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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #363817

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Crop growth condition assessment at county scale based on heat-aligned growth stages

item QUAN, Y. - National Meteorological Center
item YANG, Z. - US Department Of Agriculture (USDA)
item DI, L. - George Mason University
item RAHMAN, M. - George Mason University
item XUE, L. - National Meteorological Center
item TRAN, Z. - Chinese Academy Of Surveying And Mapping
item Gao, Feng
item YU, E. - George Mason University
item ZHANG, X. - South Dakota State University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/11/2019
Publication Date: 10/21/2019
Citation: Quan, Y., Yang, Z., Di, L., Rahman, M., Xue, L., Tran, Z., Gao, F.N., Yu, E., Zhang, X. 2019. Crop growth condition assessment at county scale based on heat-aligned growth stages. Remote Sensing. 11:2439.

Interpretive Summary: Crop growth condition information can benefit farmers in scheduling irrigation, fertilization and harvest operations. Satellite remote sensing data have been used for crop condition monitoring for several decades. Traditional approaches compare Vegetation Indices (VI) of the current year to previous years on the same calendar date. These approaches ignore the yearly variability of crop growth stages and may not be effective. This paper proposes an approach to compare VI at the same crop growth stages estimated from active accumulated temperature. Results from Carroll County, Iowa show that the crop growth condition assessment using aligned growth stages are more consistent to the reported National Agricultural Statistics Service (NASS) results than using aligned calendar dates. This approach provides an effective way to map crop condition, which is required by the National Agricultural Statistics Service and Foreign Agricultural Service for crop yield estimation.

Technical Abstract: Remotely sensed data have been used in crop condition monitoring for decades. Traditionally, crop growth conditions were assessed by comparing Normalized Difference Vegetation Index (NDVIs) of current year to historical years at pixel scale on the same calendar day. The assumption of this comparison is that the different crops were at the same growing stage on the same day. However, this assumption is often violated in reality. This paper proposes to use multisource data from remotely sensed data and meteorological data to assess corn growth conditions at a county level for a potential operational application. The proposed approach uses the crop growth stages estimated from active accumulated temperature (AAT) computed from Daymet data to align two different years of NDVI time series at the same growth stage. study area covers Carroll County, Iowa. Eleven years of NDVI time series covering corn growing season were derived by using the best index slope extraction (BISE) and Savitzky-Golay filters to 250m MODIS daily surface reflectance data product (MOD09GQ). An averaged NDVI curve is computed from normal years as a baseline. The corn growth stages were identified for each year with a precise date from eleven years of AAT time series and an AAT baseline. The corn growth conditions were assessed with alignment on both growth stages and on Julian days. The study results indicate that the crop growth condition assessment results with aligned growth stages are consistent with the National Agricultural Statistics Service (NASS) reported results and better than the results with aligned Julian days. This finding indicates that the corn crop condition assessment based on the aligned Julian day may not be reliable. Overall, the NDVI curve can quantify crop growth condition changes departing from the baseline and the AAT provides the heat supply and changes, which are useful for further yield assessment.