XXII ISPRS Congress 2012: Technical Commission VII

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Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/1: PHYSICAL MODELLING AND SIGNATURES IN REMOTE SENSING]

Document type:: Multivolume work

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Chapter

Title:: GEOSTATISTICAL ANALYSIS OF SURFACE TEMPERATURE AND IN-SITU SOIL MOISTURE USING LST TIME-SERIES FROM MODIS M. Sohrabinia, W. Rack and P Zawar-Reza

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia issue with these models, however, is the complexity and the risk of compromise in accuracy if good quality data for all the impor- tant parameters in the model are not available. To overcome this issue, an assessment of the relationship between the near surface SM and LST can help to understand the complexity of these two parameters over various LC types in a particular region. Such a relationship can be helpful for approximation of one parameter (SM or LST) with the availability of the other. This paper is aimed to analyse the correlation between LST using MODIS product with the measured near-surface SM based on geostatistical methods. Daily rainfall data is used to detect the interfering effects of a sudden rainfall on the surface SM, and to discover the time-scale for the best correlation with LST. 2 DATA AND STUDY AREA 2.1 Data Two distinct datasets, one from the remotely sensed MODIS LST and the second from the in-situ SM measurements provide the inputs for this analysis. The LST product is a scientific dataset derived from MODIS moming and afternoon thermal observa- tions on-board Terra and Aqua satellites. Since the observations in thermal bands of the sensor are also available at night, the prod- uct contain night-time values for evening and midnight, which are collected upon the overpasses of Terra and Aqua satellites on the night side of the planet. The product is derived from bands 31 & 32 (spectral range 10.78-11.28 uum & 11.77-12.27 um range, re- spectively). Theoretical background and technical details of the algorithms and procedure for the extraction of LST from MODIS thermal bands is available in the literature (Wan and Dozier, 1996, Wan, 1999, Wan et al.. 2004, Wan, 2008). The dataset is available in hierarchical data format (HDF) and can be accessed online via Reverb tool. The in-situ near-surface (<5 cm depth) SM data used in this paper have been collected in five sites with various land-cover types. These data have been recorded using MadgeTech® digital soil volumetric moisture data loggers known as SMR1 108, Fre- quency rate of the logged data had been set to every 30 minute. These measurements have been collected from 1st October 2011 till 7th January 2012, however, for consistency with the other datasets only Nov. and Dec. data are used in the analysis. 2.2 Study Area The study area is located in Canterbury Plains in South Island of New Zealand (Fig. 1), at approximate geographic coordinates 43.54 S and 172.31 E in the central point. The in-situ sites were selected in the area for the measurement of the near-surface SM. Criteria for selection of the measurement sites included the area percentage of the dominant LC types, accessibility of the site and finally, a minimum of 1x1 km homogeneous extent of the dom- inant LC type in the area so that at least one pixel from that LC type to be distinguished in the LST satellite dataset. 3 METHODS 3.1 Empirical relationship between near-surface SM and LST The principle assumption in derivation of SM from LST data in physical models used in thermal remote sensing of SM, such as SEBAL, is partitioning of surface energy to latent and sensible heat fluxes (Eq. 1 and 2), and to relate the latent heat to the amount of moisture content in the near-surface layer. LST from 18 0 5 10 15 m— — M Kilom eters (OQ SMmeasurement point Study Area Soll moisture measurement sites Canterbury Plains New Zealand Figure 1: In-situ soil moisture measurement points overlaid on Landsat image (TM5, 28 March 2011) the thermal remote sensing observations is accounted for the sen- sible heat part of the energy balance in these models. However, time-lag between the maximum LST on the surface and the max- imum solar insolation (Wang and Qu. 2009) can also be related to the amount of surface SM which contribute to the escaping of heat from the surface via latent heat. Without allocation of the heat energy to the Qg by the near-surface SM, the two maxima (maximum solar insolation and maximum LST) would coincide or be closer in time. To assess this hypothesis, statistical analy- sis of the relationship between the measured surface SM and the remotely sensed LST is implemented in this paper. Q'-—Qc-cr-Qrg-- Qn (1) where Q* is the net radiation, Qc is the ground or storage heat flux, QE is the turbulent latent heat flux, and Q g is the convec- tive sensible heat flux (Rigo and Parlow, 2005, Rigo and Parlow, 2007). Based on Eq. 1, QE can be calculated as: Qe=Q" — Qc — Qu (2) 3.2 Land-cover analysis To identify dominant land-cover classes in the study area, un- supervised classification method has been used. Iterative clas- sification process was carried out using a Landsat TM-5 image acquired on 28 March 2011 over the study area. Eight dominant LC classes were extracted in the region. With the order of high- est to the lowest percentage these classes included grass, water, irrigated crop/grass, bush, baresoil/fallow, water, fallow/exposed soil, and forest. The in-situ sites were chosen only on five LC classes due to the issues in finding suitable sites for some of the LC types (Table !). 3.3 Construction of LST Time-series Inside LST scientific datasets (SDSs) derived from MODIS-Terra and MODIS-Aqua, data for each day or night are stored in sepa- rate data fields. To extract correlations between the in-situ SM data and LST from MODIS, time-series of LST product were constructed. Regarding the number of datasets involved in the analysis, reading day and night values for every day during the analysis period was a cumbersome task, therefore, Matlab® codes were written for reading and construction of time-series from

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