Global crop calendars of maize and wheat in the framework of the WorldCereal project

被引:16
作者
Franch, Belen [1 ,2 ]
Cintas, Juanma [1 ]
Becker-Reshef, Inbal [2 ]
Jose Sanchez-Torres, Maria [1 ]
Roger, Javier [3 ]
Skakun, Sergii [2 ,4 ]
Antonio Sobrino, Jose [1 ]
Van Tricht, Kristof [5 ]
Degerickx, Jeroen [5 ]
Gilliams, Sven [5 ]
Koetz, Benjamin [6 ]
Szantoi, Zoltan [6 ,7 ]
Whitcraft, Alyssa [2 ]
机构
[1] Univ Valencia, Global Change Unit, Image Proc Lab, Paterna, Valencia, Spain
[2] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[3] Univ Politecn Valencia, LARS Grp, Valencia, Spain
[4] NASA, Goddard Space Flight Ctr, Greenbelt, MD USA
[5] VITO Remote Sensing, Mol, Belgium
[6] European Space Agcy, Sci Applicat & Climate Dept, Paris, France
[7] Stellenbosch Univ, Dept Geog & Environm Studies, Stellenbosch, South Africa
关键词
Agriculture monitoring; Start of Season; End of Season; LSP; Sentinel-2; Landsat; 8; LAND-SURFACE PHENOLOGY; EARTH OBSERVATION; FOOD SECURITY; WINTER-WHEAT; TIME-SERIES; CHINA; MODEL; AREA; INFORMATION; REGRESSION;
D O I
10.1080/15481603.2022.2079273
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Crop calendars provide valuable information on the timing of important stages of crop development such as the planting or Start of Season (SOS) and harvesting dates or End of Season (EOS). This information is critical for many crop monitoring applications such as crop-type mapping, crop condition monitoring, and crop yield estimation and forecasting. Spatially detailed information on the crop calendars provides an important asset in this respect, as it allows the algorithms to account for specific local circumstances while also maximizing their robustness and global applicability. Existing global crop calendar products, as produced by the Group on Earth Observations' Global Agricultural Monitoring (GEOGLAM) Crop Monitor, the United States Department of Agriculture Foreign Agricultural Service (USDA-FAS), the Food and Agriculture Organization (FAO), and the European Commission Joint Research Center's Anomaly hot Spots of Agricultural Production (ASAP), generally provide this information only at national or subnational level. In this work, we present gridded SOS and EOS maps for wheat and maize that represent the crop calendars' spatial variability at 0.5 degrees spatial resolution. These maps are generated in the framework of WorldCereal, which is a European Space Agency (ESA) funded project whose cropland and crop-type wheat and maize algorithms at global scale and at 10 m spatial resolution require this information. The proposed maps are built leveraging the above noted global products (Crop Monitor, USDA-FAS, FAO, ASAP) whose datasets are combined into a baseline map and sampled to train a Random Forest algorithm based on climatic and geographic data. Their evaluation against test data from the baseline maps set aside for validation purposes show a good performance with SOS (EOS) R (2) of 0.87 (0.92) and a root mean square error (RMSE) of 27 (26) days for wheat, showing the lowest errors (RMSE <15 days) in North America, Central Europe, South Africa, and Australia, all critical areas for global wheat production and trade. Meanwhile, the largest errors (RMSE between 40 and 60 days) occurred in regions of South America close to the Amazon Forest and in Africa close to the Congo Basin. In the case of maize, the SOS (EOS) evaluation shows an R (2) of 0.88 (0.79) and an RMSE of 24 (28) days for maize, with the best performing regions (RMSE < 15 days) located in the Northern Hemisphere, South Africa, and Australia, important areas for global maize production and trade. Meanwhile, the worst performing regions were in Brazil, Saudi Arabia and India. Additionally, the crop calendars were evaluated using a simple Land Surface Phenology (LSP) model based on Sentinel-2 and Landsat 8 Earth Observation data from Sentinel-2 and Landsat 8 over known wheat and maize fields. The results show a SOS (EOS) R (2) of 0.75 (0.88) and an RMSE of 25 (18) days for wheat and SOS (EOS) R (2) of 0.80 (0.88) and an RMSE of 35 (24) days for maize. Therefore, the presented calendars present an advancement over the existing crop calendar products in terms of capturing spatial coverage and variability and reporting their accuracy.
引用
收藏
页码:885 / 913
页数:29
相关论文
共 79 条
  • [1] Permutation importance: a corrected feature importance measure
    Altmann, Andre
    Tolosi, Laura
    Sander, Oliver
    Lengauer, Thomas
    [J]. BIOINFORMATICS, 2010, 26 (10) : 1340 - 1347
  • [2] [Anonymous], 2010, USDA National Agricultural Statistics Service Cropland Data Layer
  • [3] [Anonymous], 2021, FAO CROP CAL
  • [4] [Anonymous], Climate Change and Food Security: Risks and Responses
  • [5] [Anonymous], 2008, CLIM CHANG FOOD SEC
  • [6] Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs
    Atzberger, Clement
    [J]. REMOTE SENSING, 2013, 5 (02) : 949 - 981
  • [7] Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure
    Baetens, Louis
    Desjardins, Camille
    Hagolle, Olivier
    [J]. REMOTE SENSING, 2019, 11 (04)
  • [8] Banerjee A., 2020, AGR J IJOEAR, V6, P10
  • [9] Harvest date forecast for soybeans from maximum vegetative development using satellite images
    Becker, Willyan Ronaldo
    Silva, Laiza Cavalcante De Albuquerque
    Richetti, Jonathan
    Lo, Thiago Berticelli
    Johann, Jerry Adriani
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2021, 42 (03) : 1121 - 1138
  • [10] Becker WR, 2020, SEMIN-CIENC AGRAR, V41, P2419, DOI [10.5433/1679-0359.2020v41n5supl1p2419, 10.5433/1679-0359.2020v41n5Supl1p2419]