Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland.

MLA

Lu, Ruhua, et al. “Improving the Spatial and Temporal Estimation of Ecosystem Respiration Using Multi-Source Data and Machine Learning Methods in a Rainfed Winter Wheat Cropland.” Science of the Total Environment, vol. 871, May 2023, p. N.PAG. EBSCOhost, https://doi.org/10.1016/j.scitotenv.2023.161967.

APA

Lu, R., Zhang, P., Fu, Z., Jiang, J., Wu, J., Cao, Q., Tian, Y., Zhu, Y., Cao, W., & Liu, X. (2023). Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland. Science of the Total Environment, 871, N.PAG. https://doi.org/10.1016/j.scitotenv.2023.161967

Chicago

Lu, Ruhua, Pei Zhang, Zhaopeng Fu, Jie Jiang, Jiancheng Wu, Qiang Cao, Yongchao Tian, Yan Zhu, Weixing Cao, and Xiaojun Liu. 2023. “Improving the Spatial and Temporal Estimation of Ecosystem Respiration Using Multi-Source Data and Machine Learning Methods in a Rainfed Winter Wheat Cropland.” Science of the Total Environment 871 (May): N.PAG. doi:10.1016/j.scitotenv.2023.161967.