Evaluating the Relationship between Field Aerodynamic Roughness and the MODIS BRDF, NDVI, and Wind Speed over Grassland

Xing, Qiang; Wu, Bingfang; Niu, Y.; Yu, Mingzhou; Zhu, Weiwei

doi:10.3390/atmos8010016

Cited by 4 publications

(7 citation statements)

References 20 publications

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“…The BRDF_R MODIS and NDVI MODIS data are both sensitive indicators of the z0m over grasslands (R 2 : 0.5228 for NIR BRDF_R MODIS ; R 2 : 0.579 for NDVI MODIS ). The combined index has a significantly higher R 2 value of 0.721, which is similar to the result inferred from the wind speed (R 2 : 0.7411) [17]. However, the result is different for the sparse Tamarix at Si Daoqiao site in this study.…”

Section: The Difference With Farmland and Grasslandsupporting

confidence: 81%

“…The NDVI Landsat8 data also show little correlation with the field z0m with an R 2 value of 0.1646 and a large dispersion (Figure 5c). These values clearly indicate that the NDVI and BRDF_R factors are not sensitive indicators for characterizing the z0m variations over sparse Tamarix at Si Daoqiao site, which is significantly different from previous studies of farmlands and grasslands [14,17,18]. The underlying surface at the Si Daoqiao station is composed of sparse Tamarix and bare soils (Figure 1).…”

Section: Relationship Analysescontrasting

confidence: 79%

“…In recent years, multi-angle remote sensing information in the near-infrared (NIR) or shortwave infrared (SWIR) bands has been used to determine the z0m of farmlands over eight-day time intervals in the form of a bi-directional reflectance distribution function (BRDF) R index, which is the ratio of the weighting coefficient of the Li-sparse-reciprocal geometric scattering kernel and the weighting coefficient of the isotropic scattering contribution [14][15][16]. The combination of the NDVI and BRDF_R has been used to estimate the z0m of grassland and farmland areas [17,18]. However, whether the BRDF, NDVI, or their combinations are sensitive enough to estimate the z0m in sparsely vegetated regions remains unknown.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of BRDF, NDVI and Wind Speed to the Aerodynamic Roughness Length over Sparse Tamarix in the Downstream Heihe River Basin

Xing

Niu

et al. 2018

Remote Sensing

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Abstract:The aerodynamic roughness length (z0m) is an important parameter that affects the momentum and energy exchange between the earth surface's and the atmosphere. In this paper, the gradient wind speed data that were observed from May to October, 2014, at the Si Daoqiao station, which is located in an area of sparse Tamarix in the downstream region of the Heihe River Basin (HRB), are used to evaluate the sensitivity of the moderate-resolution imaging spectroradiometer (MODIS) near-infrared (NIR) bi-directional reflectance distribution function (BRDF) R, the MODIS/Landsat 8 normalized difference vegetation index (NDVI), and the wind speed at 5 m in comparison with the field-measured z0m. The results indicate that the NIR BRDF_R_MODIS and the NDVI MODIS /NDVI Landsat8 are less sensitive indicators of the z0m over sparse Tamarix areas (R 2 : 0.0045 for NIR BRDF_R MODIS ; R 2 : 0.0342 for NDVI MODIS ; and, R 2 : 0.1646 for NDVI Landsat8 ), which differs significantly from the results obtained by previous studies for farmlands and grasslands. However, there is a nearly linear correlation between the wind speed at 5 m and the z0m at the time scale of the NDVI Landsat8 acquisitions (R 2 : 0.3696). Furthermore, the combination of the NDVI and wind speed at 5 m can significantly improve this correlation (R 2 : 0.7682 for NDVI MODIS ; R 2 : 0.6304 for NDVI Landsat8 ), whereas the combination of the NIR BRDF_R MODIS and wind speed at 5 m still has a low correlation (R 2 : 0.0886). Finally, the regional z0m of the oasis in the downstream region of the HRB was determined using Landsat 8 surface reflectance data and the wind speed data at the Si Daoqiao station, which properly reflect the temporal evolution of the z0m in that region. The parameterization scheme proposed in this paper has great potential to be applied to evapotranspiration, land surface, and hydrologic model simulations of sparse Tamarix at Si Daoqiao site in the future.

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Section: The Difference With Farmland and Grasslandsupporting

confidence: 81%

Section: Relationship Analysescontrasting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

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Sensitivity of BRDF, NDVI and Wind Speed to the Aerodynamic Roughness Length over Sparse Tamarix in the Downstream Heihe River Basin

Xing

Niu

et al. 2018

Remote Sensing

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“…More recently, several studies have shown that z 0 for specific, uniform land cover types can also be estimated from optical remote sensing measurements, for instance, using vegetation indices (e.g. Xing et al, 2017;Yu et al, 2016;Bolle and Streckenbach, 1993;Hatfield, 1988;Moran, 1990). The z 0 can also be estimated using (satellite-derived) vegetation height (e.g.…”

mentioning

confidence: 99%

Satellite-derived leaf area index and roughness length information for surface–atmosphere exchange modelling: a case study for reactive nitrogen deposition in north-western Europe using LOTOS-EUROS v2.0

Graaf

Kranenburg²,

Segers³

et al. 2020

Geosci. Model Dev.

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Abstract. The nitrogen cycle has been continuously disrupted by human activity over the past century, resulting in almost a tripling of the total reactive nitrogen fixation in Europe. Consequently, excessive amounts of reactive nitrogen (Nr) have manifested in the environment, leading to a cascade of adverse effects, such as acidification and eutrophication of terrestrial and aquatic ecosystems, and particulate matter formation. Chemistry transport models (CTMs) are frequently used as tools to simulate the complex chain of processes that determine atmospheric Nr flows. In these models, the parameterization of the atmosphere–biosphere exchange of Nr is largely based on few surface exchange measurement and is therefore known to be highly uncertain. In addition to this, the input parameters that are used here are often fixed values, only linked to specific land use classes. In an attempt to improve this, a combination of multiple satellite products is used to derive updated, time-variant leaf area index (LAI) and roughness length (z0) input maps. As LAI, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD15A2H product. The monthly z0 input maps presented in this paper are a function of satellite-derived normalized difference vegetation index (NDVI) values (MYD13A3 product) for short vegetation types (such as grass and arable land) and a combination of satellite-derived forest canopy height and LAI for forests. The use of these growth-dependent satellite products allows us to represent the growing season more realistically. For urban areas, the z0 values are updated, too, and linked to a population density map. The approach to derive these dynamic z0 estimates can be linked to any land use map and is as such transferable to other models. We evaluated the sensitivity of the modelled Nr deposition fields in LOng Term Ozone Simulation – EURopean Operational Smog (LOTOS-EUROS) v2.0 to the abovementioned changes in LAI and z0 inputs, focusing on Germany, the Netherlands and Belgium. We computed z0 values from FLUXNET sites and compared these to the default and updated z0 values in LOTOS-EUROS. The root mean square difference (RMSD) for both short vegetation and forest sites improved. Comparing all sites, the RMSD decreased from 0.76 (default z0) to 0.60 (updated z0). The implementation of these updated LAI and z0 input maps led to local changes in the total Nr deposition of up to ∼30 % and a general shift from wet to dry deposition. The most distinct changes are observed in land-use-specific deposition fluxes. These fluxes may show relatively large deviations, locally affecting estimated critical load exceedances for specific natural ecosystems.

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“…More recently, several studies have shown that z 0 for specific, uniform land cover types can also be estimated from optical remote sensing measurements, for instance using vegetation indices (e.g. (Xing et al, 2017;Yu et al, 2016;Bolle and Streckenbach, 1993;Hatfield, 1988;Moran, 1990)). The z 0 can also be estimated using (satellite-derived) vegetation height (e.g.…”

mentioning

confidence: 99%

The effect of satellite derived leaf area index and roughness length information on modelled reactive nitrogen deposition in north-western Europe

Graaf

Kranenburg²,

Segers³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. The nitrogen cycle has been continuously disrupted by human activity over the past century, resulting in almost a tripling of the total reactive nitrogen fixation in Europe. Consequently, excessive amounts of reactive nitrogen (Nr) have manifested in the environment, leading to a cascade of adverse effects, such as acidification and eutrophication of terrestrial and aquatic ecosystems, and particulate matter formation. Chemistry transport models (CTM) are frequently used as tools to simulate the complex chain of processes that determine atmospheric Nr flows. In these models, the parameterization of the atmosphere-biosphere exchange of Nr is largely based on few surface exchange measurement and is therefore known to be highly uncertain. In addition to this, the input parameters that are used here are often fixed values, only linked to specific land use classes. In an attempt to improve this, a combination of multiple satellite products is used to derive updated, time-variant leaf area index (LAI) and roughness length (z0) input maps. As LAI, we use the MODIS MCD15A2H product. The monthly z0 input maps presented in this paper are a function of satellite-derived NDVI values (MYD13A3 product) for short vegetation types (such as grass and arable land) and a combination of satellite-derived forest canopy height and LAI for forests. The use of these growth-dependent satellite products allows us to represent the growing season more realistically. For urban areas, the z0 values are updated, too, and linked to a population density map. The approach to derive these dynamic z0 estimates can be linked to any land use map and is as such transferable to other models. We evaluated the resulting changes in modelled deposition of Nr components using the LOTOS-EUROS CTM, focusing on Germany, the Netherlands and Belgium. The implementation of these updated LAI and z0 input maps led to local changes in the total Nr deposition of up to ~ 30 % and a general shift from wet to dry deposition. The most distinct changes are observed in land use specific deposition fluxes. These fluxes may show relatively large deviations, locally affecting estimated critical load exceedances for specific natural ecosystems.

show abstract

Evaluating the Relationship between Field Aerodynamic Roughness and the MODIS BRDF, NDVI, and Wind Speed over Grassland

Cited by 4 publications

References 20 publications

Sensitivity of BRDF, NDVI and Wind Speed to the Aerodynamic Roughness Length over Sparse Tamarix in the Downstream Heihe River Basin

Sensitivity of BRDF, NDVI and Wind Speed to the Aerodynamic Roughness Length over Sparse Tamarix in the Downstream Heihe River Basin

Satellite-derived leaf area index and roughness length information for surface–atmosphere exchange modelling: a case study for reactive nitrogen deposition in north-western Europe using LOTOS-EUROS v2.0

The effect of satellite derived leaf area index and roughness length information on modelled reactive nitrogen deposition in north-western Europe

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