XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

Bibliographic data

Multivolume work

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:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

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

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

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

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:: [VIII/6: Agriculture, Ecosystems and Bio-Diversity]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: ESTABLISHING CROP PRODUCTIVITY USING RADARSAT-2 H. McNairn, J. Shang, X. Jiao, B. Deschamps

Document type:: Multivolume work

Structure type:: Chapter

ESTABLISHING CROP PRODUCTIVITY USING RADARSAT-2 H. McNairn ?', J. Shang?, X. Jiao*, B. Deschamps” ? Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, Canada K2K 2C2 — heather.mcnairn@agr.gc.ca ® MDA Geospatial Services Inc., 57 Auriga Drive Suite 201, Ottawa, Canada K2E 8B2 - bdeschamps(a)mdacorporation.com Commission VI, WG VIII/6 KEY WORDS: agriculture, crop, monitoring, SAR, RADARSAT, modelling, retrieval ABSTRACT: Crop productivity is influenced by a number of management and environmental conditions, and variations in crop growth can occur in-season due to, for example, unfavourable meteorological conditions. Consequently information on crop growth must be temporally frequent in order to adequately characterize crop productivity. Leaf Area Index (LAI) is a key indicator of crop productivity and a number of methods have been developed to derive LAI from optical satellite data. Integration of LAI estimates from synthetic aperture radar (SAR) sensors would assist in efforts to monitor crop production through the growing season, particularly during periods of persistent cloud cover. Consequently, Agriculture and Agri-Food Canada has assessed the capability of RADARSAT-2 data to estimate LAI. The results of a sensitivity analysis revealed that several SAR polarimetric variables were strongly correlated with LAI derived from optical sensors for small grain crops. As the growing season progressed, contributions from volume scattering from the crop canopies increased. This led to the sensitivity of the intensity of linear cross-polarization backscatter, entropy and the Freeman-Durden volume scattering component, to LAI. For wheat and oats, correlations above 0.8 were reported. Following this sensitivity analysis, the Water Cloud Model (WCM) was parameterized using LAI, soil moisture and SAR data. A look up table inversion approach to estimate LAI from SAR parameters, using the WCM, was subsequently developed. This inversion approach can be used to derive LAI from sensors like RADARSAT- to support the monitoring of crop condition throughout the cropping season. 1. INTRODUCTION Monitoring crop productivity is critical in determining risks to regional and global food security. Gathering the necessary data to monitor productivity is challenging given the acreages involved and the variable nature of crop growth. Crop management applications during active crop growth, as well as ever changing meteorological conditions, mean that crop condition must be monitored continuously through the growing season. Crop descriptors such as leaf area index (LAI) are good indicators of crop condition and productivity. LAI is the total one-sided green leaf area per unit ground surface area and is a strong indicator of crop production. LAI can be linked with crop yield through process models. Derivation of these crop descriptors from remote sensing data can be used to drive these crop yield models, to validate model estimates and to update or adjust model predictions. Although LAI can be derived from optical sensors (Liu et al, 2010) the reliability of access to data to monitor continuously through the season is questionable due to cloud cover. Synthetic aperture radars (SARs) are thus an appropriate data source to build reliability into monitoring activities. However, the methods to estimate LAI from radar response are not as developed as those that use optical data and thus significant research is required. To address this knowledge gap, Agriculture and Agri-Food Canada (AAFC) has been investigating the sensitivity of polarimetric SAR data to LAI. This research has included the acquisition of numerous RADARSAT-2 and optical satellite *Corresponding author data sets over different cropping regions. In eastern Canada, where corn and soybean production dominates, results have proven the sensitivity of SAR response to LAI for these two broadleaf crops (Jiao et al., 2011). In 2009, the European Space Agency led the AgriSAR campaign under which fully polarimetric RADARSAT-2 data were acquired over three international agriculture research sites. One site was located in western Canada in a region of extensive production of small grains (wheat, oats and barley). The sensitivity of RADARSAT- 2 to LAI for this class of crops, using the data acquired during AgriSAR, is presented here. The Water Cloud Model (WCM) is then used to model LAI from the radar scattering and a method is proposed to invert the WCM for LAI estimation. 2. METHODOLOGY 2. Study Sites and Data Collection In 2009, an extensive collection of both optical (RapidEye and Landsat TM) and polarimetric SAR (RADARSAT-2) data were acquired over a site in western Canada. The selected site was AAFC's precision farm located at Indian Head in southern Saskatchewan. These data were collected under a European Space Agency initiative called AgriSAR. One objective of this campaign was to develop a methodology to estimate crop condition from SAR data. In total, 57 quad-polarimetric ascending and descending RADARSAT-2 images were acquired as part of this AgriSAR initiative. Ground measurements of crop condition, including LAI, were acquired over several small grain (barley, oats,

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