GLASS Daytime All-Wave Net Radiation Product: Algorithm Development and Preliminary Validation

Jiang, Bo; Liang, Shunlin; Ma, Han; Zhang, Xiaolin; Xiao, Zhiqiang; Zhao, Xiang; Jia, Kun; Yao, Yunjun; Jia, Aolin

doi:10.3390/rs8030222

Cited by 43 publications

(35 citation statements)

References 53 publications

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“…To further evaluate the land surface R n temporal variation from 2001 to 2008, we included the GLASS surface daytime R n product, which is a new product directly estimated from satellite observations without relying on other ancillary information from clouds and aerosols. The GLASS daytime R n shows an average RMSE of 31.61 W m −2 , average bias of −17.59 W m −2 , and R 2 of 0.879 (Jiang et al, ). The GLASS product suite focuses on long‐term data records based on multiple satellite observations and also takes advantage of existing high‐level satellite products (Liang et al, , ).…”

Section: Results Analysismentioning

confidence: 99%

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Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis

et al. 2018

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Earth surface net radiation (Rn) characterizes the surface radiation budget and plays a critical role in ecological, biogeochemical, and hydrological processes. The Rn products from remote sensing and reanalysis have not been validated comprehensively. In this study, four Rn products (Clouds and the Earth's Radiant Energy System [CERES], ERA‐Interim, Modern‐Era Retrospective analysis for Research and Applications version 2, and Japanese 55‐year Reanalysis) were validated using global ground measurements on monthly (255 sites) and annual (172 sites) timescales. These products have similar accuracies, with average root‐mean‐square error (RMSE) ranges of 5.35 W m−2 (monthly) and 2.30 W m−2 (annually). However, varying accuracies and systemic biases exist across different climatic zones. The annual land Rn intercomparison illustrates that large uncertainty exists over polar regions and deserts. A significantly negative annual anomaly in the CERES product for the 2001–2008 period is identified when examining annual Rn anomalies over the global land surface. Detailed uncertainty analysis indicates that the global CERES Rn anomaly is mainly due to different versions of input data such as aerosol optical thickness and atmospheric profiles (in 2006 and 2008) and cloud properties (in 2002). This work demonstrates that temporal analysis provides powerful quality control for global time series satellite products when the validation using ground measurements fails to capture potential issues.

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Section: Results Analysismentioning

confidence: 99%

“…As one of the newest global land net radiation products, the GLASS daytime R n product (Jiang et al, ) was also used for comparison with the CERES daytime R n to detect anomalous annual variations. The GLASS daytime R n product converts shortwave radiation to all‐wave net radiation using the Multivariate Adaptive Regression Splines model.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis

et al. 2018

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“…Durbha et al [28] used support vector regression (SVR) to retrieve LAI from Multi-angle Imaging SpectroRadiometer (MISR) data with satisfactory results. Jiang et al [29] used multivariate adaptive regression splines (MARS) to generate the long-term GLASS Daytime All-Wave Net Radiation Product. To generate the global FVC product, a satisfactory balance between the accuracy and the computational efficiency of the FVC estimation algorithm is needed.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data

et al. 2016

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Long-term global land surface fractional vegetation cover (FVC) products are essential for various applications. Currently, several global FVC products have been generated from medium spatial resolution remote sensing data. However, validation results indicate that there are inconsistencies and spatial and temporal discontinuities in the current FVC products. Therefore, the Global LAnd Surface Satellite (GLASS) FVC product algorithm using general regression neural networks (GRNNs), which achieves an FVC estimation accuracy comparable to that of the GEOV1 FVC product with much improved spatial and temporal continuities, was developed. However, the computational efficiency of the GRNNs method is low and unsatisfactory for generating the long-term GLASS FVC product. Therefore, the objective of this study was to discover an alternative algorithm for generating the GLASS FVC product that has both an accuracy comparable to that of the GRNNs method and adequate computational efficiency. Four commonly used machine learning methods, back-propagation neural networks (BPNNs), GRNNs, support vector regression (SVR), and multivariate adaptive regression splines (MARS), were evaluated. After comparing its performance of training accuracy and computational efficiency with the other three methods, the MARS model was preliminarily selected as the most suitable algorithm for generating the GLASS FVC product. Direct validation results indicated that the performance of the MARS model (R 2 = 0.836, RMSE = 0.1488) was comparable to that of the GRNNs method (R 2 = 0.8353, RMSE = 0.1495), and the global land surface FVC generated from the MARS model had good spatial and temporal consistency with that generated from the GRNNs method. Furthermore, the computational efficiency of MARS was much higher than that of the GRNNs method. Therefore, the MARS model is a suitable algorithm for generating the GLASS FVC product from Moderate Resolution Imaging Spectroradiometer (MODIS) data.

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“…On this basis, the collected ground measurements can then be aggregated into the pixel scale to validate corresponding RSPs with the aid of upscaling approaches, particularly against coarse-scale products (e.g., [44]). Assessment of the MODIS LAI product in the meadow steppe [86]; Sampling strategy for observing and validating remotely sensed LAI product over heterogeneous land surface [87,88]; Development of a WSN used for validating LAI products [89]; Upscaling algorithm to obtain ground truth of LAI time series over heterogeneous land surface [90] LST TIR 90 m-1 km [11,55,58,60,[91][92][93] Evaluation of LST retrieval from FY-3B/VIRR data [94]; Analysis of scale mismatch between in situ and remote sensing LST product [95]; Evaluation and validation of the MODIS LST in arid regions of China [96,97]; Validation of LST estimated from ASTER data in the HRB [98] Net radiation VNIR, TIR 30 m-1 km [99][100][101][102][103] Analysis of representativeness errors of point-scale ground-based solar radiation measurements in the validation of RSPs [104]; A framework to obtain high resolution surface radiation products [105] …”

Section: Continuous Product This Type Of Variable Features Continuoumentioning

confidence: 99%

Validation of Regional-Scale Remote Sensing Products in China: From Site to Network

Wang

et al. 2016

Remote Sensing

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Abstract:Validation is mandatory to quantify the reliability of remote sensing products (RSPs). However, this process is not straightforward and usually presents formidable challenges in terms of both theory and real-world operations. In this context, a dedicated validation initiative was launched in China, and we identified a validation strategy (VS). This overall VS focuses on validating regional-scale RSPs with a systematic site-to-network concept, consisting of four main components: (1) general guidelines and technical specifications to guide users in validating various land RSPs, particularly aiming to further develop in situ sampling schemes and scaling approaches to acquire ground truth at the pixel scale over heterogeneous surfaces; (2) sound site-based validation activities, conducted through multi-scale, multi-platform, and multi-source observations to experimentally examine and improve the first component; (3) a national validation network to allow for comprehensive assessment of RSPs from site or regional scales to the national scale across various zones; and (4) an operational RSP evaluation system to implement operational validation applications. Research progress on the development of these four components is described in this paper. Some representative research results, with respect to the development of sampling methods and site-based validation activities, are also highlighted. The development of this VS improves our understanding of validation issues, especially to facilitate validating RSPs over heterogeneous land surfaces both at the pixel scale level and the product level.

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GLASS Daytime All-Wave Net Radiation Product: Algorithm Development and Preliminary Validation

Cited by 43 publications

References 53 publications

Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis

Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis

Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data

Validation of Regional-Scale Remote Sensing Products in China: From Site to Network

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