Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

Gao, Feng; Zhang, Xiaoyang

doi:10.34133/2021/8379391

Cited by 111 publications

(82 citation statements)

References 60 publications

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“…Many remote sensing algorithms have been developed in recent decades for estimating transition dates from VI timeseries. They can be categorized in the threshold, curvature, trend, and priori curve-based approach [34]. The threshold-based approach uses the predefined threshold of amplitude of VI (difference between base and maximum VI) to detect SOS, EOS, and other phenological metrics [3,6,7].…”

Section: Detection Methods and Analysismentioning

confidence: 99%

“…Many remote sensing algorithms have been developed in recent decades for estimating transition dates from VI time series, and can be categorized in four broad categories: threshold, curvature and inflection, trend, and priori curve-based approach [8, 43]. Here we use two approaches to evaluate LSP dynamics in drylands: a threshold and inflection (max rate of change) model.…”

Section: Methodsmentioning

confidence: 99%

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Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

Taylor

Browning

Baca

et al. 2021

Preprint

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Land surface phenology, the tracking of seasonal productivity via satellite remote sensing, enables global scale tracking of ecosystem processes, but its utility is limited in some areas. In dryland ecosystems low vegetation cover can cause the growing season vegetation index (VI) to be indistinguishable from the dormant season VI, making phenology extraction impossible. Here, using simulated data and multi-temporal UAV imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, and VI uncertainty. We found that plants with distinct VI signals, such as deciduous shrubs with a high leaf area index, require at least 30-40\% fractional cover on the landscape to consistently detect pixel level phenology with satellite remote sensing. Evergreen plants, which have lower VI amplitude between dormant and growing seasons, require considerably higher cover and can have undetectable phenology even with 100\% vegetation cover. We also found that even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100\% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. Many dryland areas do not have detectable LSP with the current suite of satellite based sensors. Our results showed the feasibility of dryland LSP studies using high-resolution UAV imagery, and highlighted important scale effects due to within canopy VI variation. Future sensors with sub-meter resolution will allow for identification of individual plants and are the best path forward for studying large scale phenological trends in drylands.

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Section: Detection Methods and Analysismentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

Taylor

Browning

Baca

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Detection Methods and Analysis. Many remote sensing algorithms have been developed in recent decades for estimating transition dates from VI time series and can be categorized in four broad categories: threshold, curvature and inflection, trend, and priori curve-based approach [8,44]. Here, we use two approaches to evaluate LSP dynamics in drylands: a threshold and inflection (max rate of change) model.…”

Section: 2mentioning

confidence: 99%

Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

et al. 2021

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Land surface phenology (LSP) enables global-scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle, it is necessary to understand the limitations in measuring dryland dynamics. Here, using simulated data and multitemporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data, we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands and highlighted important scale effects driven by within-canopy VI variation. With high-resolution imagery, the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research.

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“…Various vegetation phenology detecting methods have been developed in previous studies. However, mapping crop phenology is still challenging because the land surface vegetation dynamics or remote sensing phenology is different from crop physiological growth stages [47]. In this study, we detected the crop planting dates, harvesting dates, and growing season length for corn and soybean using an NDVI curve-change-based dynamic threshold approach.…”

Section: Trends Of Crop Yield and Its Correlation With Crop Phenologymentioning

confidence: 99%

Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky

et al. 2021

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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.

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Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

Cited by 111 publications

References 60 publications

Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky

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