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 (NDVI) of the current year and past years at a pixel scale on the same calendar day. The assumption of this comparison is that the different years’ crops were at the same growing stage on the same day. However, this assumption is often violated in reality. This paper proposes to combine remotely sensed data and meteorological data to assess corn growth conditions at the same growth stages at county level. The proposed approach uses the active accumulated temperature (AAT) computed from Daymet, a daily weather data product, to align different years of NDVI time series at the same growth stages estimated from AATs. The study area covers Carroll County, Iowa. The best index slope extraction (BISE) method and Savitzky–Golay filter are used to filter noise and to reconstruct 11 years of corn growing season NDVI time series from 250 m MODIS daily surface reflectance data product (MOD09GQ). The corn growth stages are identified every year with precise Julian dates from AAT time series. The corn growth conditions are assessed based on the aligned growth stages. The validation of the assessed crop conditions is performed based on National Agricultural Statistics Service (NASS) reports. The study indicates that the crop condition assessment results based on aligned growth stages are consistent with the NASS reported results and they are more reliable than the results based on the same calendar days. The proposed method provides not only crop growth condition information but also crop phenology information. Potentially, it can help improve crop yield prediction since it can effectively measure crop growth changes with NDVI and AAT data.
Highlights
Crop growth condition monitoring is critical to decision making in both public and private sectors that concern agricultural policy, production, food security, and food prices
The study aimed to develop a reliable method for large scale operational crop growth condition monitoring, which enabled us to derive county or sub-county crop specific growth condition assessment with no crop mismatch
The accumulated temperature (AAT)-aligned approach proposed in this paper provides a solution for crop condition assessment based on aligned crop growth stages
Summary
Crop growth condition monitoring is critical to decision making in both public and private sectors that concern agricultural policy, production, food security, and food prices. Index (NDVI) has been widely used for research and operations of crop condition assessment [8,9,10,11,12,13]. NDVI changes dynamically with crop biomass changing from its emergence to maturity [14,15,16]. It has become the indicator for crop growth condition monitoring [12,13,14,15,16,17]. For the same crop at the same growth stage, the higher NDVI value usually indicates better vegetation condition, i.e., better crop (growth) condition
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