Yield Prediction for Winter Wheat with Machine Learning Models Using Sentinel-1, Topography, and Weather Data

被引:2
|
作者
Bogdanovski, Oliver Persson [1 ]
Svenningsson, Christoffer [1 ]
Mansson, Simon [2 ]
Oxenstierna, Andreas [3 ]
Sopasakis, Alexandros [1 ]
机构
[1] Lund Univ, Fac Sci, Dept Math, S-22100 Lund, Sweden
[2] Niftitech AB, Hedvig Mollers gata 12, S-22355 Lund, Sweden
[3] T Kartor AB, Olof Mohlins vag 12, S-29162 Kristianstad, Sweden
来源
AGRICULTURE-BASEL | 2023年 / 13卷 / 04期
基金
瑞典研究理事会;
关键词
precision agriculture; Sentinel-1; SAR; machine learning; yield prediction; despeckling; GROWTH;
D O I
10.3390/agriculture13040813
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
We train and compare the performance of two different machine learning algorithms to learn changes in winter wheat production for fields from the southwest of Sweden. As input to these algorithms, we use cloud-penetrating Sentinel-1 polarimetry radar data together with respective field topography and local weather over four different years. We note that all of the input data were freely available. During training, we used information on winter wheat production over the fields of interest which was available from participating farmers. The two machine learning models we trained are the Light Gradient-Boosting Machine and a Feed-forward Neural Network. Our results show that Sentinel-1 data contain valuable information which can be used for training to predict winter wheat yield once two important steps are taken: performing a critical transformation of each pixel in the images to align it to the training data and then following up with despeckling treatment. Using this approach, we were able to achieve a top root mean square error of 0.75 tons per hectare and a top accuracy of 86% using a k-fold method with k=5. More importantly, however, we established that Sentinel-1 data alone are sufficient to predict yield with an average root mean square error of 0.89 tons per hectare, making this method feasible to employ worldwide.
引用
收藏
页数:19
相关论文
共 50 条
  • [41] Prediction of Soil Temperature in Wheat Field Using Machine Learning Models
    Durgam, Maheshwar
    Mailapalli, Damodhara Rao
    Singh, Rajendra
    COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 2024, 55 (22) : 3510 - 3534
  • [42] Unveiling soil coherence patterns along Etihad Rail using Sentinel-1 and Sentinel-2 data and machine learning in arid region
    Alyounis, Sona
    Al Momani, Delal E.
    Gafoor, Fahim Abdul
    Alansari, Zaineb
    Al Hashemi, Hamed
    AlShehhi, Maryam R.
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2024, 36
  • [43] NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region
    Ayari, Emna
    Kassouk, Zeineb
    Lili-Chabaane, Zohra
    Ouaadi, Nadia
    Baghdadi, Nicolas
    Zribi, Mehrez
    GISCIENCE & REMOTE SENSING, 2024, 61 (01)
  • [44] Optimal county-level crop yield prediction using MODIS-based variables and weather data: A comparative study on machine learning models
    Ju, Sungha
    Lim, Hyoungjoon
    Ma, Jong Won
    Kim, Soohyun
    Lee, Kyungdo
    Zhao, Shuhe
    Heo, Joon
    AGRICULTURAL AND FOREST METEOROLOGY, 2021, 307
  • [45] Digital Soil Texture Mapping and Spatial Transferability of Machine Learning Models Using Sentinel-1, Sentinel-2, and Terrain-Derived Covariates
    Mirzaeitalarposhti, Reza
    Shafizadeh-Moghadam, Hossein
    Taghizadeh-Mehrjardi, Ruhollah
    Demyan, Michael Scott
    REMOTE SENSING, 2022, 14 (23)
  • [46] Statistical and machine learning models for location-specific crop yield prediction using weather indices
    Ajith, S.
    Debnath, Manoj Kanti
    Karthik, R.
    INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, 2024, 68 (12) : 2453 - 2475
  • [47] Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning
    Paluba, Daniel
    Le Saux, Bertrand
    Sarti, Francesco
    Stych, Premysl
    BIG EARTH DATA, 2025,
  • [48] Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series
    de Castro Filho, Hugo Crisostomo
    de Carvalho Junior, Osmar Abilio
    Ferreira de Carvalho, Osmar Luiz
    de Bem, Pablo Pozzobon
    de Moura, Rebeca dos Santos
    de Albuquerque, Anesmar Olino
    Silva, Cristiano Rosa
    Guimaraes Ferreira, Pedro Henrique
    Guimaraes, Renato Fontes
    Trancoso Gomes, Roberto Arnaldo
    REMOTE SENSING, 2020, 12 (16)
  • [49] Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles
    Caballero, Gabriel
    Pezzola, Alejandro
    Winschel, Cristina
    Casella, Alejandra
    Angonova, Paolo Sanchez
    Orden, Luciano
    Berger, Katja
    Verrelst, Jochem
    Delegido, Jesus
    REMOTE SENSING, 2022, 14 (22)
  • [50] Object-based machine learning approach for soybean mapping using temporal sentinel-1/sentinel-2 data
    Kumari, Mamta
    Pandey, Varun
    Choudhary, Karun Kumar
    Murthy, C. S.
    GEOCARTO INTERNATIONAL, 2022, 37 (23) : 6848 - 6866