A modified vegetation index based algorithm for thermal imagery sharpening

Chen, Ling; Yan, Guangjian; Ren, Huazhong; Li, Aihua

doi:10.1109/igarss.2010.5651428

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…The RF model from Zhao et al [53] that is dependent on the LST/VIs feature space was proposed for relatively high vegetation cover conditions, and the importance scores of VIs in their study were very high. However, the feature space performed relatively poorly in areas with NDVI values ranging from 0 to 0.30 [59,60], and the LST with low sensitivity to surface SM in winter would reduce the accuracy of the downscaling SM [53]. Thus, σ 0 vv with a high sensitivity to surface SM plays a more significant role than the other variables in this study.…”

Section: Analysis Of Input Variables In the Rf Modelsmentioning

confidence: 76%

“…Additionally, the triangular/trapezoidal feature space among LST, VIs, and SM can also contribute to the relationship model. Although some RF models that are mainly dependent on LST/VIs feature space have been developed, feature space is relatively unstable in low vegetation cover conditions [53,59,60]. The surface elevation and slope related to land surface energy and surface backscatter were used for constructing models [38,53].…”

Section: Introductionmentioning

confidence: 99%

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An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

et al. 2019

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A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1-RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1-RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active-passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical-vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions. 2 of 20 salinity (SMOS) satellites [10] based on L-band passive microwaves can provide high-accuracy global daily SM products [16]. However, coarse-resolution passive microwave SM data cannot reflect the detailed distribution of surface SM; therefore, many researchers have downscaled coarse-resolution passive microwave SM data based on fine-resolution auxiliary data [17][18][19][20][21][22][23].Some downscaling methods are based on active or passive microwave data including downscaling coarse-resolution microwave brightness temperature (TB) data or coarse-resolution SM data based on microwave backscatter data [22,[24][25][26][27][28][29][30], and downscaling low-frequency passive microwave data based on high-frequency passive microwave data [31][32][33]. Active microwave techniques offer higher spatial resolutions than passive microwave techniques. Downscaling methods based on active microwaves usually leverage the linear or near-linear relationship between microwave TB or SM data and microwave backscatter data in a time series. Vegetation and surface roughness are generally considered time-invariant during long-time series [26]. However, active microwaves are greatly affected by vegetation and surface roughness [34]; therefore, their applications in most areas are limited [35]. Another assumption is that invariance of vegetation and surface roughness occurs in adjacent observations (several days); thus, these techniques req...

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Section: Analysis Of Input Variables In the Rf Modelsmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

et al. 2019

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“…Statistical regressions connect LST with remote sensing indices, which are extracted from high-resolution, visible, near-infrared, or short-wavelength infrared bands through statistical correlations. Several vegetation indices are widely used to downscale LST effectively especially in vegetated regions; these indices include normalized difference vegetation index [20], fractal vegetation index [21][22][23], vegetation dryness index [28][29][30], and soil-adjusted vegetation index (SAVI) [26]. Various types of remote sensing indices are used in statistical regressions in other types of land surfaces; these factors include normalized difference building index (NDBI) [27] in building areas and normalized difference dust index (NDDI) [31] in bail soil areas.…”

Section: Introductionmentioning

confidence: 99%

Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression

et al. 2017

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Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in arid regions (especially in deserts) because of inaccurate remote sensing LST products. In this study, LST was downscaled by a random forest model between LST and multiple remote sensing indices (such as soil-adjusted vegetation index, normalized multi-band drought index, modified normalized difference water index, and normalized difference building index) in an arid region with an oasis-desert ecotone. The proposed downscaling approach, which involves the selection of remote sensing indices, was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The spatial resolution of MODIS LST was downscaled from 1 km to 500 m. Results of visual and quantitative analyses show that the distribution of downscaled LST matched that of the oasis and desert ecosystem. The lowest (approximately 22 • C) and highest temperatures (higher than 37 • C) were detected in the middle oasis and desert regions, respectively. Furthermore, the proposed approach achieves relatively satisfactory downscaling results, with coefficient of determination and root mean square error of 0.84 and 2.42 • C, respectively. The proposed approach shows higher accuracy and minimization of the MODIS LST in the desert region compared with other methods. Optimal availability occurs in the vegetated region during summer and autumn. In addition, the approach is also efficient and reliable for LST downscaling of Landsat images. Future tasks include reliable LST downscaling in challenging regions.

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“…Various types of downscaling techniques have been developed to get high-resolution LST from a coarse resolution thermal infrared band (Wan and Dozier, 1996; Chen et al., 2010; Pardo-Igúzquiza and Atkinson, 2006; Zhang, 2015; Yang and Yao, 2009). Extended reviews on LST downscaling methods have been assessed so far (Zhan et al., 2013; Chen et al., 2014).…”

Section: Introductionmentioning

confidence: 99%

Seasonal evaluation of downscaled land surface temperature: A case study in a humid tropical city

Govil

Guha

Dey³

et al. 2019

Heliyon

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The present study evaluates the seasonal variation of estimated error in downscaled land surface temperatures (LST) over a heterogeneous urban land. Thermal sharpening (TsHARP) downscaling algorithm has been used with a separate combination of four selected remote sensing indices. This study assesses the capability of TsHARP technique over mixed land use/land covers (LULC) by analyzing the correlation between LST and remote sensing indices, namely, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized multi-band drought index (NMDI) and by determining the root mean square error (RMSE) and mean error (ME) produced by downscaled LST. Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images have been used for pre-monsoon, monsoon, post-monsoon, and winter seasons in 2014 covering the whole Raipur City, India. The RMSE of the downscaled LST decreases from 120 to 480 m spatial resolution in all the four seasons. It is concluded that NDBI is the most effective LULC index having the least error produced in TsHARP downscaling technique, irrespective of any season. Post-monsoon season reflects the most successful result followed by monsoon season. Even in the monsoon season of high vegetation coverage, NDBI presents a lower range of downscaled error compared to NDVI. This indicates better performance of NDBI in detecting the spatial and temporal distribution of mixed urban land.

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A modified vegetation index based algorithm for thermal imagery sharpening

Cited by 9 publications

References 10 publications

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression

Seasonal evaluation of downscaled land surface temperature: A case study in a humid tropical city

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