Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems

Qader, Sarchil; Utazi, C. Edson; Priyatikanto, Rhorom; Najmaddin, Peshawa; Hama-Ali, Emad Omer; Khwarahm, Nabaz R.; Tatem, Andrew J.; Dash, Jadunandan

doi:10.1016/j.scitotenv.2023.161716

Cited by 5 publications

(3 citation statements)

References 65 publications

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“…The fusion of structural and spectral parameters of crops was adopted in this study. Exploring multi-data fusion, such as thermal infrared, LiDAR, or environmental data, remains a future research focus ( Maimaitijiang et al., 2020 ; Li et al., 2022 ; Qader et al., 2023 ). In addition, in terms of machine learning algorithms, previous studies have used deep learning algorithms for yield prediction and have achieved good results ( Khaki and Wang, 2019 ; Khaki et al., 2020 ; Sagan et al., 2021 ; Jeong et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery

Zhou,

Yang,

Lou

et al. 2023

Front. Plant Sci.

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Rapid and accurate prediction of crop yield is particularly important for ensuring national and regional food security and guiding the formulation of agricultural and rural development plans. Due to unmanned aerial vehicles’ ultra-high spatial resolution, low cost, and flexibility, they are widely used in field-scale crop yield prediction. Most current studies used the spectral features of crops, especially vegetation or color indices, to predict crop yield. Agronomic trait parameters have gradually attracted the attention of researchers for use in the yield prediction in recent years. In this study, the advantages of multispectral and RGB images were comprehensively used and combined with crop spectral features and agronomic trait parameters (i.e., canopy height, coverage, and volume) to predict the crop yield, and the effects of agronomic trait parameters on yield prediction were investigated. The results showed that compared with the yield prediction using spectral features, the addition of agronomic trait parameters effectively improved the yield prediction accuracy. The best feature combination was the canopy height (CH), fractional vegetation cover (FVC), normalized difference red-edge index (NDVI_RE), and enhanced vegetation index (EVI). The yield prediction error was 8.34%, with an R2 of 0.95. The prediction accuracies were notably greater in the stages of jointing, booting, heading, and early grain-filling compared to later stages of growth, with the heading stage displaying the highest accuracy in yield prediction. The prediction results based on the features of multiple growth stages were better than those based on a single stage. The yield prediction across different cultivars was weaker than that of the same cultivar. Nevertheless, the combination of agronomic trait parameters and spectral indices improved the prediction among cultivars to some extent.

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Section: Discussionmentioning

confidence: 99%

Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery

Zhou,

Yang,

Lou

et al. 2023

Front. Plant Sci.

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“…However, most of the approaches are applied in small-scale areas, A maize distribution product in China based on a 10-meter spatial resolution has not yet emerged in the current literature due to three potential issues. First, unlike the intensive croplands in developed countries, most agriculture in China is in the form of smallholdings with irregular planting patterns and uncertain farm sizes, which may impede maize mapping 20 , 21 . Second, the lack of ground truth labels and the interference of noise may decrease the performance of mapping dramatically.…”

Section: Background and Summarymentioning

confidence: 99%

Mapping annual 10-m maize cropland changes in China during 2017–2021

Li,

Qu,

Geng

et al. 2023

Sci Data

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China contributed nearly one-fifth of the world maize production over the past few years. Mapping the distributions of maize cropland in China is crucial to ensure global food security. Nonetheless, 10 m maize cropland maps in China are still unavailable, restricting the promotion of sustainable agriculture. In this paper, we collect numerous samples to produce annual 10-m maize cropland maps in China from 2017 to 2021 with a machine learning based classification framework. To overcome the temporal variations of plants, the proposed framework takes Sentinel-2 sequence images as input and utilizes deep neural networks and random forest as classifiers to map maize in a zone-specific way. The generated maps have an overall accuracy (OA) spanning from 0.87 to 0.95 and the maize-cultivated areas estimated by the maps are highly consistent with the records in statistical yearbooks (R2 varying from 0.83 to 0.95). To the best of our knowledge, this is the first annual 10-m maize maps across China, which largely facilitates the sustainable agriculture development in China dominated by smallholder farmlands.

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“…The utilization of remote sensing data is prevalent in crop growth monitoring (Wang et al., 2023) and yield prediction (Qader et al., 2023) due to its advantages of high resolution, low cost, and vast coverage. Choosing remote sensing indicators that are reflective of growth circumstances and closely related to agricultural growth processes is a common step in monitoring crop growth and yield estimation.…”

Section: Introductionmentioning

confidence: 99%

Study on the impact of low‐temperature stress on winter wheat based on multi‐model coupling

Chen,

Zhang,

Liu

et al. 2024

Food and Energy Security

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Crop growth models, such as the WOrld FOod STudies (WOFOST) model, mimic the mechanistic processes involved in crop development, growth, and yield production. The accuracy of simulation is decreased in unfavorable low‐temperature settings because these models do not accurately represent crop response processes in low‐temperature stress. Enhancing the WOFOST crop growth model's accuracy in simulating crops' responses to cold temperatures is the aim of this work. Given its vulnerability to low temperatures, the inquiry uses winter wheat in Henan Province as a focal point. It integrates the WHEATGROW wheat phenology model with the Frost model of Lethal Temperature 50 (FROSTOL) inside the framework of the crop growth model. This link aims to improve simulation accuracy and supplement the model's mechanisms, particularly when it comes to the impact of low temperatures on crop development. The study uses Long Short‐Term Memory networks to build a yield model that integrates remote sensing data with information from simulated crop models. Under low temperatures, the leaf area index, total above ground biomass, and total weight of storage organs of the model WWF—which combines FROSTOL and WHEATGROW with WOFOST—show a considerable decline. It was discovered that there is a greater improvement in simulation accuracy of the linked model WWF relative to the WOFOST model in frost years than in normal years, based on a comparison analysis between typical frost years and normal years. To be more precise, the improvement is 8.03% in frost years and 1.98% in regular years. When all is said and done, the coupled model advances our knowledge of how winter wheat is impacted by low temperatures.

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Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems

Cited by 5 publications

References 65 publications

Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery

Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery

Mapping annual 10-m maize cropland changes in China during 2017–2021

Study on the impact of low‐temperature stress on winter wheat based on multi‐model coupling

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