation ◽  
Shortwave Radiation ◽  
Glass Product ◽  
Ancillary Data ◽  
Diurnal Variability ◽  
Active Radiation ◽  
Downward Shortwave Radiation

Surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR), its visible component, are key parameters needed for many land process models and terrestrial applications. Most existing DSR and PAR products were developed for climate studies and therefore have coarse spatial resolutions, which cannot satisfy the requirements of many applications. This paper introduces a new global high-resolution product of DSR (MCD18A1) and PAR (MCD18A2) over land surfaces using the MODIS data. The current version is Collection 6.0 at the spatial resolution of 5 km and two temporal resolutions (instantaneous and three-hour). A look-up table (LUT) based retrieval approach was chosen as the main operational algorithm so as to generate the products from the MODIS top-of-atmosphere (TOA) reflectance and other ancillary data sets. The new MCD18 products are archived and distributed via NASA’s Land Processes Distributed Active Archive Center (LP DAAC). The products have been validated based on one year of ground radiation measurements at 33 Baseline Surface Radiation Network (BSRN) and 25 AmeriFlux stations. The instantaneous DSR has a bias of −15.4 W/m2 and root mean square error (RMSE) of 101.0 W/m2, while the instantaneous PAR has a bias of −0.6 W/m2 and RMSE of 45.7 W/m2. RMSE of daily DSR is 32.3 W/m2, and that of the daily PAR is 13.1 W/m2. The accuracy of the new MODIS daily DSR data is higher than the GLASS product and lower than the CERES product, while the latter incorporates additional geostationary data with better capturing DSR diurnal variability. MCD18 products are currently under reprocessing and the new version (Collection 6.1) will provide improved spatial resolution (1 km) and accuracy.

scholarly journals Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation

2020 ◽  
Vol 12 (3) ◽  
pp. 372
Author(s):  
Meredith G. L. Brown ◽  
Sergii Skakun ◽  
Tao He ◽  
Shunlin Liang
Keyword(s):  
Machine Learning ◽  
Photosynthetically Active Radiation ◽  
Input Data ◽  
Surface Radiation ◽  
Shortwave Radiation ◽  
Learning Methods ◽  
Active Radiation ◽  
Machine Learning Methods ◽  
Robust Model ◽  
Data Requirements

Satellite-derived estimates of downward surface shortwave radiation (SSR) and photosynthetically active radiation (PAR) are a part of the surface radiation budget, an essential climate variable (ECV) required by climate and vegetation models. Ground measurements are insufficient for generating long-term, global measurements of surface radiation, primarily due to spatial limitations; however, remotely sensed Earth observations offer freely available, multi-day, global coverage of radiance that can be used to derive SSR and PAR estimates. Satellite-derived SSR and PAR estimates are generated by computing the radiative transfer inversion of top-of-atmosphere (TOA) measurements, and require ancillary data on the atmospheric condition. To reduce computational costs, often the radiative transfer calculations are done offline and large look-up tables (LUTs) are generated to derive estimates more quickly. Recently studies have begun exploring the use of machine-learning techniques, such as neural networks, to try to improve computational efficiency. Here, nine machine-learning methods were tested to model SSR and PAR using minimal input data from the Moderate Resolution Imaging Spectrometer (MODIS) observations at 1 km spatial resolution. The aim was to reduce the input data requirements to create the most robust model possible. The bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression, and Neural Network yielded the best results with minimal training data requirements: an R 2 of 0.77, 0.78, and 0.78 respectively, a bias of 0 ± 6, 0 ± 6, and 0 ± 5 W / m 2 , and an RMSE of 140 ± 7, 135 ± 8, and 138 ± 7 W / m 2 , respectively, for all-sky condition total surface shortwave radiation and viewing angles less than 55°. Viewing angles above 55° were excluded because the residual analysis showed exponential error growth above 55°. A simple, robust model for estimating SSR and PAR using machine-learning methods is useful for a variety of climate system studies. Future studies may focus on developing high temporal resolution direct and diffuse estimates of SSR and PAR as most current models estimate only total SSR or PAR.

scholarly journals A New Clear-Sky Method for Assessing Photosynthetically Active Radiation at the Surface Level

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 219
Author(s):  
William Wandji Nyamsi ◽  
Philippe Blanc ◽  
John A. Augustine ◽  
Antti Arola ◽  
Lucien Wald
Keyword(s):  
Photosynthetically Active Radiation ◽  
Photosynthetic Photon Flux Density ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Photon Flux ◽  
Photon Flux Density ◽  
Distribution Method ◽  
Active Radiation ◽  
Surface Level ◽  
Clear Sky

A clear–sky method to estimate the photosynthetically active radiation (PAR) at the surface level in cloudless atmospheres is presented and validated. It uses a fast and accurate approximation adopted in several radiative transfer models, known as the k-distribution method and the correlated-k approximation, which gives a set of fluxes accumulated over 32 established wavelength intervals. A resampling technique, followed by a summation, are applied over the wavelength range [0.4, 0.7] µm in order to retrieve the PAR fluxes. The method uses as inputs the total column contents of ozone and water vapor, and optical properties of aerosols provided by the Copernicus Atmosphere Monitoring Service. To validate the method, its outcomes were compared to instantaneous global photosynthetic photon flux density (PPFD) measurements acquired at seven experimental sites of the Surface Radiation Budget Network (SURFRAD) located in various climates in the USA. The bias lies in the interval [−12, 61] µmol m−2 s−1 ([−1, 5] % in values relative to the means of the measurements at each station). The root mean square error ranges between 37 µmol m−2 s−1 (3%) and 82 µmol m−2 s−1 (6%). The squared correlation coefficient fluctuates from 0.97 to 0.99. This comparison demonstrates the high level of accuracy of the presented method, which offers an accurate estimate of PAR fluxes in cloudless atmospheres at high spatial and temporal resolutions useful for several bio geophysical models.

scholarly journals Deriving Photosynthetically Active Radiation at ground level in cloud-free conditions from Copernicus Atmospheric Monitoring Service (CAMS) products

2018 ◽  
Author(s):  
William Wandji Nyamsi ◽  
Phillipe Blanc ◽  
John A. Augustine ◽  
Antti Arola ◽  
Lucien Wald
Keyword(s):  
Photosynthetically Active Radiation ◽  
Ground Level ◽  
Photosynthetic Photon Flux Density ◽  
Surface Radiation ◽  
Coefficient Of Determination ◽  
Photon Flux ◽  
Photon Flux Density ◽  
Distribution Method ◽  
Active Radiation ◽  
Monitoring Service

Abstract. A method is described that estimates the photosynthetically active radiation (PAR) at ground level in cloud-free conditions. It uses a fast approximation of the libRadtran radiative transfer numerical model, known as the k-distribution method and the correlated-k approximation of Kato et al. (1999). LibRadtran provides irradiances aggregated over several fixed spectral bands and a spectral resampling is proposed followed by an aggregation in the range [400, 700] nm. The Copernicus Atmosphere Monitoring Service (CAMS) produces daily estimates of the aerosol properties, and total column contents in water vapor and ozone that are input to the method. A comparison of the results is performed against instantaneous measurements of global Photosynthetic Photon Flux Density (PPFD) on a horizontal plane made in cloud-free conditions at seven sites of the Surface Radiation network (SURFRAD) in the USA in various climates. The bias ranges between −12 µmol m−2 s−1 (−1 % of the mean value at Desert Rock) and +61 µmol m−2 s−1 (+5 % at Penn. State Univ). The root mean square error ranges from 37 µmol m−2 s−1 (3 %) to 82 µmol m−2 s−1 (6 %). The coefficient of determination R2 ranges between 0.97 and 0.99. This work demonstrates the quality of the proposed method combined with the CAMS products.

scholarly journals DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution

2020 ◽  
Vol 12 (3) ◽  
pp. 2209-2221
Author(s):  
Dalei Hao ◽  
Ghassem R. Asrar ◽  
Yelu Zeng ◽  
Qing Zhu ◽  
Jianguang Wen ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Photosynthetically Active Radiation ◽  
Spatial Scales ◽  
Radiant Energy ◽  
Surface Radiation ◽  
Radiation Budget ◽  
High Temporal Resolution ◽  
Active Radiation ◽  
Global Coverage ◽  
Mean Square Errors

Abstract. Downward shortwave radiation (SW) and photosynthetically active radiation (PAR) play crucial roles in Earth system dynamics. Spaceborne remote sensing techniques provide a unique means for mapping accurate spatiotemporally continuous SW–PAR, globally. However, any individual polar-orbiting or geostationary satellite cannot satisfy the desired high temporal resolution (sub-daily) and global coverage simultaneously, while integrating and fusing multisource data from complementary satellites/sensors is challenging because of co-registration, intercalibration, near real-time data delivery and the effects of discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR), launched in February 2015, offers an unprecedented possibility to bridge the gap between high temporal resolution and global coverage and characterize the diurnal cycles of SW–PAR globally. In this study, we adopted a suite of well-validated data-driven machine-learning models to generate the first global land products of SW–PAR, from June 2015 to June 2019, based on DSCOVR/EPIC data. The derived products have high temporal resolution (hourly) and medium spatial resolution (0.1∘×0.1∘), and they include estimates of the direct and diffuse components of SW–PAR. We used independently widely distributed ground station data from the Baseline Surface Radiation Network (BSRN), the Surface Radiation Budget Network (SURFRAD), NOAA's Global Monitoring Division and the U.S. Department of Energy's Atmospheric System Research (ASR) program to evaluate the performance of our products, and we further analyzed and compared the spatiotemporal characteristics of the derived products with the benchmarking Clouds and the Earth's Radiant Energy System Synoptic (CERES) data. We found both the hourly and daily products to be consistent with ground-based observations (e.g., hourly and daily total SWs have low biases of −3.96 and −0.71 W m−2 and root-mean-square errors (RMSEs) of 103.50 and 35.40 W m−2, respectively). The developed products capture the complex spatiotemporal patterns well and accurately track substantial diurnal, monthly, and seasonal variations in SW–PAR when compared to CERES data. They provide a reliable and valuable alternative for solar photovoltaic applications worldwide and can be used to improve our understanding of the diurnal and seasonal variabilities of the terrestrial water, carbon and energy fluxes at various spatial scales. The products are freely available at https://doi.org/10.25584/1595069 (Hao et al., 2020).

scholarly journals Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset

2018 ◽  
Vol 9 (2) ◽  
pp. 627-645 ◽  
Author(s):  
Stefan Lange
Keyword(s):  
Spatial Variability ◽  
Spatial Resolution ◽  
Bias Correction ◽  
Mean Value ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Sampling Errors ◽  
Quantile Mapping ◽  
Meteorological Forcing

Abstract. Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.

scholarly journals DSCOVR/EPIC-derived global hourly/daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution

2020 ◽  
Author(s):  
Dalei Hao ◽  
Ghassem R. Asrar ◽  
Yelu Zeng ◽  
Qing Zhu ◽  
Jianguang Wen ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Photosynthetically Active Radiation ◽  
Spatial Scales ◽  
Surface Radiation ◽  
Radiation Budget ◽  
High Temporal Resolution ◽  
Active Radiation ◽  
Global Coverage ◽  
Spatio Temporal ◽  
Mean Square Errors

Abstract. Downward shortwave radiation (SW) and photosynthetically active radiation (PAR) play crucial roles in Earth system dynamics. Spaceborne remote sensing techniques provide a unique means for mapping accurate spatio-temporally-continuous SW/PAR, globally. However, any individual polar-orbiting or geostationary satellite cannot satisfy the desired high temporal resolution (sub-daily) and global coverage simultaneously, while integrating and fusing multi-source data from complementary satellites/sensors is challenging because of co-registration, inter-calibration, near real-time data delivery and the effects of discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR), launched in February 2015, offers an unprecedented possibility to bridge the gap between high temporal resolution and global coverage, and characterize the diurnal cycles of SW/PAR globally. In this study, we adopted a suite of well-validated data-driven machine-learning models to generate the first global land products of SW/PAR, from June 2015 to June 2019, based on DSCOVR/EPIC data. The derived products have high temporal resolution (hourly) and medium spatial resolution (0.1° × 0.1°), and include estimates of the direct and diffuse components of SW/PAR. We used independently widely-distributed ground station data from the Baseline Surface Radiation Network (BSRN), the Surface Radiation Budget Network (SURFRAD), NOAA's Global Monitoring Division and the U.S. Department of Energy’s Atmospheric System Research (ASR) program to evaluate the performance of our products, and further analyzed and compared the spatio-temporal characteristics of the derived products with the benchmarking Clouds and the Earth's Radiant Energy System Synoptic (CERES) data. We found both the hourly and daily products to be consistent with ground-based observations (e.g., hourly and daily total SWs have low biases of −3.96 and −0.71 W/m2 and root mean square errors (RMSEs) of 103.50 and 35.40 W/m2, respectively). The developed products capture the complex spatio-temporal patterns well and accurately track substantial diurnal, monthly, and seasonal variations of SW/PAR when compared to CERES data. They provide a reliable and valuable alternative for solar photovoltaic applications worldwide and can be used to improve our understanding of the diurnal and seasonal variabilities of the terrestrial water, carbon and energy fluxes at various spatial scales. The products are freely available at https://doi.org/10.25584/1595069 (Hao et al., 2020).

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