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Remote Sensing
Volume 12
Issue 14
10.3390/rs12142186
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Article

Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models

by
Fengfei Xin
1,
Xiangming Xiao
2,*,
Osvaldo M.R. Cabral
3,
Paul M. White, Jr.
4,
Haiqiang Guo
1,
Jun Ma
1,
Bo Li
1 and
Bin Zhao
1
1
Ministry of Education Key Laboratory of Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai 200433, China
2
Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK 73019, USA
3
Embrapa Meio Ambiente, Jaguariúna CEP 13918-110, SP, Brazil
4
Agriculture Research Service, Sugarcane Research Unit, United States Department of Agriculture, Houma, LA 70360, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2186; https://doi.org/10.3390/rs12142186
Submission received: 17 June 2020 / Revised: 3 July 2020 / Accepted: 6 July 2020 / Published: 8 July 2020
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Abstract

Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPPEC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPPEC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPPEC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPPEC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPPVPM). The seasonal dynamics of the GPPVPM and GPPEC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.
Keywords: CO2 eddy covariance flux tower; MODIS images; vegetation photosynthesis model; vegetation index CO2 eddy covariance flux tower; MODIS images; vegetation photosynthesis model; vegetation index
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MDPI and ACS Style

Xin, F.; Xiao, X.; Cabral, O.M.R.; White, P.M., Jr.; Guo, H.; Ma, J.; Li, B.; Zhao, B. Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models. Remote Sens. 2020, 12, 2186. https://doi.org/10.3390/rs12142186

AMA Style

Xin F, Xiao X, Cabral OMR, White PM Jr., Guo H, Ma J, Li B, Zhao B. Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models. Remote Sensing. 2020; 12(14):2186. https://doi.org/10.3390/rs12142186

Chicago/Turabian Style

Xin, Fengfei, Xiangming Xiao, Osvaldo M.R. Cabral, Paul M. White, Jr., Haiqiang Guo, Jun Ma, Bo Li, and Bin Zhao. 2020. "Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models" Remote Sensing 12, no. 14: 2186. https://doi.org/10.3390/rs12142186

APA Style

Xin, F., Xiao, X., Cabral, O. M. R., White, P. M., Jr., Guo, H., Ma, J., Li, B., & Zhao, B. (2020). Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models. Remote Sensing, 12(14), 2186. https://doi.org/10.3390/rs12142186

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