气候变化领域集成服务门户(CCPortal): A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt

CCPortal

DOI	10.1016/j.rse.2020.112174
	A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt
	Deines J.M.; Patel R.; Liang S.-Z.; Dado W.; Lobell D.B.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	Crop yield maps estimated from satellite data increasingly are used to understand drivers of yield trends and variability, yet satellite-derived regional maps are rarely compared with location-specific yields due to the difficulty of acquiring sub-field ground truth data at scale. In commercial agricultural systems, combine harvesters with onboard yield monitors collect real-time yield data during harvest with high spatial resolution, generating a large ground dataset. Here, we leveraged a yield monitor dataset of over one million maize field observations across the United States Corn Belt from 2008 to 2018 to evaluate the Scalable Crop Yield Mapper (SCYM). SCYM uses region-specific crop model simulations and climate data to interpret vegetation indices from satellite observations, thus enabling efficient sub-field yield estimation across large regions and multiple years without reliance on ground data calibration. We used the ground dataset to compare alternative SCYM model implementations, define minimum required satellite observation criteria, and evaluate the sensitivity of satellite-based yield estimates to on-the-ground variation in management, soil, and annual weather. We found that smoothing annual time series data with harmonic regression increased 30 m pixel-level accuracy from r2 = 0.31 to 0.40 and reduced dependency on specific satellite observation timing, enabling robust yield estimation on 97% of annual maize area using only Landsat data. Agreement improved as the assessment was aggregated to field-level (r2 = 0.45) and county-level (r2 = 0.69) scales, demonstrating the need for fine-resolution ground truth data to better assess sub-field level accuracy in high resolution products. We found that SCYM and ground data showed a similar yield response to management and environmental variation, particularly capturing linear and nonlinear responses to sowing density, soil water holding capacity, and growing season precipitation. However, sensitivity to factors like soil quality and planting date was muted for SCYM estimates compared to ground-based yields. Random forest models were able to match SCYM performance when trained on at least 1000 ground observations, but performed poorly when tested on years and locations not represented in the training data. Our results demonstrate that satellite yield maps can provide much-needed information on multidecadal yield trends and inform yield gap analyses. © 2020 The Authors
英文关键词	Agricultural monitoring; Crop yields; Landsat; US Corn Belt
语种	英语
scopus关键词	Agricultural robots; Climate models; Crops; Decision trees; Forestry; Large dataset; Real time systems; Sensitivity analysis; Soil moisture; Environmental variations; Ground observations; Harmonic regression; High spatial resolution; Model implementation; Non-linear response; Satellite observations; Soil water holdings; Satellites; accuracy assessment; calibration; crop yield; data set; growing season; maize; mapping method; performance assessment; satellite altimetry; satellite data; time series analysis; Corn Belt; United States; Miletinae; Varanidae
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179038
作者单位	Department of Earth System Science, Center on Food Security and the Environment, Stanford University, United States; Granular, A Corteva Agriscience™ Company, United States; Corteva Agriscience™, United States
推荐引用方式 GB/T 7714	Deines J.M.,Patel R.,Liang S.-Z.,et al. A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt[J],2021,253.
APA	Deines J.M.,Patel R.,Liang S.-Z.,Dado W.,&Lobell D.B..(2021).A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt.Remote Sensing of Environment,253.
MLA	Deines J.M.,et al."A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt".Remote Sensing of Environment 253(2021).