Integrating spectral and textural attributes to measure magnitude in object-based change vector analysis

Sun, Hao; Zhou, Wei; Zhang, Yixiu; Cai, Chuangchuang; Chen, Qiang

doi:10.1080/01431161.2019.1582111

Cited by 8 publications

(11 citation statements)

References 29 publications

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“…The barren module estimates the LEE based on a complementary hypothesis that surface soil moisture dynamic can be indicated by surface meteorological variables at mid-day conditions [20]. LEE is estimated in Barren module by the following equation:…”

Section: Barren Modulementioning

confidence: 99%

“…Note that the parameter θ CRIT is critical to derive the higher-resolution SM. It has been recognized that the parameter θ CRIT is an effective parameter rather than a pure physical parameter when the above equation was applied with remote sensing data over larger spatiotemporal domain [20]. The parameter θ CRIT can be determined from given coarser-resolution SM (θ CR ) and aggregated coarser-resolution LEE (LEE CR ) data pairs.…”

Section: Downscaling Modulementioning

confidence: 99%

“…The agreement of both the original and downscaled SM data with in situ SM data is better in Little Washita and Fort Cobb than in South Fork. Land cover type may be one of the main causes for the performance difference [20,46]. The sites of Little Washita and Fort Cobb are dominated by the land cover of grassland, while the South Fork site is mainly covered by cropland.…”

Section: Comparison Against In Situ Sm At Cvsmentioning

confidence: 99%

“…The former fusion methods are limited by the availability of matched active and passive microwave data. For example, after the SMAP's active radar sensor stopped working, some researchers sought to incorporate C-band active microwave data from Sentinel-1 satellite to downscale the SMAP L-band passive microwave data [14,20]. However, this approach faces some technical challenges due to data latency, spatial coverage, revisit frequency, penetration depth, and retrieval performance [21].…”

Section: Introductionmentioning

confidence: 99%

“…The above problems have severely hampered the development and extensive application of the downscaling methods that rely on LST and LST-derived SMIs. [12,20].…”

Section: Introductionmentioning

confidence: 99%

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DSCALE_mod16: A Model for Disaggregating Microwave Satellite Soil Moisture with Land Surface Evapotranspiration Products and Gridded Meteorological Data

et al. 2020

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Improving the spatial resolution of microwave satellite soil moisture (SM) products is important for various applications. Most of the downscaling methods that fuse optical/thermal and microwave data rely on remotely sensed land surface temperature (LST) or LST-derived SM indexes (SMIs). However, these methods suffer from the problems of “cloud contamination”, “decomposing uncertainty”, and “decoupling effect”. This study presents a new downscaling method, referred to as DSCALE_mod16, without using LST and LST-derived SMIs. This model combines MODIS ET products and a gridded meteorological data set to obtain Land surface Evaporative Efficiency (LEE) as the main downscaling factor. A cosine-square form of downscaling function was adopted to represent the quantitative relationship between LEE and SM. Taking the central part of the United States as the case study area, we downscaled SMAP (Soil Moisture Active and Passive) SM products with an original resolution of 36km to a resolution of 500m. The study period spans more than three years from 2015 to 2018. In situ SM measurements from three sparse networks and three core validation sites (CVS) were used to evaluate the downscaling model. The evaluation results indicate that the downscaled SM values maintain the spatial dynamic range of original SM data while providing more spatial details. Moreover, the moisture mass is conserved during the downscaling process. The downscaled SM values have a good agreement with in situ SM measurements. The unbiased root-mean-square errors (ubRMSEs) of downscaled SM values is 0.035 m3/m3 at Fort Cobb, 0.026 m3/m3 at Little Washita, and 0.055 m3/m3 at South Fork, which are comparable to ubRMSEs of original SM estimates at these three CVS.

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Section: Barren Modulementioning

confidence: 99%

Section: Downscaling Modulementioning

confidence: 99%

Section: Comparison Against In Situ Sm At Cvsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The above problems have severely hampered the development and extensive application of the downscaling methods that rely on LST and LST-derived SMIs. [12,20].…”

Section: Introductionmentioning

confidence: 99%

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