A 30 m annual maize phenology dataset from 1985 to 2020 in China

Niu, Quandi; Li, Xuecao; Huang, Jianxi; Huang, Hai; Huang, Xianda; Su, Wei; Yuan, Wenping

doi:10.5194/essd-14-2851-2022

Cited by 22 publications

(13 citation statements)

References 49 publications

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“…Remote Sens. 2022, 14, 5005 3 of 13 to late September [44]. In the growing season, the dominant wind direction in the study area is south-southwest and the mean wind speed is 2.9 m/s.…”

Section: Study Areamentioning

confidence: 95%

“…It is located in the temperate semi-arid and semi-humid climate zone with an annual mean temperature of 1-7 • C and annual total precipitation of 300-550 mm [6]. The major crop is corn, which is grown from mid-April to late September [44]. In the growing season, the dominant wind direction in the study area is south-southwest and the mean wind speed is 2.9 m/s.…”

Section: Study Areamentioning

confidence: 99%

“…Corn yield near the shelterbelt was estimated along the corn strips in each target shelterbelt. Niu's corn phenology dataset [44] was used to identify the corn farmlands [44]. To highlight the impact of the shelterbelt on corn yield and exclude the potential influence of other factors, we used four criteria to select corn strips: (1) the strips were located in the middle of the target shelterbelt to minimize the influence of shelterbelt edge effect on corn yield; (2) the strips were homogenous in crop type (only corn) and continuous (no gap); (3) the strips were aligned with the direction of planting rows to ensure field management consistency; (4) the corn row-shelterbelt angle was greater than 60° to minimize the influence of the shelterbelt edge.…”

Section: Selection Of Target Shelterbelt and Corn Stripsmentioning

confidence: 99%

“…The localized linear model estimated the variability of corn yield well at within-field after Corn yield near the shelterbelt was estimated along the corn strips in each target shelterbelt. Niu's corn phenology dataset [44] was used to identify the corn farmlands [44]. To highlight the impact of the shelterbelt on corn yield and exclude the potential influence of other factors, we used four criteria to select corn strips: (1) the strips were located in the middle of the target shelterbelt to minimize the influence of shelterbelt edge effect on corn yield; (2) the strips were homogenous in crop type (only corn) and continuous (no gap);…”

Section: Estimation Of the Corn Yieldmentioning

confidence: 99%

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Estimating the Legacy Effect of Post-Cutting Shelterbelt on Crop Yield Using Google Earth and Sentinel-2 Data

Liu

et al. 2022

Remote Sensing

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Shelterbelts (or windbreaks) can effectively improve the microclimate and soil conditions of adjacent farmland and thus increase crop yield. However, the individual contribution of these two factors to yield changes is still unclear since the short-term effect from the microclimate and the accumulated effect from the soil jointly affect crop yield. The latter (soil effect) is supposed to remain after shelterbelt-cutting, thus inducing a post-cutting legacy effect on yield, which can be used to decompose the shelterbelt-induced yield increase. Here, we develop an innovative framework to investigate the legacy effect of post-cutting shelterbelt on corn yield by combining Google Earth and Sentinel-2 data in Northeastern China. Using this framework, for the first time, we decompose the shelterbelt-induced yield increase effect into microclimate and soil effects by comparing the yield profiles before and after shelterbelt-cutting. We find that on average, the intensity of the legacy effect, namely the crop yield increment of post-cutting shelterbelts, is 0.98 ± 0.03%. The legacy effect varies depending on the shelterbelt–farmland relative location and shelterbelt density. The leeward side of the shelterbelt-adjacent farmland has a more remarkable legacy effect compared to the windward side. Shelterbelts with medium–high density have the largest legacy effect (1.94 ± 0.05%). Overall, the legacy effect accounts for 47% of the yield increment of the shelterbelt before cutting, implying that the soil effect is almost equally important for increasing crop yield compared to the microclimate effect. Our findings deepen the understanding of the mechanism of shelterbelt-induced yield increase effects and can help to guide shelterbelt management.

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“…Remote Sens. 2022, 14, 5005 3 of 13 to late September [44]. In the growing season, the dominant wind direction in the study area is south-southwest and the mean wind speed is 2.9 m/s.…”

Section: Study Areamentioning

confidence: 95%

Section: Study Areamentioning

confidence: 99%

Section: Selection Of Target Shelterbelt and Corn Stripsmentioning

confidence: 99%

Section: Estimation Of the Corn Yieldmentioning

confidence: 99%

See 2 more Smart Citations

Estimating the Legacy Effect of Post-Cutting Shelterbelt on Crop Yield Using Google Earth and Sentinel-2 Data

Liu

et al. 2022

Remote Sensing

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“…On the other hand, there are dozens of recent papers that focus on the large-scale prediction of phenology for other major crops [39,40]. Indicatively, there have been interesting recent studies on maize [31,[41][42][43][44], rice [43,45,46], wheat [43,47,48] and soybean [31,41,44].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy clustering for the within-season estimation of cotton phenology

et al. 2023

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Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level. For this, we exploit a variety of Earth observation vegetation indices (derived from Sentinel-2) and numerical simulations of atmospheric and soil parameters. Our method is unsupervised to address the ever-present problem of sparse and scarce ground truth data that makes most supervised alternatives impractical in real-world scenarios. We applied fuzzy c-means clustering to identify the principal phenological stages of cotton and then used the cluster membership weights to further predict the transitional phases between adjacent stages. In order to evaluate our models, we collected 1,285 crop growth ground observations in Orchomenos, Greece. We introduced a new collection protocol, assigning up to two phenology labels that represent the primary and secondary growth stage in the field and thus indicate when stages are transitioning. Our model was tested against a baseline model that allowed to isolate the random agreement and evaluate its true competence. The results showed that our model considerably outperforms the baseline one, which is promising considering the unsupervised nature of the approach. The limitations and the relevant future work are thoroughly discussed. The ground observations are formatted in an ready-to-use dataset and will be available at https://github.com/Agri-Hub/cotton-phenology-dataset upon publication.

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