Monitoring autumn agriculture activities using Synthetic Aperture Radar (SAR) and coherence change detection

被引:2
|
作者
Robertson, Laura Dingle [1 ,4 ]
McNairn, Heather [1 ]
van der Kooij, Marco [2 ]
Jiao, Xianfeng [1 ]
Ihuoma, Samuel [1 ]
Joosse, Pamela [3 ]
机构
[1] Agr & Agrifood Canada, Agrienvironm Div, Ottawa, ON, Canada
[2] Vanderkooij Consult, Ottawa, ON, Canada
[3] Agr & Agrifood Canada, Harrow Res & Dev Ctr, Harrow, ON, Canada
[4] Environm & Climate Change Canada, Landscape Sci & Technol Div, Ottawa, ON, Canada
关键词
Coherence change detection; Agriculture; Tillage; Harvest; SAR; Sentinel-1; RADARSAT Constellation Mission; NO-TILLAGE; SOIL; ONTARIO;
D O I
10.1016/j.heliyon.2023.e17322
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Across Canada, farmers are encouraged to adopt beneficial management practices (BMPs) to protect soil heath, reduce green house gas emissions and mitigate off-site impacts from agriculture. Measuring the uptake of BMPs, including the implementation of conservation tillage, helps gauge the success of policies and programs to promote adoption. Satellites are one way to monitor BMP adoption and Synthetic Aperture Radars (SARs) are of particular interest given their allweather data collection capability. This research investigated coherent change detection (CCD) to determine when farmers harvest and till their fields. A time series of both Sentinel-1 and RADARSAT Constellation Mission (RCM) images was acquired over a site in the Canadian Lake Erie basin, during the autumn of 2021, when farmers were harvesting and tilling fields of corn, soybeans and wheat. 16 CCD pairs were created and coherence values were interpreted based on observations collected for 101 fields. An m-chi decomposition was applied to the RCM data, and the Volume/Surface (V/S) ratio was calculated as an additional source of information to interpret results. Change events due to harvest, tillage, autumn seeding and chemical termination resulted in coherence values below 0.20. The mean and standard deviation for fields with observed change was 0.18 & PLUSMN; 0.03. Coherence values were 0.42 & PLUSMN; 0.15 for fields where no change was noted. Tests confirmed that the coherence associated with changed and unchanged fields was significantly different. Coherence values could also differentiate between some types of management events, including tillage and harvest. CCD could also separate harvest as a function of crop type (corn or soybeans). V/S ratios declined after tillage events but increased after both harvesting and chemical termination. Narrowing the date of harvest and tillage is as important as detecting change. To meet this requirement, Sentinel-1 and RCM CCD products with values below 0.20 (indicating change had occurred), were graphically overlaid. With this approach, the timing of corn harvest was identified as occurring within a 5-day window. The tilling of corn, soybeans and wheat was narrowed to a 4-day window. The results of this research confirmed that CCD can be used to capture change due to autumn agricultural activities, and this technique can also separate change due to harvest and tillage. Finally, this study demonstrated that when data from different SAR missions are combined in a virtual constellation, timing of harvest and tillage can be more precisely defined.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Monitoring and Change Detection of Natural Disaster (Like Subsidence) Using Synthetic Aperture Radar (SAR) Data
    Singh, D.
    Chamundeeswari, V. V.
    Singh, K.
    Wiesbeck, Werner
    INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN MICROWAVE THEORY AND APPLICATIONS, PROCEEDINGS, 2008, : 419 - +
  • [2] Monitoring forest disturbance using change detection on synthetic aperture radar imagery
    Durieux, Alice M. S.
    Calef, Matthew T.
    Arko, Scott
    Chartrand, Rick
    Kontgis, Caitlin
    Keisler, Ryan
    Warren, Michael S.
    APPLICATIONS OF MACHINE LEARNING, 2019, 11139
  • [3] MONITORING OF IONOSPHERIC SCINTILLATION PHENOMENA USING SYNTHETIC APERTURE RADAR (SAR)
    Mohanty, S.
    Carrano, C.
    Singh, G.
    ISPRS TC V MID-TERM SYMPOSIUM GEOSPATIAL TECHNOLOGY - PIXEL TO PEOPLE, 2018, 4-5 : 331 - 337
  • [4] CHANGE DETECTION WITH SYNTHETIC APERTURE RADAR
    CIHLAR, J
    PULTZ, TJ
    GRAY, AL
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 1992, 13 (03) : 401 - 414
  • [5] Synthetic Aperture Radar (SAR) Monitoring of Avalanche Activity: An Automated Detection Scheme
    Vickers, H.
    Eckerstorfer, M.
    Malnes, E.
    Doulgeris, A.
    IMAGE ANALYSIS, SCIA 2017, PT II, 2017, 10270 : 136 - 146
  • [6] Synthetic aperture radar (SAR) data applications for tropical peatlands monitoring activities: An overview
    Novresiandi, Dandy Aditya
    Setiyoko, Andie
    Arief, Rahmat
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2023, 29
  • [7] A WEIGHTED COHERENCE ESTIMATOR FOR COHERENT CHANGE DETECTION IN SYNTHETIC APERTURE RADAR IMAGES
    Wang, Mengmeng
    Huang, Guoman
    Zhang, Jixian
    Hua, Fenfen
    Lu, Lijun
    XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III, 2022, 43-B3 : 1369 - 1375
  • [8] Toward the use of multiparametric synthetic aperture radar for agriculture monitoring
    McNairn, Heather
    Brisco, Brian
    Parihar, Jai Singh
    Canadian Journal of Remote Sensing, 2011, 37 (01) : 3 - 5
  • [9] AREA CHANGE DETECTION IN RIVER MOUTHBARS AT THE MEKONG RIVER DELTA USING SYNTHETIC APERTURE RADAR (SAR) DATA
    Tanaka, Akiko
    Uehara, Katsuto
    Tamura, Toru
    Saito, Yoshiki
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 4911 - 4914
  • [10] GROUND DISPLACEMENT DETECTION AND MONITORING USING SYNTHETIC APERTURE RADAR IMAGERY
    Negula, Iulia Dana
    Poenaru, Violeta
    INFORMATICS, GEOINFORMATICS AND REMOTE SENSING, VOL I (SGEM 2015), 2015, : 1083 - 1088