Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

doi:10.1016/j.isprsjprs.2014.12.016

Cited by 37 publications

(19 citation statements)

References 26 publications

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“…Cokriging is a useful approach because it is based on a well-established geostatistical theory and provides a framework to combine different types of data [23,24]. However, the application of the approach has been limited, until recently [40]. The results show that image downscaling using cokriging can be applied in vegetation studies and is useful for the analysis of multi-temporal data.…”

Section: Discussionmentioning

confidence: 99%

Mapping of Vegetation Using Multi-Temporal Downscaled Satellite Images of a Reclaimed Area in Saemangeum, Republic of Korea

et al. 2017

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The aim of this study is to adapt and evaluate the effectiveness of a multi-temporal downscaled images technique for classifying the typical vegetation types of a reclaimed area. The areas reclaimed from estuarine tidal flats show high spatial heterogeneity in soil salinity conditions. There are three typical vegetation types for which the distribution is restricted by the soil conditions. A halophyte-dominated vegetation is located in a high saline area, grass vegetation is found in a mid-or low saline area, and reed/small-reed vegetation is situated in a low saline area. Multi-temporal satellite images were used to classify the vegetation types. Landsat images were downscaled to take into account spatial heterogeneity using cokriging. A random forest classifier was used for the classification, with downscaled Landsat and RapidEye images. Classification with RapidEye images alone demonstrated a lower level of accuracy than when combined with multi-temporal downscaled images. The results demonstrate the usefulness of a downscaling technique for mapping. This approach can provide a framework which is able to maintain low costs whilst producing richer images for the monitoring of a large and heterogeneous ecosystem.

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Section: Discussionmentioning

confidence: 99%

Mapping of Vegetation Using Multi-Temporal Downscaled Satellite Images of a Reclaimed Area in Saemangeum, Republic of Korea

et al. 2017

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“…There have been many studies on the spatial downscaling of precipitation [18,19,40], soil moisture [35,[41][42][43][44], land surface temperature [45][46][47][48], and so on. The basic idea of these methods is to use the spatial variation characteristics reflected by high-resolution auxiliary data to improve the spatial resolution of these surface parameters.…”

Section: The Relationship Between Precipitation and Soil Moisturementioning

confidence: 99%

“…Thereafter, Lloyd et al [10], Duan et al [11], Park et al [12], and Fang et al [13] established a linear or exponential regression model between precipitation, NDVI, and digital elevation model (DEM) to achieve the spatial downscaling of TRMM. Later, the wavelet [14], multifractal [15], Bayesian model [16], area-to-point kriging (ATPK) [17][18][19][20][21], geographic weight regression methods (GWR) [18,[22][23][24][25][26], random forests (RF) method [5,27,28], support vector machine (SVM) [29], and artificial neural network method [30] were also introduced into the spatial downscaling of TRMM data by establishing a statistical relationship between TRMM data and environmental parameters, such as NDVI, DEM, latitude, longitude, slope, aspect, land surface temperature, and so on [31][32][33][34]. However, these spatial downscaling methods are only available on an annual scale, because environmental variables, such as vegetation and DEM, usually show a long-term distribution of precipitation.…”

Section: Introductionmentioning

confidence: 99%

A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data

Fan

Dong³

et al. 2019

Remote Sensing

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Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the "hysteresis phenomenon" of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets.

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“…A number of alternative methods including the SIMPAT algorithm [17], unilateral path algorithm [18] and FILTERSIM algorithm [19], have also been proposed to deal with both categorical and continuous variables. To date, MPS has been widely used for the reconstruction of complex geological structures [20][21][22] and in more recent years, has been applied to analyze remote sensing land cover classification [23,24] as well as downscaling applications [25,26]. As such, the method is anticipated to serve as an efficient approach to fill gaps within remote sensing imagery, caused for instance by cloud cover and orbital characteristics [27] or, as in the case to be explored here, instrument failure.…”

Section: Introductionmentioning

confidence: 99%

Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method

2016

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Abstract:The failure of the Scan Line Corrector (SLC) on Landsat 7 imposed systematic data gaps on retrieved imagery and removed the capacity to provide spatially continuous fields. While a number of methods have been developed to fill these gaps, most of the proposed techniques are only applicable over relatively homogeneous areas. When they are applied to heterogeneous landscapes, retrieving image features and elements can become challenging. Here we present a gap-filling approach that is based on the adoption of the Direct Sampling multiple-point geostatistical method. The method employs a conditional stochastic resampling of known areas in a training image to simulate unknown locations. The approach is assessed across a range of both homogeneous and heterogeneous regions. Simulation results show that for homogeneous areas, satisfactory results can be obtained by simply adopting non-gap locations in the target image as baseline training data. For heterogeneous landscapes, bivariate simulations using an auxiliary variable acquired at a different date provides more accurate results than univariate simulations, especially as land cover complexity increases. Apart from recovering spatially continuous fields, one of the key advantages of the Direct Sampling is the relatively straightforward implementation process that relies on relatively few parameters.

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Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

Cited by 37 publications

References 26 publications

Mapping of Vegetation Using Multi-Temporal Downscaled Satellite Images of a Reclaimed Area in Saemangeum, Republic of Korea

Mapping of Vegetation Using Multi-Temporal Downscaled Satellite Images of a Reclaimed Area in Saemangeum, Republic of Korea

A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data

Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method

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