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Article ; Online: Soil Moisture Estimate Uncertainties from the Effect of Soil Texture on Dielectric Semiempirical Models

Jing Liu / Qinhuo Liu

Remote Sensing, Vol 12, Iss 2343, p

2020 Volume 2343

Abstract: Soil texture has been shown to affect the dielectric behavior of soil over the entire frequency range. Three universally employed dielectric semiempirical models (SEMs), the Dobson model, the Wang–Schmugge model and the Mironov model, as well as a new ... ...

Abstract	Soil texture has been shown to affect the dielectric behavior of soil over the entire frequency range. Three universally employed dielectric semiempirical models (SEMs), the Dobson model, the Wang–Schmugge model and the Mironov model, as well as a new improved SEM known as the soil semi-empirical mineralogy-related-to-water dielectric model (SSMDM), incorporate a significant soil texture effect in different ways. In this paper, soil moisture estimate uncertainties from the effect of soil texture on these four SEMs are systematically and widely investigated over all soil texture cases at different frequencies between 1.4 and 18 GHz for volumetric water content levels between 0.0 and 0.4 m 3 /m 3 from the perspective of two aspects: soil dielectric model discordance and soil texture discordance. Firstly, the effect of soil texture on these four dielectric SEMs is analyzed. Then, soil moisture estimate uncertainties due to the effect of soil texture are carefully investigated. Finally, the applicability of these SEMs is discussed, which can supply references for their choice. The results show that soil moisture estimate uncertainties are small and satisfy the 4% volumetric water content retrieval requirement in some cases. However, in other cases, it may contribute relatively significant uncertainties to soil moisture estimates and correspond to a difference that exceeds the 4% volumetric water content requirement, with potential for the largest deviations to exceed 0.22 m 3 /m 3 .
Keywords	microwave remote sensing ; moist soils ; soil complex dielectric permittivity ; soil moisture ; soil texture ; Science ; Q
Subject code	630
Language	English
Publishing date	2020-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Regional Sampling of Forest Canopy Covers Using UAV Visible Stereoscopic Imagery for Assessment of Satellite-Based Products in Northeast China

Tianyu Yu / Wenjian Ni / Zhiyu Zhang / Qinhuo Liu / Guoqing Sun

Journal of Remote Sensing, Vol

2022 Volume 2022

Abstract: Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable ... ...

Abstract	Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable references for accuracy assessments. The rapid development of unmanned aerial vehicle (UAV) equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost, which provides the research community a promising tool to collect reference data. However, it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images (RGB) due to the limited spectral information. In addition, the canopy height model (CHM) derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations. This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience. The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map (DOM) and structural information from digital surface model (DSM). Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km2 across Daxing’anling forested area in northeast of China. Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient (r) of 0.96 and root mean square error (RMSE) of 5.7%. Then, the UAV-based canopy covers served as references for assessment of satellite-based maps, including MOD44B Version 6 Vegetation Continuous Fields (MODIS VCF), maps developed by the Global Land Cover Facility (GLCF) and by the Global Land Analysis and Discovery laboratory (GLAD). Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns, but GLAD canopy cover tended to miss scattered trees in ...
Keywords	Environmental sciences ; GE1-350 ; Physical geography ; GB3-5030
Subject code	333
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	American Association for the Advancement of Science (AAAS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Generation of a 16 m/10-day fractional vegetation cover product over China based on Chinese GaoFen-1 observations

Jing Zhao / Jing Li / Qinhuo Liu / Baodong Xu / Xihan Mu / Yadong Dong

International Journal of Digital Earth, Vol 16, Iss 2, Pp 4229-

method and validation

2023 Volume 4246

Abstract: As China has recently launched the GaoFen-1 satellite (GF-1) carrying on the wide-field view (WFV) sensor, it is a challenging task to make full use of its observations to produce the fractional vegetation cover (FVC). In light of this, our study ... ...

Abstract	As China has recently launched the GaoFen-1 satellite (GF-1) carrying on the wide-field view (WFV) sensor, it is a challenging task to make full use of its observations to produce the fractional vegetation cover (FVC). In light of this, our study presents a comprehensive algorithm to generate a 16 m/10-day FVC product by considering the vegetation types characteristics. For forests, considering the foliage clumping effect, FVC was estimated from the gap probability theory using GF-1 leaf area index (LAI) and clumping index (CI) as a priori knowledge; for non-forests, FVC was estimated from the dimidiate pixel model using GF-1 normalized difference vegetation index (NDVI). The performance of GF-1 FVC from 2018 to 2020 was evaluated using FVC ground measurements obtained from 7 sites for crops, grasslands, and forests in China. The direct validation indicated that the performance of the FVC product was satisfactory, as evidenced by R2 = 0.55, RMSE = 0.15 and BIAS = 0.01 for all vegetation types. Furthermore, the GF-1 FVC exhibited better performance compared to the GEOV3 FVC at a spatial resolution of 300 meters. Moreover, the 10-day temporal interval of GF-1 FVC product successfully facilitated the extraction of regional phenological information at a spatial resolution of 16 meters.
Keywords	fvc ; gf-1 wfv ; gap probability theory ; dimidiate pixel model ; Mathematical geography. Cartography ; GA1-1776
Subject code	910
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Cloud Water Path-Based Model for Cloudy-Sky Downward Longwave Radiation Estimation from FY-4A Data

Shanshan Yu / Xiaozhou Xin / Hailong Zhang / Li Li / Lin Zhu / Qinhuo Liu

Remote Sensing, Vol 15, Iss 23, p

2023 Volume 5531

Abstract: Clouds are a critical factor in regulating the climate system, and estimating cloudy-sky Surface Downward Longwave Radiation (SDLR) from satellite data is significant for global climate change research. The models based on cloud water path (CWP) are less ...

Abstract	Clouds are a critical factor in regulating the climate system, and estimating cloudy-sky Surface Downward Longwave Radiation (SDLR) from satellite data is significant for global climate change research. The models based on cloud water path (CWP) are less affected by cloud parameter uncertainties and have superior accuracy in SDLR satellite estimation when compared to those empirical and parameterized models relying mainly on cloud fraction or cloud-base temperature. However, existing CWP-based models tend to overestimate the low SDLR values and underestimate the larger SDLR. This study found that this phenomenon was caused by the fact that the models do not account for the varying relationships between cloud radiative effects and key parameters under different Liquid Water Path (LWP) and Precipitable Water Vapor (PWV) ranges. Based upon this observation, this study utilized Fengyun-4A (FY-4A) cloud parameters and ERA5 data as data sources to develop a new CWP-based model where the model coefficients depend on the cloud phase and cloud water path range. The accuracy of the new model’s estimated SDLR is 20.8 W/m 2 for cloudy pixels, with accuracies of 19.4 W/m 2 and 23.5 W/m 2 for overcast and partly cloudy conditions, respectively. In contrast, the accuracy of the old CWP-based model was 22.4, 21.2, and 24.8 W/m 2 , respectively. The underestimation and overestimation present in the old CWP-based model are effectively corrected by the new model. The new model exhibited higher accuracy under various station locations, cloud cover scenarios, and cloud phase conditions compared to the old one. Comparatively, the new model showcased its most remarkable improvements in situations involving overcast conditions, water clouds with low PWV and low LWP values, ice clouds with large PWV, and conditions with PWV ≥ 5 cm. Over a temporal scale, the new model effectively captured the seasonal variations in SDLR.
Keywords	cloud water path ; downward longwave radiation ; FY-4A ; cloudy sky ; overcast ; partly cloudy ; Science ; Q
Subject code	333
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Extending the GOSAILT Model to Simulate Sparse Woodland Bi-Directional Reflectance with Soil Reflectance Anisotropy Consideration

Juan Cheng / Jianguang Wen / Qing Xiao / Shengbiao Wu / Dalei Hao / Qinhuo Liu

Remote Sensing, Vol 14, Iss 1001, p

2022 Volume 1001

Abstract: Anisotropic canopy reflectance plays a crucial role in estimating vegetation biophysical parameters, whereas soil reflectance anisotropy affects canopy reflectance. However, woodland canopy bidirectional reflectance distribution function (BRDF) models ... ...

Abstract	Anisotropic canopy reflectance plays a crucial role in estimating vegetation biophysical parameters, whereas soil reflectance anisotropy affects canopy reflectance. However, woodland canopy bidirectional reflectance distribution function (BRDF) models considering soil anisotropy are far from universal, especially for the BRDF models of mountain forest. In this study, a mountain forest canopy model, named geometric-optical and mutual shadowing and scattering from arbitrarily inclined-leaves model coupled with topography (GOSAILT), was extended to consider the soil anisotropic reflectance characteristics by introducing the simple soil directional (SSD) reflectance model. The modified GOSAILT model (named GOSAILT-SSD) was evaluated using unmanned aerial vehicle (UAV) field observations and discrete anisotropic radiative transfer (DART) simulations. Then, the effects of Lambertian soil assumption on simulating the vi-directional reflectance factor (BRF) were evaluated across different fractions of vegetation cover (Cv), view zenith angles (VZA), solar zenith angles (SZA), and spectral bands with the GOSAILT-SSD model. The evaluation results, with the DART simulations, show that the performance of the GOSAILT-SSD model in simulating canopy BRF is significantly improved, with decreasing RMSE, from 0.027 to 0.017 for the red band and 0.051 to 0.037 for the near-infrared (NIR) band. Meanwhile, the GOSAILT-SSD simulations show high consistency with UAV multi-angular observations (R 2 = 0.97). Besides, it is also found that the BRF simulation errors caused by Lambertian soil assumption are too large to be neglected, with a maximum relative bias of about 45% for the red band. This inappropriate assumption results in a remarkable BRF underestimation near the hot spot direction and an obvious BRF overestimation for large VZA in the solar principal plane (PP). Meanwhile, this simulation bias decreases with the increase of fraction of vegetation cover. This study provides an effective technique to improve the capability of the ...
Keywords	canopy BRF ; soil reflectance ; sloping terrain ; forest ; Lambertian assumption ; Science ; Q
Subject code	333
Language	English
Publishing date	2022-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Global Comparison of Leaf Area Index Products over Water-Vegetation Mixed Heterogeneous Surface Network (HESNet-WV)

Chang Liu / Jing Li / Qinhuo Liu / Baodong Xu / Yadong Dong / Jing Zhao / Faisal Mumtaz / Chenpeng Gu / Hu Zhang

Remote Sensing, Vol 15, Iss 1337, p

2023 Volume 1337

Abstract: Spatial land surface heterogeneities are widespread at various scales and represent a great challenge to leaf area index (LAI) retrievals and product validations. In this paper, considering the mixed water and vegetation pixels prevalent at moderate and ... ...

Abstract	Spatial land surface heterogeneities are widespread at various scales and represent a great challenge to leaf area index (LAI) retrievals and product validations. In this paper, considering the mixed water and vegetation pixels prevalent at moderate and low resolutions, we propose a methodological framework for conducting global comparisons of heterogeneous land surfaces based on criterion setting and a global search of high-resolution data. We construct a global network, Heterogeneous Surface Network aiming Water and Vegetation Mixture (HESNet-WV), comprised of three vegetation types: grassland, evergreen broadleaf forests (EBFs), and evergreen needle forests (ENFs). Validation is performed using the MCD15A3H Global 500-m/4-day and GLASS 500-m/8-day LAI products. As the water area fraction (WAF), LAI values and LAI inversion errors increase in the MODIS and GLASS products, the GLASS product errors (relative LAI error (RELAI): 94.43%, bias: 0.858) are lower than the MODIS product errors (RELAI: 124.05%, bias: 1.209). The result indicates that the proposed framework can be applied to evaluate the accuracy of LAI values in mixed water-vegetation pixels in different global LAI products.
Keywords	validation ; LAI ; mixed pixel ; spatial heterogeneity ; Science ; Q
Subject code	333
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Modeling the Stereoscopic Features of Mountainous Forest Landscapes for the Extraction of Forest Heights from Stereo Imagery

Wenjian Ni / Zhiyu Zhang / Guoqing Sun / Qinhuo Liu

Remote Sensing, Vol 11, Iss 10, p

2019 Volume 1222

Abstract: Spaceborne stereoscopic systems have been growing in recent years, and the point cloud extracted from spaceborne stereo imagery has been used to measure forest spatial structures. These systems work on different viewing angles and image spatial ... ...

Abstract	Spaceborne stereoscopic systems have been growing in recent years, and the point cloud extracted from spaceborne stereo imagery has been used to measure forest spatial structures. These systems work on different viewing angles and image spatial resolutions, which are two critical factors determining the quality of the derived point cloud. In addition, the complex terrain is also a great challenge for the regional mapping of forest spatial structures using spaceborne stereo imagery. Although several theoretical models for simulating multi-view spectral features of forest canopies have been developed, there is hardly any report of a stereoscopic analysis using these models due to the limited size of the simulated forest scenes and the lack of a geometric sensory model (i.e., physical relationship between two-dimensional image coordinates and three-dimensional georeferenced coordinates). The stereoscopic features (i.e., parallax) are, as important as the spectral features contained in the multi-view images of a targeted area, the basis for the extraction of a point cloud. In this study, a new model, referred to as LandStereo model, has been proposed, which is capable of simulating the stereoscopic features of forest canopies over mountainous areas at landscape scales. The model comprised five parts, including defining the mountainous forest landscapes, setting the sun-senor observation geometry, simulating images, generating ground control points, and building geometric sensor models. The LandStereo model was validated over three different scenes, including flat forest landscapes, bare mountain landscapes, and mountainous forest landscapes. The results clearly demonstrated that the LandStereo model worked well on simulating stereoscopic features of both terrains and forest canopies at landscape scales. The extracted height of a forest canopy top from simulated stereo imagery was highly correlated to the truth (R 2 = 0.96 and RMSE = 0.99 m) over the flat terrains and (R 2 = 0.92 and RMSE = 1.15 m) over the ...
Keywords	forest ; height ; biomass ; photogrammetry ; stereo imagery ; multiview ; optical ; modeling ; multiangular ; POV-Ray ; Science ; Q
Subject code	910
Language	English
Publishing date	2019-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comparison between Physical and Empirical Methods for Simulating Surface Brightness Temperature Time Series

Zunjian Bian / Yifan Lu / Yongming Du / Wei Zhao / Biao Cao / Tian Hu / Ruibo Li / Hua Li / Qing Xiao / Qinhuo Liu

Remote Sensing, Vol 14, Iss 14, p

2022 Volume 3385

Abstract: Land surface temperature (LST) is a vital parameter in the surface energy budget and water cycle. One of the most important foundations for LST studies is a theory to understand how to model LST with various influencing factors, such as canopy structure, ...

Abstract	Land surface temperature (LST) is a vital parameter in the surface energy budget and water cycle. One of the most important foundations for LST studies is a theory to understand how to model LST with various influencing factors, such as canopy structure, solar radiation, and atmospheric conditions. Both physical-based and empirical methods have been widely applied. However, few studies have compared these two categories of methods. In this paper, a physical-based method, soil canopy observation of photochemistry and energy fluxes (SCOPE), and two empirical methods, random forest (RF) and long short-term memory (LSTM), were selected as representatives for comparison. Based on a series of measurements from meteorological stations in the Heihe River Basin, these methods were evaluated in different dimensions, i.e., the difference within the same surface type, between different years, and between different climate types. The comparison results indicate a relatively stable performance of SCOPE with a root mean square error (RMSE) of approximately 2.0 K regardless of surface types and years but requires many inputs and a high computational cost. The empirical methods performed relatively well in dealing with cases either within the same surface type or changes in temporal scales individually, with an RMSE of approximately 1.50 K, yet became less compatible in regard to different climate types. Although the overall accuracy is not as stable as that of the physical method, it has the advantages of fast calculation speed and little consideration of the internal structure of the model.
Keywords	land surface temperature ; radiative transfer ; random forest regression ; LSTM ; SCOPE ; Science ; Q
Subject code	550
Language	English
Publishing date	2022-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An Improved Parameterization for Retrieving Clear-Sky Downward Longwave Radiation from Satellite Thermal Infrared Data

Shanshan Yu / Xiaozhou Xin / Qinhuo Liu / Hailong Zhang / Li Li

Remote Sensing, Vol 11, Iss 4, p

2019 Volume 425

Abstract: Surface downward longwave radiation (DLR) is a crucial component in Earth’s surface energy balance. Yu et al. (2013) developed a parameterization for retrieving clear-sky DLR at high spatial resolution by combined use of satellite thermal infrared (TIR) ... ...

Abstract	Surface downward longwave radiation (DLR) is a crucial component in Earth’s surface energy balance. Yu et al. (2013) developed a parameterization for retrieving clear-sky DLR at high spatial resolution by combined use of satellite thermal infrared (TIR) data and column integrated water vapor (IWV). We extended the Yu2013 parameterization to Moderate Resolution Imaging Spectroradiometer (MODIS) data based on atmospheric radiative simulation, and we modified the parameterization to decrease the systematic negative biases at large IWVs. The new parameterization improved DLR accuracy by 1.9 to 3.1 W/m 2 for IWV ≥3 cm compared to the Yu2013 algorithm. We also compared the new parameterization with four algorithms, including two based on Top-of-Atmosphere (TOA) radiance and two using near-surface meteorological parameters and water vapor. The algorithms were first evaluated using simulated data and then applied to MODIS data and validated using surface measurements at 14 stations around the globe. The results suggest that the new parameterization outperforms the TOA-radiance based algorithms in the regions where ground temperature is substantially different (enough that the difference between them is as large as 20 K) from skin air temperature. The parameterization also works well at high elevations where atmospheric parameter-based algorithms often have large biases. Furthermore, comparing different sources of atmospheric input data, we found that using the parameters interpolated from atmospheric reanalysis data improved the DLR estimation by 7.8 W/m 2 for the new parameterization and 19.1 W/m 2 for other algorithms at high-altitude sites, as compared to MODIS atmospheric products.
Keywords	parameterization ; brightness temperature ; water vapor content ; ground-air temperature difference ; MODIS ; downward longwave radiation ; Science ; Q
Subject code	006 ; 333
Language	English
Publishing date	2019-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory

Jing Zhao / Jing Li / Qinhuo Liu / Baodong Xu / Wentao Yu / Shangrong Lin / Zhang Hu

International Journal of Applied Earth Observations and Geoinformation, Vol 90, Iss , Pp 102112- (2020)

2020

Abstract: Gap probability theory provides a theoretical equation to calculate fractional vegetation cover (FVC). However, the main algorithms used in present FVC products generation are still the linear mixture model and machine learning methods. The reason to ... ...

Abstract	Gap probability theory provides a theoretical equation to calculate fractional vegetation cover (FVC). However, the main algorithms used in present FVC products generation are still the linear mixture model and machine learning methods. The reason to limit the gap probability theory applied in the product algorithm is the availability and accuracy of leaf area index (LAI) and clumping index (CI) products. With the improvement of the LAI and CI products, it is necessary to assess whether the algorithm based on gap probability theory using the present products can improve the accuracy of FVC products. In this study, we generated the FVC estimates based on the gap probability theory (FVCgap) with a resolution of 500 m every 8 days for Europe. FVCgap estimates were validated with field FVC measurements of ImagineS from 2013 to 2015 for crop types. Two existing FVC products, Geoland2 Version1 (GEOV1) and Multisource data Synergized Quantitative remote sensing production system (MuSyQ), were used to inter-compare with the FVCgap estimates. FVCgap estimates showed a better agreement with field FVC measurements, with lowest root mean square error (RMSE) (0.1211) and bias (0.0224), than GEOV1 and MuSyQ FVC products. The inter-annual and seasonal variations of FVCgap estimates were also showed the most consistent with field measurements.
Keywords	FVC ; LAI ; CI ; GEOV1 ; MuSyQ ; Physical geography ; GB3-5030 ; Environmental sciences ; GE1-350
Subject code	333
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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