Modification of solar radiation model over rugged terrain

Li, Xin; Cheng, Guodong; Chen, Xianzhang; Ling, Lu

doi:10.1007/bf02885977

Cited by 41 publications

(34 citation statements)

References 5 publications

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“…Astronomical solar radiation (I 0 ): it was determined whether each DEM grid cell is irradiated directly in a specific 5-min-period(s) with the method indicated in Ref. 43. For each DEM grid cell that can be irradiated directly, I 0 was calculated as follows:…”

Section: Total Solar Radiationmentioning

confidence: 99%

“…where T is the length of the 5-min-period (300 s); S const is solar constant with the value of 1367 W∕m 2 ; E 0 is the correction parameter of earth-sun distance (unitless) and can be calculated from the day number of the year; 25 and cos θ is the cosine of the insolation angle on a tilted surface, which can be calculated as follows: 43 cos θ ¼ ðsin φ cos α − cos φ sin α cos βÞ sin δ þ ðsin φ sin α cos β þ cos φ cos αÞ cos δ cos ω þ sin α sin β cos δ sin ω;…”

Section: Total Solar Radiationmentioning

confidence: 99%

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Sharpening method of satellite thermal image based on the geographical statistical model

Zhang

et al. 2016

J. Appl. Remote Sens

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Abstract. To improve the effectiveness of thermal sharpening in mountainous regions, paying more attention to the laws of land surface energy balance, a thermal sharpening method based on the geographical statistical model (GSM) is proposed. Explanatory variables were selected from the processes of land surface energy budget and thermal infrared electromagnetic radiation transmission, then high spatial resolution (57 m) raster layers were generated for these variables through spatially simulating or using other raster data as proxies. Based on this, the local adaptation statistical relationship between brightness temperature (BT) and the explanatory variables, i.e., the GSM, was built at 1026-m resolution using the method of multivariate adaptive regression splines. Finally, the GSM was applied to the high-resolution (57-m) explanatory variables; thus, the high-resolution (57-m) BT image was obtained. This method produced a sharpening result with low error and good visual effect. The method can avoid the blind choice of explanatory variables and remove the dependence on synchronous imagery at visible and near-infrared bands. The influences of the explanatory variable combination, sampling method, and the residual error correction on sharpening results were analyzed deliberately, and their influence mechanisms are reported herein.

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Section: Total Solar Radiationmentioning

confidence: 99%

Section: Total Solar Radiationmentioning

confidence: 99%

Sharpening method of satellite thermal image based on the geographical statistical model

Zhang

et al. 2016

J. Appl. Remote Sens

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“…Although the global irradiance and components are wavelength-dependent in Landsat images, formulas can follow the broadband estimation scheme described by Zhang et al [34], except for the surrounding-reflected irradiance. The detailed calculation of the relevant terrain factors can be referenced in previous studies [26,15]. In addition, the TOA solar spectral irradiance of Landsat5 TM is replaced according to the official website [38].…”

Section: Estimation Of the Solar Spectral Irradiancementioning

confidence: 99%

“…Moreover, the spatial distribution of atmospheric transmissivity and optical depth vary with the rapidly changing elevation, aerosol optical depth (AOD) and precipitable water (PW); thus, topographic and atmospheric corrections must be performed simultaneously pixel by pixel [15,26,27]. In addition, surface anisotropy, which is a function of the view and illumination angles, is evident.…”

Section: Introductionmentioning

confidence: 99%

Improved Topographic Normalization for Landsat TM Images by Introducing the MODIS Surface BRDF

Zhang

Wen

et al. 2015

Remote Sensing

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Abstract:In rugged terrain, the accuracy of surface reflectance estimations is compromised by atmospheric and topographic effects. We propose a new method to simultaneously eliminate atmospheric and terrain effects in Landsat Thematic Mapper (TM) images based on a 30 m digital elevation model (DEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products. Moreover, we define a normalized factor of a Bidirectional Reflectance Distribution Function (BRDF) to convert the sloping pixel reflectance into a flat pixel reflectance by using the Ross Thick-Li Sparse BRDF model (Ambrals algorithm) and MODIS BRDF/albedo kernel coefficient products. Sole atmospheric correction and topographic normalization were performed for TM images in the upper stream of the Heihe River Basin. The results show that using MODIS atmospheric products can effectively remove atmospheric effects compared with the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model and the Landsat Climate Data Record (CDR). OPEN ACCESSRemote Sens. 2015, 7 6559Moreover, superior topographic effect removal can be achieved by considering the surface BRDF when compared with the surface Lambertian assumption of topographic normalization.

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“…The PSD is defined as its astronomical analog and geographic counter part with no atmospheric influence and only terrain intershielding is taken into account on its spatial distribution [1]. The daily PSD varies from locality to locality in rugged lands due to height, aspect and the obstruction created by the landforms in immediate surroundings [2]. The slopes aspects that positioned opposite to solar irradiance can not see sunrise (earlier) or sunset (later) than the plains [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Regional and Monthly Assessment of Possible Sunshine Duration in Pakistan: A Geographical Approach

Ambreen¹,

Ahmad²,

Qiu³

et al. 2015

JGIS

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The monthly possible sunshine hours have been simulated separately for all the months of the year with the help of ArcGIS. The results are evident that the geography of the area plays a pivotal role in giving shape to spatial distribution of monthly possible sunshine duration (PSD). The landforms distributions and latitudinal extent are the major geographical factors behind the spatial-temporal distribution of monthly PSD. Maps of all the months depict different sunshine hours that vary from region to region in Pakistan. The maximum difference in PSD was noticed between lofty mountains in the north and Indus Plains. In addition, the variation is phenomenal from January to August and vice versa. This sort of study based on spatial modeling is significant in Pakistan where we lack appropriate ground observed data of PSD.

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Modification of solar radiation model over rugged terrain

Cited by 41 publications

References 5 publications

Sharpening method of satellite thermal image based on the geographical statistical model

Sharpening method of satellite thermal image based on the geographical statistical model

Improved Topographic Normalization for Landsat TM Images by Introducing the MODIS Surface BRDF

Regional and Monthly Assessment of Possible Sunshine Duration in Pakistan: A Geographical Approach

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