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A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data

机译:利用时间序列MODIS数据检测玉米和大豆物候的两步过滤方法

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The crop developmental stage represents essential information for irrigation scheduling/fertilizer management, understanding seasonal ecosystem carbon dioxide (CO_2) exchange, and evaluating crop productivity. In this study, we devised an approach called the Two-Step Filtering (TSF) for detecting the phenological stages of maize and soybean from time-series Wide Dynamic Range Vegetation Index (WDRVI) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations. The TSF method consists of a Two-Step Filtering scheme that includes: (i) smoothing the temporal WDRVI data with a wavelet-based filter and (ii) deriving the optimum scaling parameters from shape-model fitting procedure. The date of key crop development stages are then estimated by using the optimum scaling parameters and an initial value of the specific phenological date on the shape model, which are preliminary defined in reference to ground-based crop growth stage observations. The shape model is a crop-specific WDRVI curve with typical seasonal features, which were defined by averaging smoothed, multi-year WDRVI profiles from MODIS 250-m data collected over irrigated maize and soybean study sites.In this study, the TSF method was applied to MODIS-derived WDRVI data over a 6-year period (2003 to 2008) for two irrigated sites and one rainfed site planted to either maize or soybean as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln. A comparison of satellite-based retrievals with ground-based crop growth stage observations collected by the CSP over the six growing seasons for these three sites showed that the TSF method can accurately estimate the date of four key phenological stages of maize (V2.5: early vegetative stage, R1: silking stage, R5: dent stage and R6: maturity) and soybean (V1: early vegetative stage, R5: beginning seed, R6: full seed and R7: beginning maturity). The root mean square error (RMSE) of phenological-stage estimation for maize ranged from 2.9 [R1] to 7.0 [R5] days and from 3.2 [R6] to 6.9 [R7] days for soybean, respectively. In addition, the TSF method was also applied for two years (2001 and 2002) over eastern Nebraska to test its ability to characterize the spatio-temporal patterns of these key phenological stages over a larger geographic area. The MODIS-derived crop phenological stage dates agreed well with the statistical crop progress data reported by the United State Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) for eastern Nebraska's three crop agricultural statistic districts (ASDs). At the ASD-level, the RMSE of phenological-stage estimation ranged from 1.6 [R1] to 5.6 [R5] days for maize and from 2.5 [R7] to 5.3 [R5] days for soybean.
机译:作物发育阶段代表了灌溉计划/肥料管理,了解季节性生态系统二氧化碳(CO_2)交换以及评估作物生产力的重要信息。在这项研究中,我们设计了一种称为“两步过滤(TSF)”的方法,用于从中等分辨率成像光谱仪(MODIS)250-获得的时间序列宽动态范围植被指数(WDRVI)数据中检测玉米和大豆的物候期。米的意见。 TSF方法由两步过滤方案组成,该方案包括:(i)使用基于小波的滤波器对时间WDRVI数据进行平滑处理,以及(ii)从形状模型拟合过程中得出最佳缩放参数。然后,通过使用最佳缩放参数和形状模型上特定物候日期的初始值来估计关键作物发育阶段的日期,这些参数是参考基于地面作物生长阶段的观测而初步定义的。形状模型是具有特定季节特征的特定于作物的WDRVI曲线,通过对从灌溉玉米和大豆研究地点收集的MODIS 250-m数据中的平滑多年WDRVI曲线求平均值来定义。在内布拉斯加州林肯大学碳固存计划(CSP)的一部分中,将MODIS衍生的WDRVI数据应用于6年期间(2003年至2008年),用于两个灌溉点和一个用于玉米或大豆的雨养点。 CSP在这三个地点的六个生长季节收集的基于卫星的检索与地面作物生长阶段的观测结果的比较表明,TSF方法可以准确地估算玉米四个关键物候阶段的日期(V2.5:营养早期,R1:蚕丝期,R5:凹痕期,R6:成熟期)和大豆(V1:营养早期,R5:初种,R6:饱满,R7:开始成熟)。玉米物候期估计的均方根误差(RMSE)在大豆上为2.9 [R1]天至7.0 [R5]天,而大豆为3.2 [R6]天至6.9 [R7]天。此外,TSF方法还在内布拉斯加州东部应用了两年(2001年和2002年),以测试其表征较大地理区域中这些关键物候阶段的时空分布的能力。 MODIS得出的作物物候期日期与美国农业部(USDA)国家农业统计局(NASS)报告的内布拉斯加州东部三个作物农业统计区(ASD)的统计作物进展数据非常吻合。在ASD级别上,玉米物候期估计的RMSE为1.6 [R1]天至5.6 [R5]天,大豆为2.5 [R7]至5.3 [R5]天。

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