Next Article in Journal
The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters
Next Article in Special Issue
Past and Future Trajectories of Farmland Loss Due to Rapid Urbanization Using Landsat Imagery and the Markov-CA Model: A Case Study of Delhi, India
Previous Article in Journal
Thermal Energy Release Measurement with Thermal Camera: The Case of La Solfatara Volcano (Italy)
Previous Article in Special Issue
Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm
Article

Using the Bayesian Network to Map Large-Scale Cropping Intensity by Fusing Multi-Source Data

by 1, 2,* and 1
1
Key Laboratory for Geographical Process Analysis & Simulation of Hubei province/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
2
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(2), 168; https://doi.org/10.3390/rs11020168
Received: 16 October 2018 / Revised: 10 January 2019 / Accepted: 12 January 2019 / Published: 17 January 2019
(This article belongs to the Special Issue Selected Papers from Agro-Geoinformatics 2018)
Global food demand will increase over the next few decades, and sustainable agricultural intensification on current cropland may be a preferred option to meet this demand. Mapping cropping intensity with remote sensing data is of great importance for agricultural production, food security, and agricultural sustainability in the context of global climate change. However, there are some challenges in large-scale cropping intensity mapping. First, existing indicators are too coarse, and fine indicators for measuring cropping intensity are lacking. Second, the regional, intra-class variations detected in time-series remote sensing data across vast areas represent environment-related clusters for each cropping intensity level. However, few existing studies have taken into account the intra-class variations caused by varied crop patterns, crop phenology, and geographical differentiation. In this research, we first presented a new definition, a normalized cropping intensity index (CII), to quantify cropping intensity precisely. We then proposed a Bayesian network model fusing prior knowledge (BNPK) to address the issue of intra-class variations when mapping CII over large areas. This method can fuse regional differentiation factors as prior knowledge into the model to reduce the uncertainty. Experiments on five sample areas covering the main grain-producing areas of mainland China proved the effectiveness of the model. Our research proposes the framework of obtain a CII map with both a finer spatial resolution and a fine temporal resolution at a national scale. View Full-Text
Keywords: cropping intensity index; regional differentiation; Bayesian network; prior knowledge; MODIS time-series cropping intensity index; regional differentiation; Bayesian network; prior knowledge; MODIS time-series
Show Figures

Graphical abstract

MDPI and ACS Style

Tao, J.; Wu, W.; Xu, M. Using the Bayesian Network to Map Large-Scale Cropping Intensity by Fusing Multi-Source Data. Remote Sens. 2019, 11, 168. https://doi.org/10.3390/rs11020168

AMA Style

Tao J, Wu W, Xu M. Using the Bayesian Network to Map Large-Scale Cropping Intensity by Fusing Multi-Source Data. Remote Sensing. 2019; 11(2):168. https://doi.org/10.3390/rs11020168

Chicago/Turabian Style

Tao, Jianbin, Wenbin Wu, and Meng Xu. 2019. "Using the Bayesian Network to Map Large-Scale Cropping Intensity by Fusing Multi-Source Data" Remote Sensing 11, no. 2: 168. https://doi.org/10.3390/rs11020168

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop