Literature DB >> 29888751

Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale.

Sergii Skakun1,2, Eric Vermote2, Jean-Claude Roger1,2, Belen Franch1,2.   

Abstract

Timely and accurate information on crop yield is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to enable temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat assessment at regional scale. For the former, we adapt a previously developed approach for Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution that allows automatic mapping of winter crops taking into account knowledge on crop calendar and without ground truth data. For the latter, we use a generalized winter wheat yield model that is based on NDVI-peak estimation and MODIS data, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A has a positive impact both for winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times comparing to the single satellite usage.

Entities:  

Keywords:  Landsat-8; MODIS; Sentinel-2; Ukraine; agriculture; area; mapping; wheat; yield

Year:  2017        PMID: 29888751      PMCID: PMC5992624          DOI: 10.3934/geosci.2017.2.163

Source DB:  PubMed          Journal:  AIMS Geosci        ISSN: 2471-2132


  1 in total

1.  High-resolution global maps of 21st-century forest cover change.

Authors:  M C Hansen; P V Potapov; R Moore; M Hancher; S A Turubanova; A Tyukavina; D Thau; S V Stehman; S J Goetz; T R Loveland; A Kommareddy; A Egorov; L Chini; C O Justice; J R G Townshend
Journal:  Science       Date:  2013-11-15       Impact factor: 47.728

  1 in total
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1.  County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model.

Authors:  Jie Sun; Liping Di; Ziheng Sun; Yonglin Shen; Zulong Lai
Journal:  Sensors (Basel)       Date:  2019-10-09       Impact factor: 3.576

  1 in total

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