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Agro-ecological modelling for monitoring rice productions: contribution of field experiment and multi-temporal EO data

机译:监测水稻生产的农业生态模型:田间试验和多时相EO数据的贡献

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Crop growth and production can be simulated by models for the whole canopy as a function of intercepted radiation, water availability, air temperature and nitrogen availability. Simulation models supply quantitative outputs starting from quantitative inputs and they need quite complex databases to run simulations. In practice, the more complex and physically based these tools are, the more inputs are required for their application. In most cases such data are not available. This is the reason why, for large scale evaluations, simplified models are often applied and satellite data are used as input. In particular, multi-temporal Earth Observation data represent a valid tool to define crop phenological stages and derive temporal and spatial variability of vegetation biophysical parameters, such as the Leaf Area Index (LAI). In 2003 and 2004 two intensive field campaigns were conducted over different areas of the Italian Rice Belt, Northern Italy, with the objective of collecting data for growth model calibration. Field spectroradiometer measurements and LAI estimation, retrieved by LAI2000, have been used to study the best Vegetation Index (VI) for rice growth monitoring. VI vs LAI relationship has been scaled up to MODIS data to produce LAI map for the entire growing season and the key phenological rice events have been detected by multitemporal MODIS analysis. Preliminary results of rice production estimation using a Light Use efficiency model that ingests spatially distributed phenological information are presented. Comparison with CropSyst model phenological parameters are provided and the contribution of multi-temporal EO data for regional crop monitoring is discussed.
机译:可以通过截留辐射,水,空气温度和氮的有效利用整个冠层的模型来模拟作物的生长和生产。模拟模型从定量输入开始提供定量输出,并且它们需要相当复杂的数据库来运行模拟。在实践中,这些工具越复杂且基于物理的情况,其应用就需要更多的输入。在大多数情况下,此类数据不可用。这就是为什么在进行大规模评估时,通常会使用简化的模型并将卫星数据用作输入的原因。特别是,多时相地球观测数据代表了定义作物物候阶段并推导出植被生物物理参数(如叶面积指数(LAI))的时空变化的有效工具。 2003年和2004年,在意大利北部意大利稻米带的不同地区进行了两次密集的野外运动,目的是收集数据以进行生长模型校准。由LAI2000检索的现场光谱辐射计测量和LAI估计已用于研究最佳的植被指数(VI),用于水稻生长监测。 VI与LAI的关系已按MODIS数据进行了放大,以生成整个生长季节的LAI图,并且通过多时相MODIS分析检测到了重要的物候水稻事件。提出了使用吸收空间分布的物候信息的光利用效率模型估算稻米产量的初步结果。提供了与CropSyst模型物候参数的比较,并讨论了多时相EO数据对区域作物监测的贡献。

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