Abstract
Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 and 0.07 days/year, respectively. Corn harvesting dates were also delayed at a rate of 0.67 days/year, while advanced soybean harvesting occurred at a rate of 0.05 days/year. The growing season length has increased considerably at a rate of 0.66 days/year for corn and was shortened by 0.12 days/year for soybean. Sensitivity analysis showed that planting dates were more sensitive to the early season temperature, while harvesting dates were significantly correlated with temperature over the entire growing season. In terms of the changing climatic factors, only the increased summer precipitation was statistically related to the delayed corn harvesting dates in Kentucky. Further analysis showed that the increased corn yield was significantly correlated with the delayed harvesting dates (1.37 Bu/acre per day) and extended growing season length (1.67 Bu/acre per day). Our results suggested that seasonal climate change (e.g., summer precipitation) was the main factor influencing crop phenological trends, particularly corn harvesting in Kentucky over the study period. We also highlighted the critical role of changing crop phenology in constraining crop production, which needs further efforts for optimizing crop management practices.
Highlights
IntroductionAccurate retrieval of crop phenology information is a prerequisite for evaluating crop adaptation to climate change, modeling agricultural ecosystem carbon exchange, and predicting future agricultural production [2,3,4,5]
We identified crop phenological dates based on the characteristics of the derivatives: Heading dates: Previous studies have shown that the maximum Normalized Differential Vegetation Index (NDVI) occurs around the heading dates [41]
The state-level evaluation results showed that crop phenology estimated by remote sensing was at a high level of agreement with the crop reports from the survey data (Figure 3)
Summary
Accurate retrieval of crop phenology information is a prerequisite for evaluating crop adaptation to climate change, modeling agricultural ecosystem carbon exchange, and predicting future agricultural production [2,3,4,5]. Panel on Climate Change has reported a change in global mean temperature of 1.5 ◦ C above pre-industrial levels, along with changes in precipitation and an increased frequency of extreme climate events (IPCC, 2018). This shift in climate may bring varying degrees of impacts on agricultural ecosystems at different temporal and spatial scales.
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