Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data
Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data
复制标题
DOI：
10.1016/j.rse.2016.07.030
复制
发表时间：
2016-10-01
影响因子：
13.5
通讯作者：
Demarez, Valerie
中科院分区：
文献类型：
--
作者：
Battude, Marjorie;Al Bitar, Ahmad;Demarez, Valerie
本研究旨在开发一种基于高分辨率遥感数据的稳健且通用的方法，以提供大面积（即区域范围）玉米生物量和产量的准确估计。我们在这里提出了一种尽可能独立于原位测量并且在大面积和各种气候条件下可靠的校准和空间化策略。为此，我们将简单产量估算算法 (SAFY) 模型与来自多个传感器的高空间和时间分辨率遥感数据相结合：Formosat-2、SPOT4-Take5、Landsat-8 和 Deimos-1。 SPOT4-Take5 实验于 2013 年进行，旨在模拟 ESA Sentinel-2 任务的时间采样。这项研究提出了新版本的 SAFY 模型，该模型考虑了特定叶面积 (SLA) 和有效光利用效率 (ELUE) 的季节性变化。该研究在位于法国西南部的温带农业系统中进行。 SAFY 的输出通过使用多年数据集在当地和区域尺度上对生物量和产量估计的当地测量进行了验证。当地生物量（R = 0.98；RRMSE = 14%）和产量（R = 0.81；RRMSE = 8.9%）以及区域规模产量估算（R = 0.96；RRMSE = 4.6%）都获得了良好的结果。结果还表明，使用双逻辑函数插值绿化面积指数 (GAI) 时间序列可以在遥感数据缺失时改进生物量和产量的估计。这项工作展示了高分辨率遥感数据在不借助现场数据的情况下校准简单作物模型的潜力，从而预示了使用 Sentinel-2 数据的未来应用。 (C) 2016 Elsevier Inc. 保留所有权利。
This study aims at developing a robust and generic methodology, based on the use of high resolution remote sensing data to provide accurate estimates of maize biomass and yield over large areas (i.e. at regional scale). We propose here a strategy of calibration and spatialization independent as much as possible of in situ measurements and reliable over large areas and under various climatic conditions. For this purpose, we combine the Simple Algorithm For Yield estimates (SAFY) model with high spatial and temporal resolution remote sensing data from several sensors: Formosat-2, SPOT4-Take5, Landsat-8 and Deimos-1. SPOT4-Take5 experiment conducted in 2013 was designed to simulate the temporal sampling of ESA's Sentinel-2 mission. This study led to a new version of the SAFY model that takes into account the seasonal variation of specific leaf area (SLA) and effective light use efficiency (ELUE). The study takes place in a temperate agrosystem located in the south west of France. The SAFY outputs were validated with local measurements of biomass and yield estimates at both local and regional scales using a multiannual dataset. Good results were obtained for both local biomass (R = 0.98; RRMSE = 14%) and yield (R = 0.81; RRMSE = 8.9%), and for yield estimations at regional scale (R = 0.96; RRMSE = 4.6%). Results also showed that the use of a double logistic function to interpolate Green Area Index (GAI) time series permits to improve the estimations of biomass and yield when remote sensing data are missing. This work demonstrates the potential of high resolution remote sensing data to calibrate a simple crop model without resorting to in situ data and thus foreshadows the future applications using Sentinel-2 data. (C) 2016 Elsevier Inc. All rights reserved.