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

Wang, Dongdong; Liang, Shunlin; Zhang, Yi; Gao, Xiu; Brown, Meredith G. L.; Jia, Aolin

doi:10.3390/rs12010168

Cited by 70 publications

(29 citation statements)

References 26 publications

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“…For simplicity, the average value of 0.46 is used in this study, which would bring an uncertainty to some extent. Fortunately, a new PAR product from MODIS (MCD18) will be released in the future (Wang et al, 2020), and this product will benefit our CFCB correction approach. In addition, FPAR is assumed to be constant in a month for clear‐sky and cloudy‐sky days for simplicity.…”

Section: Discussionmentioning

confidence: 99%

Correcting Clear‐Sky Bias in Gross Primary Production Modeling From Satellite Solar‐Induced Chlorophyll Fluorescence Data

Zhang

et al. 2020

JGR Biogeosciences

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Satellite solar-induced chlorophyll fluorescence (SIF) has been demonstrated the potential to monitor photosynthesis, quantified as gross primary production (GPP), for large areas. However, satellite SIF retrievals are only reliable for clear-sky conditions, creating overestimation in estimating GPP for all-sky conditions, called clear-sky bias. Clouds can reduce the total radiation and increase the diffuse radiation, which can have counteracting effects on photosynthesis since plants are more efficient in using diffuse radiation. To mitigate this uncertainty in estimating global GPP from satellite SIF, we propose a clear-sky bias correction approach that considers both the increase in light use efficiency (LUE) and the decrease in photosynthetically active radiation (PAR) under cloudy conditions. An enhancement factor (α) of LUE under cloudy conditions is first parameterized from FLUXNET data set, which is found to converge to 1.21 among all vegetation types. Then, a correction factor of clear-sky bias is developed to account for the variations of LUE under cloudy conditions, which is used to estimate global terrestrial GPP in 2019 with spaceborne SIF retrievals from TROPOspheric Monitoring Instrument (TROPOMI). We estimate global annual terrestrial GPP of 135.47 ± 3.36 and 142.03 ± 3.53 PgC/year, respectively, with and without correcting clear-sky bias. This approach is also able to identify regions with large uncertainties in GPP estimation from satellite SIF data due to the effects of clear-sky bias, such as eastern America, western Europe, and southern China. Our results highlight the importance of considering clear-sky bias in estimating terrestrial photosynthesis from satellite SIF data. Plain Language Summary Photosynthesis is an important process by which plants remove the atmosphere of carbon dioxide (CO 2) and fuel the life on Earth. Satellite solar-induced chlorophyll fluorescence (SIF) shows great potential in monitoring terrestrial photosynthesis. However, satellite SIF retrievals are only reliable for clear-sky days, creating biases in estimating photosynthesis for all-sky condition. Cloud can reduce the total radiation and increase the diffused radiation, which can have counteracting effects on photosynthesis since plants are more efficient in using diffused radiation. Under slightly cloudy conditions, the decrease and increase from these two effects are approximately equal. However, under extremely cloudy conditions, the decreased radiation outweighs the efficiency enhancement, leading to an overall negative effect on photosynthesis. In this study, we propose a practical approach to mitigate this uncertainty in estimating global photosynthesis from SIF. Using the TROPOspheric Monitoring Instrument (TROPOMI) SIF during the year of 2019, we estimate a global photosynthesis for all sky conditions to be 135.47 ± 3.36 PgC/year. We also recognize three regions including eastern America, western Europe, and southern China, where clear-sky bias is clearly corrected with our approach. Mitigating clear-sky...

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Section: Discussionmentioning

confidence: 99%

Correcting Clear‐Sky Bias in Gross Primary Production Modeling From Satellite Solar‐Induced Chlorophyll Fluorescence Data

Zhang

et al. 2020

JGR Biogeosciences

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“…The best comparison we can make is to the instantaneous SSR and PAR estimates from the new MODIS suite of products, MCD18. Wang et al [16] report RMSE between 10-18% at the different SURFRAD sites.…”

Section: Discussionmentioning

confidence: 99%

“…Current satellite-based estimates of surface radiation incorporate atmospheric information in their algorithms, which can be difficult to obtain and propagate error and uncertainty through the algorithm. A popular method for reducing the computational demands of generating a product is to compute the radiative transfer inversions offline and store them in a look-up table (LUT) [14][15][16]. LUTs can be generated using in situ data or simulated data, and their major advantage is the ability to do the radiative transfer inversion calculations ahead of time to speed up data generation [15].…”

Section: Introductionmentioning

confidence: 99%

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

Brown

Skakun

et al. 2020

Remote Sensing

Self Cite

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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.

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“…Two datasets were used; the first one was utilized for the development and the second one for the training of the models. GHI and PAR training data were downloaded from MODIS Land Products (MCD18) [44], particularly, the products handled were MCD18A1 [45] and MCD18A2 [46], respectively. This dataset covers a grid from 35.3 • N to 44.0 • N in latitude and from 9.5 • W to 3.5 • E in longitude.…”

Section: Methodsmentioning

confidence: 99%

A New Index Assessing the Viability of PAR Application Projects Used to Validate PAR Models

2021

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Photosynthetically active radiation (PAR) is a useful variable to estimate the growth of biomass or microalgae. However, it is not always feasible to access PAR measurements; in this work, two sets of nine hourly PAR models were developed. These models were estimated for mainland Spain from satellite data, using multilinear regressions and artificial neural networks. The variables utilized were combinations of global horizontal irradiance, clearness index, solar zenith angle cosine, relative humidity, and air temperature. The study territory was divided into regions with similar features regarding PAR through clustering of the PAR clearness index (kPAR). This methodology allowed PAR modeling for the two main climatic regions in mainland Spain (Oceanic and Mediterranean). MODIS 3 h data were employed to train the models, and PAR data registered in seven stations across Spain were used for validation. Usual validation indices assess the extent to which the models reproduce the observed data. However, none of those indices considers the exceedance probabilities, which allow the assessment of the viability of projects based on the data to be modeled. In this work, a new validation index based on these probabilities is presented. Hence, its use, along with the other indices, provides a double and thus more complete validation.

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

Cited by 70 publications

References 26 publications

Correcting Clear‐Sky Bias in Gross Primary Production Modeling From Satellite Solar‐Induced Chlorophyll Fluorescence Data

Correcting Clear‐Sky Bias in Gross Primary Production Modeling From Satellite Solar‐Induced Chlorophyll Fluorescence Data

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

A New Index Assessing the Viability of PAR Application Projects Used to Validate PAR Models

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