Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images
Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images
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DOI：
10.3390/rs16010046
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发表时间：
2023-12
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影响因子：
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通讯作者：
Jorge Celis;Xiangming Xiao;Paul M. White;Osvaldo M. R. Cabral;Helber C. Freitas
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作者：
Jorge Celis;Xiangming Xiao;Paul M. White;Osvaldo M. R. Cabral;Helber C. Freitas
甘蔗农田约占全球食糖产量的 70%，约占全球乙醇产量的 60%。监测和预测这些田地的总初级生产力（GPP）和蒸腾量（T）对于改善作物产量估算和管理至关重要。虽然中等空间分辨率（MSR，数百米）卫星图像已在多种模型中用于估计 GPP 和 T，但只有少数出版物考虑了高分辨率（HSR，数十米）图像的潜力，并且在甘蔗田中尚未得到充分探索。我们的研究评估了 MSR 和 HSR 卫星图像在预测两个配备涡流塔的地点的甘蔗种植园每日 GPP 和 T 方面的功效：美国路易斯安那州（亚热带气候）和巴西圣保罗（热带气候）。我们采用具有 C4 光合作用途径的植被光合作用模型 (VPM) 和植被蒸腾模型 (VTM)，整合来自卫星图像和地面天气数据的植被指数数据，计算每日 GPP 和 T。MSR 图像（MODIS 传感器，500 m）和 HSR 图像（Landsat，30 m；Sentinel-2，10 m）的植被指数季节动态与 EC 的 GPP 季节性很好地跟踪通量塔。 HSR 图像的增强植被指数 (EVI) 与基于塔的 GPP 具有更强的相关性。我们的研究结果强调了 HSR 图像在估算小型甘蔗种植园中 GPP 和 T 方面的潜力。
Sugarcane croplands account for ~70% of global sugar production and ~60% of global ethanol production. Monitoring and predicting gross primary production (GPP) and transpiration (T) in these fields is crucial to improve crop yield estimation and management. While moderate-spatial-resolution (MSR, hundreds of meters) satellite images have been employed in several models to estimate GPP and T, the potential of high-spatial-resolution (HSR, tens of meters) imagery has been considered in only a few publications, and it is underexplored in sugarcane fields. Our study evaluated the efficacy of MSR and HSR satellite images in predicting daily GPP and T for sugarcane plantations at two sites equipped with eddy flux towers: Louisiana, USA (subtropical climate) and Sao Paulo, Brazil (tropical climate). We employed the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) with C4 photosynthesis pathway, integrating vegetation index data derived from satellite images and on-ground weather data, to calculate daily GPP and T. The seasonal dynamics of vegetation indices from both MSR images (MODIS sensor, 500 m) and HSR images (Landsat, 30 m; Sentinel-2, 10 m) tracked well with the GPP seasonality from the EC flux towers. The enhanced vegetation index (EVI) from the HSR images had a stronger correlation with the tower-based GPP. Our findings underscored the potential of HSR imagery for estimating GPP and T in smaller sugarcane plantations.