CAPTURING CORN AND SOYBEAN YIELD VARIABILITY AT FIELD SCALE USING VERY HIGH SPATIAL RESOLUTION SATELLITE DATA

被引：0
|
作者：
Skakun, S. [1 ,2 ]
Brown, M. G. L. [1 ]
Roger, J-C [1 ,2 ]
Vermote, E. [2 ]
机构：
[1] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[2] NASA, Goddard Space Flight Ctr, Code 619, Greenbelt, MD 20771 USA
来源：
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年
基金：
美国国家航空航天局;
关键词：
Yield; Planet; WorldView-3; corn; soybean; SURFACE REFLECTANCE; WINTER-WHEAT;
D O I：
10.1109/IGARSS39084.2020.9324033
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this work, we focus on exploring very high spatial resolution (1-3 m) satellite imagery for capturing crop yield variability at field scale. In-field yields of soybean and corn were collected in Iowa, USA, and were correlated with multi-spectral satellite data acquired by WorldView-3 (at 1.25 m) and PlanetScope (Dove-Classic) (at 3 m). Results show that the most important spectral bands explaining corn and soybean yield variability are green/yellow, red edge and NIR. High temporal frequency of Planet data allowed identification of best suitable date for yield assessment: PlanetScope's spectral bands at 3 m explained 10% to 75% of in-field corn and soybean yield variability.
引用
收藏
页码：3723 / 3726
页数：4
相关论文
共 50 条
  • [1] Using high spatial resolution multispectral data to classify corn and soybean crops
    [J]. 2000, ASPRS, Bethesda, MD, USA (66):
  • [2] Using high spatial resolution multispectral data to classify corn and soybean crops
    Senay, GB
    Lyon, JG
    Ward, AD
    Nokes, SE
    [J]. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2000, 66 (03): : 319 - 327
  • [3] Spatial Variability in Biomass Yield of Switchgrass, Native Prairie, and Corn at Field Scale
    Hao, Xinmei
    Thelen, Kurt
    Gao, Juan
    [J]. AGRONOMY JOURNAL, 2016, 108 (02) : 548 - 558
  • [4] Rotation Effects on Corn and Soybean Yield Inferred from Satellite and Field-level Data
    Cohen, Allegra A. Beal
    Seifert, Christopher A.
    Azzari, George
    Lobell, David B.
    [J]. AGRONOMY JOURNAL, 2019, 111 (06) : 2940 - 2948
  • [5] Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery
    Gao, Feng
    Anderson, Martha
    Daughtry, Craig
    Johnson, David
    [J]. REMOTE SENSING, 2018, 10 (09)
  • [6] Use of spatial information after segmentation for very high spatial resolution satellite data classification
    Carleer, AP
    Wolff, E
    [J]. IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING X, 2004, 5573 : 150 - 160
  • [7] The spatial variability of irrigated corn yield in relation to field topography
    da Silva, JRM
    Alexandre, C
    [J]. PRECISION AGRICULTURE, 2003, : 385 - 391
  • [8] Spatial variability and correlation of chemicals and physical soil attributes with corn and soybean yield
    Gelain, Evandro
    Bottega, Eduardo Leonel
    Motomiya, Anamari Viegas de Araujo
    de Oliveira, Zanandra Boff
    [J]. NATIVA, 2021, 9 (05): : 536 - 543
  • [9] Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling
    Rasanen, Aleksi
    Juutinen, Sari
    Aurela, Mika
    Virtanen, Tarmo
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2019, 40 (03) : 1175 - 1199
  • [10] Forecasting maize yield at field scale based on high-resolution satellite imagery
    Schwalbert, Rai A.
    Amado, Tehno J. C.
    Nieto, Luciana
    Varela, Sebastian
    Corassa, Geomar M.
    Horbe, Tiago A. N.
    Rice, Charles W.
    Peralta, Nahuel R.
    Ciampitti, Ignacio A.
    [J]. BIOSYSTEMS ENGINEERING, 2018, 171 : 179 - 192
12345