Developing an operational algorithm for near-real-time monitoring of crop progress at field scales by fusing harmonized Landsat and Sentinel-2 time series with geostationary satellite observations

Shen, Yu; Zhang, Xiaoyang; Yang, Zhengwei; Ye, Yongchang; Jian‐min, Wang; Gao, Shuai; Liu, Yuxia; Wang, Weile; Tran, Khuong H.; Ju, Junchang

doi:10.1016/j.rse.2023.113729

Cited by 15 publications

(4 citation statements)

References 85 publications

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“…The spatial reference system of the dataset is WGS_1984_UTM_Zone_49N. The dataset currently includes mean calendar dates during five periods: 2003-2007, 2008-2012, 2013-2017, 2018-2022, and 2003-2022. ChinaRiceCalendar is available at https://doi.org/10.7910/DVN/EUP8EY (Liu et al, 2023).…”

Section: Data Availabilitymentioning

confidence: 99%

“…However, current regional-level rice calendar datasets do not accurately distinguish between rice seasons in China, causing uncertainty in crop model simulation and climate change impact analysis. Based on satellite remote sensing data, we extracted transplanting, heading, and maturity dates of early-, middle-, and late-season rice across China from 2003 to 2022 and established a multi-season rice calendar dataset named ChinaRiceCalendar (https://doi.org/10.7910/DVN/EUP8EY, Liu et al, 2023). Overall, the ChinaRiceCalendar dataset shows good agreement with field-observed phenological dates of early-, middle-, and late-season rice in Chinese agricultural meteorological stations (AMSs).…”

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confidence: 99%

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ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China

Li,

Wang,

Wang

et al. 2024

Earth Syst. Sci. Data

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Abstract. Long time series and large-scale rice calendar datasets provide valuable information for agricultural planning and field management in rice-based cropping systems. However, current regional-level rice calendar datasets do not accurately distinguish between rice seasons in China, causing uncertainty in crop model simulation and climate change impact analysis. Based on satellite remote sensing data, we extracted transplanting, heading, and maturity dates of early-, middle-, and late-season rice across China from 2003 to 2022 and established a multi-season rice calendar dataset named ChinaRiceCalendar (https://doi.org/10.7910/DVN/EUP8EY, Liu et al., 2023). Overall, the ChinaRiceCalendar dataset shows good agreement with field-observed phenological dates of early-, middle-, and late-season rice in Chinese agricultural meteorological stations (AMSs). According to the calendar data from 2003 to 2022 in China, the transplanting dates for early-, middle-, and late-season rice shifted by +0.7, −0.7, and −5.1 DOY (day of year) per decade, respectively; the heading dates for early-, middle-, and late-season rice shifted by −0.5, +2.7, and −0.6 DOY per decade, respectively; the maturity dates for early-, middle-, and late-season rice shifted by −0.7, +3.8, and −1.6 DOY per decade, respectively. ChinaRiceCalendar can be utilized to investigate and optimize the spatiotemporal structure of rice cultivation in China under climate and land use change.

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Section: Data Availabilitymentioning

confidence: 99%

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confidence: 99%

ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China

Li,

Wang,

Wang

et al. 2024

Earth Syst. Sci. Data

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“…For the country-wide assessment, L-WRSI was grouped into four qualitative categories of Good (L-WRSI > 95%), Fair (80-95%), Poor (50-80%), and Severe Damage (L-WRSI < 50%). A summary of the L-WRSI by croplands [56] of the CONUS (Figure 16) shows the drought year of 2012 had 66% of the CONUS under severe damage whereas 2016 and 2018 experienced severe damage to a lesser extent (26-27%). The extent observed in 2016 and 2018 may represent the areas that normally require irrigation for crop production.…”

Section: Conusmentioning

confidence: 99%

Improvements and Evaluation of the Agro-Hydrologic VegET Model for Large-Area Water Budget Analysis and Drought Monitoring

Senay

Kagone²,

Parrish³

et al. 2023

Hydrology

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We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by land surface phenology (LSP), the enhanced VegET model was implemented in the cloud to simulate daily soil moisture (SM), actual evapotranspiration (ETa), and runoff (R) for the conterminous United States (CONUS) and the Greater Horn of Africa (GHA). Evaluation of the VegET model with independent data showed satisfactory performance, capturing the temporal variability of SM (Pearson correlation r: 0.22–0.97), snowpack (r: 0.86–0.88), ETa (r: 0.41–0.97), and spatial variability of R (r: 0.81–0.90). Absolute magnitudes showed some biases, indicating the need of calibrating the model for water budget analysis. The seasonal Landscape Water Requirement Satisfaction Index (L-WRSI) for CONUS and GHA showed realistic depictions of drought hazard extent and severity, indicating the usefulness of the L-WRSI for the convergence of an evidence toolkit used by the Famine Early Warning System Network to monitor potential food insecurity conditions in different parts of the world. Using projected weather datasets and landcover-based LSP, the VegET model can be used not only for global monitoring of drought conditions, but also for evaluating scenarios on the effect of a changing climate and land cover on agriculture and water resources.

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“…Niu et al [22] extracted the three-leaves date (V3) and maturity date (MD) of maize from Landsat data in the past 30 years. Moreover, Shen et al [23] used harmonized Landsat and Sentinel-2 to monitor crop progress at field scales. However, these optical data are easily affected by clouds and rainfall, which decrease phenology monitoring accuracy.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold

Ma,

Jiang,

Huang

et al. 2024

Remote Sensing

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Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected by cloud occlusion, have been widely applied in monitoring maize phenology. Nonetheless, their reliance on manual threshold settings, which depend on the user’s expertise, limits their applicability. Furthermore, the neglect of SAR’s potential for monitoring other phenological periods (e.g., seven-leaves date (V7), jointing date (JD), tassel date (TD), and milky date (MID)) hinders their robustness, particularly for regional-scale applications. To address these issues, this study used an adaptive dynamic threshold to evaluate the ability of the Sentinel-1 cross-polarization ratio (CR) in detecting the three-leaves date (V3), V7, JD, TD, MID, and maturity date (MD) of maize. We analyzed the effect of incidence angle, precipitation, and wind speed on Sentinel-1 features to identify the optimal feature for time series fitting. Then, we employed linear regression to determine the optimal threshold and developed an adaptive dynamic threshold for phenology detection. This approach effectively mitigated the speckle noise of Sentinel-1 and minimized artificial interference caused by customary conventional thresholds. Finally, we mapped phenology across 8.3 million ha in Heilongjiang Province. The results indicated that the approach has a higher ability to detect JD (RMSE = 11.10 d), MID (RMSE = 10.31 d), and MD (RMSE = 9.41 d) than that of V3 (RMSE = 32.07 d), V7 (RMSE = 56.37 d), and TD (RMSE = 43.33 d) in Sentinel-1. Compared with Sentinel-2, the average RMSE of JD, MID, and MD decreased by 4.14%, 35.28%, and 26.48%. Moreover, when compared to different thresholds, the adaptive dynamic threshold can quickly determine the optimal threshold for detecting each phenological stage. CR is least affected by incident angle, precipitation, and wind speed, effectively suppressing noise to reflect phenological development better. This approach supports the rapid and feasible mapping of maize phenology across broad spatial regions with a few samples.

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Developing an operational algorithm for near-real-time monitoring of crop progress at field scales by fusing harmonized Landsat and Sentinel-2 time series with geostationary satellite observations

Cited by 15 publications

References 85 publications

ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China

ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China

Improvements and Evaluation of the Agro-Hydrologic VegET Model for Large-Area Water Budget Analysis and Drought Monitoring

Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold

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