Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology

Abstract

Detecting crop phenology with satellite time series is important to characterize agroecosystem energy-water-carbon fluxes, manage farming practices, and predict crop yields. Despite the advances in satellite-based crop phenological retrievals, interpreting those retrieval characteristics in the context of on-the-ground crop phenological events remains a long-standing hurdle. Over the recent years, the emergence of near-surface phenology cameras (e.g., PhenoCams), along with the satellite imagery of both high spatial and temporal resolutions (e.g., PlanetScope imagery), has largely facilitated direct comparisons of retrieved characteristics to visually observed crop stages for phenological interpretation and validation. The goal of this study is to systematically assess near-surface PhenoCams and high-resolution PlanetScope time series in reconciling sensor- and ground-based crop phenological characterizations. With two critical crop stages (i.e., crop emergence and maturity stages) as an example, we retrieved diverse phenological characteristics from both PhenoCam and PlanetScope imagery for a range of agricultural sites across the United States. The results showed that the curvature-based Greenup and Gu-based Upturn estimates showed good congruence with the visually observed crop emergence stage (RMSE about 1 week, bias about 0–9 days, and R square about 0.65–0.75). The threshold- and derivative-based End of greenness falling Season (i.e., EOS) estimates reconciled well with visual crop maturity observations (RMSE about 5–10 days, bias about 0–8 days, and R square about 0.6–0.75). The concordance among PlanetScope, PhenoCam, and visual phenology demonstrated the potential to interpret the fine-scale sensor-derived phenological characteristics in the context of physiologically well-characterized crop phenological events, which paved the way to develop formal protocols for bridging ground-satellite phenological characterization.

Multi-year monitoring of wheat phenology and effect of climate change in the south Asian region using Sentinel-2 NDVI time series analysis

“…Therefore, the proposed method of data collection and phenology estimation for a heterogeneous site through trend-based temporal analysis accurately identifies five phenological stages of wheat in near-real time as opposed to numerous studies which considered only the sowing and harvest stages. 12,16 Additionally, the stages were detected with a minimum difference of 1 day across multi-years, where weather varied largely, unlike 2,10,11,17 which detected phenology for a single year only, limiting the transferability of the approaches followed and hence, the results and approach of this study highlight the capability of the proposed method to be transferable to multiple years with varying weather conditions and number of satellite observations for a heterogeneous crop distribution site. In addition to that, none of the previously conducted work studies the effect of the rate of change on 5 different phenological stages in wheat.…”

“…The results of Planet-Scope satellite are compared with that of PhenoCams to detect the emergence and maturity stage in. 12 Although, Planet-Scope provides high resolution data, it is not free hence, limiting the utilization.…”

“…The authors conclude that higher temperatures lead to shorter growth duration however, the change in temperature and rainfall is constant throughout the study years. Limited work has been carried out in the domain of phenology estimation and, the studies have considered detecting only the sowing and harvest dates of crops 12,16 rather than focusing on the significant phenological stages through out a crops life cycle which holds critical information for yield estimation and crop management strategies. Moreover, the studies have carried out the detection for single year 2,6,10,11,17 which limits the applicability of the method for future data since, satellite data is largely effected by atmospheric variables and the weather varies significantly each year.…”

Spatial and Temporal Evolution of Sowing and the Onset of the Rainy Season in a Region of Large Agricultural Expansion in Brazil

“…We assumed that a high concordance with field data is achieved when several methods point to the same green-up date pattern. This approach has shown to be effective in capturing green-up dates in different areas, as shown in [14,[31][32][33][34][35][36]. The best green-up methods were determined through the comparison of the sowing dates calculated in this study (Section 2.4) with observed sowing dates, and were therefore applied to the entire region and time-series.…”

Towards Scalable Within-Season Crop Mapping With Phenology Normalization and Deep Learning

“…Crop phenology characterization using time-series remote sensing data has been explored in recent years, yet withinseason crop phenology detection remains a challenging task [27], [28]. With time-series VI curves, crop phenology was derived by phenophase extraction methods in terms of curve characteristics (e.g., predefined threshold, curve derivative, and curvature change rate) and phenology matching models (e.g., shape model and hybrid phenology matching model) [29], [30], [31], [32], [33], [34], [35], [36]. However, those abovementioned methods were mostly designed with whole-season VI curves, which made them hardly applicable within the growing season [27].…”