03091naa a2200397 a 450000100080000000500110000800800410001902400440006010000120010424501870011626000090030330000130031252019210032565000190224665000200226565000190228565000190230465000140232365000210233765000210235865000240237965000140240365000250241765300080244265300310245065300170248165300360249870000130253470000210254770000240256870000120259270000110260470000110261570000130262677300540263921254002021-08-25       2020    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.3390/rs121421862DOI1 aXIN, F.  aUnderstanding the land surface phenology and gross primary production of sugarcane plantations by eddy flux measurements, MODIS images, and data-driven models.h[electronic resource]  c2020  ap. 1-20.  aAbstract: 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.  aCarbon dioxide  aEddy covariance  aPhotosynthesis  aRemote sensing  aSugarcane  aVegetation index  aCana de Açúcar  aDióxido de Carbono  aFenologia  aSensoriamento Remoto  aCO2  aEddy covariance flux tower  aMODIS images  aVegetation photosynthesis model1 aXIAO, X.1 aCABRAL, O. M. R.1 aWHITE JUNIOR, P. M.1 aGUO, H.1 aMA, J.1 aLI, B.1 aZHAO, B.  tRemote Sensinggv. 12, n. 14, article 2186, 2020.