Coarse scale in situ albedo observations over heterogeneous snow-free land surfaces and validation strategy: A case of MODIS albedo products preliminary validation over northern China

Wu, Xiaodan; Wen, Jianguang; Xiao, Qing; Liu, Qiang; Peng, Jingjing; Dou, Baojuan; Li, Xiuhong; You, Dongqin; Tang, Yong; Liu, Qinhuo

doi:10.1016/j.rse.2016.06.013

Cited by 45 publications

(57 citation statements)

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“…Fortunately, a similar topic has been addressed by other researchers [51], although their research area and target parameter are different. In this paper, we simply used all the valid WATERNET data.…”

Section: Discussionmentioning

confidence: 95%

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

Zhang

Yang

et al. 2017

Sensors

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In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation.

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Section: Discussionmentioning

confidence: 95%

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

Zhang

Yang

et al. 2017

Sensors

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“…The information of atmospheric optical depth was extracted from MODIS 10 km aerosol products (Collection V005, at 550 nm). The high accuracy of HJ albedo has been demonstrated in (Wu et al, ).

A_{HJ_pixel} = \sum_{i = 1}^{N} α_{HJ_in situ, i} w_{i}

where A HJ_pixel is the simulated coarse pixel‐scale albedo.…”

Section: Experimental Data and Processingmentioning

confidence: 95%

“…The study area covers a 2‐km × 2‐km area within the scope between 115.778–115.799°E and 40.348–40.361°N (Figure ). The ultimate goal of this study area is originally designed to provide in situ ‐ based coarse pixel‐scale albedo through the joint observations at multiple locations with an automatic measuring system based on the WSN technology (Wu et al, , ). Therefore, it meets the requirements of providing both point‐scale and coarse pixel‐scale in situ observations to achieve the research goal of this study.…”

Section: Experimental Data and Processingmentioning

confidence: 99%

“…Although the WSN node can only provide point‐scale observations, the combination of multiple WSN nodes can provide coarse pixel‐scale albedo by weighting the multinode observations with the upscaling transfer function (Wu et al, , )

A_{pixel} = \sum_{i = 1}^{N} α_{i} w_{i}

where A pixel represents in situ‐based coarse pixel‐scale albedo, α i denotes i th WSN node albedo measurement, and w i is the upscaling weight coefficient of the i th node, which can be derived using high spatial resolution Huan Jing (HJ) albedo maps covering the year from 2010 to 2012 (equation ). The HJ albedo was generated with the angular bin algorithm from HJ CCD images (Liang, 2003; Qu et al, ), provided along with white sky albedo (WSA) and black sky albedo (BSA) at a spatial resolution of 30 m. HJ blue sky albedo was derived by weighting BSA and WSA with the fraction of diffuse radiation, which is a function of solar zenith angle and atmospheric optical depth.…”

Section: Experimental Data and Processingmentioning

confidence: 99%

“…The w i was derived from 30‐m HJ albedo, while the footprint of the WSN node was always smaller than 30 m. Therefore, only the WSN node representative of 30‐m HJ pixel albedo could be used to calculate the coarse pixel‐scale albedo using equation . Therefore, the w i was calculated for all possible combinations of WSN nodes and stored in a look up table considering the possibility that a WSN node was not able to provide effective observations at a certain time due to the limited spatial representativeness or instrument failure (Wu et al, , ).…”

Section: Experimental Data and Processingmentioning

confidence: 99%

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Direct Comparison and Triple Collocation: Which Is More Reliable in the Validation of Coarse‐Scale Satellite Surface Albedo Products

Xiao

Wen

et al. 2019

JGR Atmospheres

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A rigorous and reliable validation is the prerequisite and basis to properly understand the accuracy of satellite surface albedo products. However, the representativeness error of point scale in situ measurements, which mainly results from the spatial mismatch between in situ and satellite measurements as well as the surface heterogeneities, generally hinders an effective validation of satellite surface albedo products based on the conventional direct comparison method. Triple collocation technique has been recognized as a powerful tool to estimate the errors of satellite products without requiring the truth. Here, we evaluated the effectiveness of both the direct comparison and extended triple collocation (ETC) methods in tackling with the representativeness error of in situ measurements and characterizing the performances of satellite surface albedo products (MCD43A3 [V006], GLASS, and NPP VIIRS albedos) given whether the in situ‐based pixel‐scale albedo was available or not. The ETC method is shown to be advantageous over the direct comparison based on single “point” in situ measurements, because it is less affected by representativeness errors. However, it shall not substitute for the direct comparison method based on in situ‐based pixel‐scale albedo measurements, because its effectiveness in solving representativeness errors and estimating product errors is very limited and dependent on the choice of the data in the triplets. Furthermore, the ETC technique cannot be used to estimate the true physical errors of satellite surface albedo products.

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Assessment of NPP VIIRS Albedo Over Heterogeneous Crop Land in Northern China

Wen

Xiao

et al. 2017

JGR Atmospheres

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In this paper, the accuracy of Suomi National Polar‐orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) land surface albedo, which is derived from the direct estimation algorithm, was assessed using ground‐based albedo observations from a wireless sensor network over a heterogeneous cropland in the Huailai station, northern China. Data from six nodes spanning 2013–2014 over vegetation, bare soil, and mixed terrain surfaces were utilized to provide ground reference at VIIRS pixel scale. The performance of VIIRS albedo was also compared with Global LAnd Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) albedos (Collection 5 and 6). The results indicate that the current granular VIIRS albedo has a high accuracy with a root‐mean‐square error of 0.02 for typical land covers. They are significantly correlated with ground references indicated by a correlation coefficient (R) of 0.73. The VIIRS albedo shows distinct advantages to GLASS and MODIS albedos over bare soil and mixed‐cover surfaces, while it is inferior to the other two products over vegetated surfaces. Furthermore, its time continuity and the ability to capture the abrupt change of surface albedo are better than that of GLASS and MODIS albedo.

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Coarse scale in situ albedo observations over heterogeneous snow-free land surfaces and validation strategy: A case of MODIS albedo products preliminary validation over northern China

Cited by 45 publications

References 46 publications

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

Direct Comparison and Triple Collocation: Which Is More Reliable in the Validation of Coarse‐Scale Satellite Surface Albedo Products

Assessment of NPP VIIRS Albedo Over Heterogeneous Crop Land in Northern China

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