TY - CHAP
T1 - Remote sensing time series analysis for early rice yield forecasting using random forest algorithm
AU - Son, Nguyen Thanh
AU - Chen, Chi Farn
AU - Chen, Cheng Cru
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2022. All rights reserved.
PY - 2022/3/28
Y1 - 2022/3/28
N2 - Early information on rice crop yields is essential for production estimation to formulate strategies on food security and rice grain exports in a country. This research aimed to develop a machine learning approach for rice yield forecasting using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data in Taiwan. We processed the data following three main steps. Firstly, data pre-processing was carried out to reconstruct the smooth time-series Normalized Difference Vegetation Index (NDVI) data for 2000 to 2018, using the empirical mode decomposition (EMD). Secondly, we established rice crop yield models using the random forest algorithm. Then, the datasets from 2000 to 2017 were used for formulating predictive models to forecast rice crop yields in 2018. Thirdly, the robust performance of yield models was evaluated by comparing the predicted results with the official yield statistics. The results showed that the root mean square percentage error (RMSPE), mean absolute percentage error (MAPE), and Willmott's index of agreement (d) values, achieved for the first crop, were 11.8%, 9.3%, and 0.81, while those for the second crop were 11.2%, 9.1%, and 0.91, respectively. In both cases, these findings were also reaffirmed by a close relationship between these two datasets, with the correlation coefficient (r) values greater than 0.85. The approach followed in the study can be followed elsewhere for rice yield forecasting to address food security concerns.
AB - Early information on rice crop yields is essential for production estimation to formulate strategies on food security and rice grain exports in a country. This research aimed to develop a machine learning approach for rice yield forecasting using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data in Taiwan. We processed the data following three main steps. Firstly, data pre-processing was carried out to reconstruct the smooth time-series Normalized Difference Vegetation Index (NDVI) data for 2000 to 2018, using the empirical mode decomposition (EMD). Secondly, we established rice crop yield models using the random forest algorithm. Then, the datasets from 2000 to 2017 were used for formulating predictive models to forecast rice crop yields in 2018. Thirdly, the robust performance of yield models was evaluated by comparing the predicted results with the official yield statistics. The results showed that the root mean square percentage error (RMSPE), mean absolute percentage error (MAPE), and Willmott's index of agreement (d) values, achieved for the first crop, were 11.8%, 9.3%, and 0.81, while those for the second crop were 11.2%, 9.1%, and 0.91, respectively. In both cases, these findings were also reaffirmed by a close relationship between these two datasets, with the correlation coefficient (r) values greater than 0.85. The approach followed in the study can be followed elsewhere for rice yield forecasting to address food security concerns.
KW - MODIS
KW - NDVI
KW - Random forest algorithm
KW - Rice
KW - Yield
UR - http://www.scopus.com/inward/record.url?scp=85151466270&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-92365-5_20
DO - 10.1007/978-3-030-92365-5_20
M3 - 篇章
AN - SCOPUS:85151466270
SN - 9783030923648
SP - 353
EP - 366
BT - Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries
PB - Springer International Publishing
ER -