A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery

作者：Feng Gao, Martha C. Anderson, Craig S. T. Daughtry, Arnon Karnieli, Dean Hively, William P. Kustas · 发表于：Remote Sensing of Environment · 年份：2020 · DOI：10.1016/j.rse.2020.111752 · 被引用次数：152 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Leaf Properties and Growth Measurement

Crop emergence date is a critical input to models of crop development and biomass accumulation. The ability to robustly detect and map emergence date using remote sensing would greatly benefit operational yield estimation and crop monitoring efforts; however, this has proven to be challenging. Previous remote-sensing phenology algorithms showed that crop stages can typically be detected starting only around the V3-V4 (3 to 4 established leaves) vegetative stage. Furthermore, traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for within-season operational mapping, particularly in the early growing season. In the current paper, we describe a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations available during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2-day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENμS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stage...