An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types

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Publicado en:Remote Sensing vol. 17, no. 1 (2025), p. 123
Autor principal: Belmonte, Antonella
Otros Autores: Riefolo, Carmela, Buttafuoco, Gabriele, Castrignanò, Annamaria
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MDPI AG
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100 1 |a Belmonte, Antonella  |u National Research Council of Italy, Institute for Electromagnetic Sensing of the Environment (CNR-IREA), Via Amendola 122/D, 70126 Bari, Italy; <email>castrignano.a@irea.cnr.it</email> 
245 1 |a An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important source of spatial data at multiple scales. A crucial problem facing us is the fusion of multi-source spatial data of different natures and characteristics, among which there is the support size of measurement that unfortunately is little considered in RS. A data fusion approach of both sample (point) and grid (areal) data is proposed that explicitly takes into account spatial correlation and change of support in both increasing support (upscaling) and decreasing support (downscaling). The techniques of block cokriging and kriging downscaling were employed for the implementation of such an approach, respectively. The method is applied to soil sample data, jointly analysed with hyperspectral data measured in the laboratory, UAV, and satellite data (Planet and Sentinel 2) of an olive grove after filtering soil pixels. Each data type had its own support that was transformed to the same support as the soil sample data so that the data fusion approach could be applied. To demonstrate the statistical, as well as practical, effectiveness of such a method, it was compared by a cross-validation test with a univariate approach for predicting each soil property. The positive results obtained should stimulate advanced statistical techniques to be applied more and more widely to RS data. 
653 |a Environmental monitoring 
653 |a Data processing 
653 |a Soil testing 
653 |a Soil properties 
653 |a Remote sensing 
653 |a Remote monitoring 
653 |a Geostatistics 
653 |a Data integration 
653 |a Statistical models 
653 |a Statistical analysis 
653 |a Land cover 
653 |a Spatial data 
653 |a Soil filters 
653 |a Sample variance 
653 |a Information processing 
653 |a Soil analysis 
653 |a Multisensor fusion 
700 1 |a Riefolo, Carmela  |u CREA-AA—Council for Agricultural Research and Economics, Via Celso Ulpiani, 5, 70125 Bari, Italy; <email>carmela.riefolo@crea.gov.it</email> 
700 1 |a Buttafuoco, Gabriele  |u National Research Council of Italy, Institute for Agriculture and Forestry Systems in the Mediterranean, 87036 Rende, Italy; <email>gabriele.buttafuoco@cnr.it</email> 
700 1 |a Castrignanò, Annamaria  |u National Research Council of Italy, Institute for Electromagnetic Sensing of the Environment (CNR-IREA), Via Amendola 122/D, 70126 Bari, Italy; <email>castrignano.a@irea.cnr.it</email> 
773 0 |t Remote Sensing  |g vol. 17, no. 1 (2025), p. 123 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3153688569/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3153688569/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3153688569/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch