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Use of AVHRR NDVI time series and ground-based surveys for estimating county-level crop biomass

机译：利用AVHRR NDVI时间序列和地面调查估算县级作物生物量

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摘要

Crop biomass and residue production are major components of cropland carbon dynamics that can be estimated using yield data from ground-based surveys. In the USA, surveyed yield data arc available at county level and have been widely used for various research, economic and policy purposes, in addition to biomass estimation. However, survey data may be unavailable for certain times and/or locations and thus biomass estimates using remotely sensed data might be used to fill in any missing biomass data for estimating residue production and carbon dynamics in croplands. Compared to ground-based surveys, remotely sensed data are collected on a regular schedule and may also provide more spatially resolved data. We analysed composite biweekly Normalized Difference Vegetation Index (NDVI) data obtained using the Advanced Very High Resolution Radiom eter (AVHRR) sensor and crop aboveground biomass (AGBM) estimated from available county-level yield data reported by the National Agricultural Statistics Service (NASS) for three crops (corn, soybean and oats) during 1992, 1997 and 2002. The aim of the study was to explore the relationships between NDVI and crop biomass to complete the missing biomass data in counties where no NASS-rcported yields are available for biomass estimation.rnAGBM was estimated from Pathfinder biweekly NDVI, using canonical correlation analysis (CCA) and best subset multiple regressions incorporating canonical variates from NDVI time series. Cross-validation of model estimates was performed by randomly splitting the dataset into training and application subsets, simulating a 10 40% range of missing values. NDVI and crop biomass in Iowa during a given year were well correlated, with coefficient of determination (R~2) values > 0.8 in most cases. Using the available (training) data from a single year or a combination of years to derive models for filling the missing (validation) data within the same time period yielded a mean estimated biomass with < 1% relative error and bias. However, models applied to out-of-samplc years had lower (< 0.4) R~2 values for the relationships between biomass and NDVI, although the mean residuals were low.
机译：作物生物量和残渣生产是农田碳动态的主要组成部分，可以使用基于地面调查的产量数据进行估算。在美国，调查的产量数据可在县级获得，除生物量估算外，还广泛用于各种研究，经济和政策目的。但是，调查数据可能在某些时间和/或位置不可用，因此使用遥感数据进行的生物量估算可用于填写任何缺失的生物量数据，以估算农田中的残留物产量和碳动态。与基于地面的勘测相比，遥感数据是定期收集的，并且还可以提供更多空间分辨的数据。我们分析了使用先进超高分辨率辐射计（AVHRR）传感器和农作物地上生物量（AGBM）获得的双周标准化差异植被指数（NDVI）数据，这些数据是根据国家农业统计局（NASS）报告的可用县级产量数据估算得出的在1992年，1997年和2002年期间对三种作物（玉米，大豆和燕麦）进行了研究。该研究的目的是探索NDVI与作物生物量之间的关系，以完成没有NASS得出的生物量单产的县中缺失的生物量数据。使用标准相关分析（CCA）和结合NDVI时间序列的典型变量的最佳子集多元回归，从Pathfinder双周NDVI估计AGBM。通过将数据集随机分为训练和应用子集，模拟10％到40％的缺失值范围，进行模型估计的交叉验证。在特定年份，爱荷华州的NDVI与作物生物量之间的相关性很好，在大多数情况下，测定系数（R〜2）值> 0.8。使用一年或几年的组合中的可用（培训）数据来导出用于填充同一时间段内丢失（验证）数据的模型，得出的平均估计生物量的相对误差和偏差<1％。然而，尽管平均残差很低，但用于样本外年份的模型对于生物量和NDVI之间的关系具有较低的（<0.4）R〜2值。

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  • 来源
    《International journal of remote sensing》 |2010年第2期|141-158|共18页
  • 作者

    E. LOKUPITIYA; M. LEFSKY; K. PAUSTIAN;

  • 作者单位

    Department of Soil and Crop Sciences and Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA;

    rnDepartment of Forest, Rangeland, and Watershed Stewardship, Colorado State University, CO 80523, USA;

    rnDepartment of Soil and Crop Sciences and Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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