Sugarcane and Cassava Classification Using Machine Learning Approach Based on Multi-temporal Remote Sensing Data Analysis

Author:

Daraneesrisuk JirawatORCID,Ninsawat SarawutORCID,Losiri Chudech,Sitthi Asamaporn

Publisher

Springer International Publishing

Reference8 articles.

1. Boonyanam, N.: Agricultural economic zones in Thailand. Land Use Policy. 99, 102774 (2020)

2. Xu, J., Meng, J., Quackenbush, L.J.: Use of remote sensing to predict the optimal harvest date of corn. Field Crop Res. 236, 1–13 (2019)

3. Arias, M., Campo-Bescós, M.Á., Álvarez-Mozos, J.: Crop classification based on temporal signatures of sentinel-1 observations over Navarre Province, Spain. Remote Sens. 12(2), 278 (2020)

4. Ouzemou, J.-E., et al.: Crop type mapping from pansharpened Landsat 8 NDVI data: a case of a highly fragmented and intensive agricultural system. Remote Sens. Appl. Soc. Environ. 11, 94–103 (2018)

5. Clauss, K., et al.: Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data. Int. J. Appl. Earth Obs. Geoinf. 73, 574–585 (2018)

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