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Many commodity crops have growth stages during which they are particularlyvulnerable to stress-induced yield loss. In-season crop progress information isuseful for quantifying crop risk, and satellite remote sensing (RS) can be usedto track progress at regional scales. At present, all existing RS-based cropprogress estimation (CPE) methods which target crop-specific stages rely onground truth data for training/calibration. This reliance on ground survey dataconfines CPE methods to surveyed regions, limiting their utility. In thisstudy, a new method is developed for conducting RS-based in-season CPE inunsurveyed regions by combining data from surveyed regions with synthetic cropprogress data generated for an unsurveyed region. Corn-growing zones inArgentina were used as surrogate 'unsurveyed' regions. Existing weathergeneration, crop growth, and optical radiative transfer models were linked toproduce synthetic weather, crop progress, and canopy reflectance data. A neuralnetwork (NN) method based upon bi-directional Long Short-Term Memory wastrained separately on surveyed data, synthetic data, and two differentcombinations of surveyed and synthetic data. A stopping criterion was developedwhich uses the weighted divergence of surveyed and synthetic data validationloss. Net F1 scores across all crop progress stages increased by 8.7% whentrained on a combination of surveyed region and synthetic data, and overallperformance was only 21% lower than when the NN was trained on surveyed dataand applied in the US Midwest. Performance gain from synthetic data wasgreatest in zones with dual planting windows, while the inclusion of surveyedregion data from the US Midwest helped mitigate NN sensitivity to noise in NDVIdata. Overall results suggest in-season CPE in other unsurveyed regions may bepossible with increased quantity and variety of synthetic crop progress data.

In-Season Crop Progress in Unsurveyed Regions using Networks Trained on Synthetic Data