Ensemble learning based on remote sensing data for monitoring agricultural drought in major winter wheat-producing areas of China

Wang, Lunche; Zhang, Yuefan; Chen, Xinxin; Liu, Yuting; Wang, Shaoqiang; Wang, Lizhe

doi:10.1177/03091333231188814

Cited by 4 publications

(1 citation statement)

References 59 publications

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“…The script was run in Google Earth Engine and consisted of two separate calculations: one for the "common" SPI (n-month) and the other based on MODIS capture dates. Spectral precipitation index (SPI) can be utilized to observe variations in precipitation and investigate the influence of climate change on Earth's precipitation patterns (Wang et al, 2023). The Standardized Precipitation Evapotranspiration Index (SPEI) is a drought index that relies on data regarding precipitation and evapotranspiration (Sadiq et al, 2023).…”

Section: 𝑁𝐷𝑉𝐼 = 𝑁𝐼𝑅 − 𝑅𝑒𝑑 𝑁𝐼𝑅 + 𝑅𝑒𝑑mentioning

confidence: 99%

Spatiotemporal Drought Assessment in Ningxia Autonomous Region: A Machine Learning and Remote Sensing Approach

Awais,

Zaheen,

Fatima

et al. 2024

ABBDM

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Drought represents a significant disaster that directly impacts the economic and ecological welfare of any nation it afflicts. This study focused on the climatic anomalies of drought over the Ningxia Hui autonomous region in northwest China over the last two decades. The study employed an in-depth machine learning model, which incorporated drought indices, thus leading to a data-informed analysis of Ningxia drought patterns. The study accomplished this by using MODIS satellite data products available for vegetation and moisture monitoring. The MOD09GA, MOD11A2, and MCD43A4 data streams were loaded into Google Earth Engine as factors to develop a time-series dataset of vegetation indices. Indices are Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Temperature (LST) measurements are taken into account. Data on temperature, precipitation, and evapotranspiration was compiled for the period from 2003 to 2023 and calculated standardized indices on pixel level for the whole Ningxia region to develop the Standardized Precipitation Index (SPI), Keetch-Byram Drought Index (KBDI), and Standardized Precipitation-Evapotranspiration Index ( The study results indicated that SPI fell significantly from the year 2003 to 2023, from 0.7 to -0.3. The SPEI plummeted from 0.5 to -0.2 during the observed time frame. KBDI also went up, through 581.33 in 2003 and 681.091 in 2023, showing deterioration of aridity and drying of the soil. The conclusion of this study focuses on the deterioration of drought conditions in the Ningxia region in the last 20 years.

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Section: 𝑁𝐷𝑉𝐼 = 𝑁𝐼𝑅 − 𝑅𝑒𝑑 𝑁𝐼𝑅 + 𝑅𝑒𝑑mentioning

confidence: 99%

Spatiotemporal Drought Assessment in Ningxia Autonomous Region: A Machine Learning and Remote Sensing Approach

Awais,

Zaheen,

Fatima

et al. 2024

ABBDM

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Unlocking Wheat Drought Tolerance: The Synergy of Omics Data and Computational Intelligence

Le Roux,

Kunert,

Cullis

et al. 2024

Food and Energy Security

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Currently, approximately 4.5 billion people in developing countries consider bread wheat (Triticum aestivum L.) as a staple food crop, as it is a key source of daily calories. Wheat is, therefore, ranked the second most important grain crop in the developing world. Climate change associated with severe drought conditions and rising global mean temperatures has resulted in sporadic soil water shortage causing severe yield loss in wheat. While drought responses in wheat crosscut all omics levels, our understanding of water‐deficit response mechanisms, particularly in the context of wheat, remains incomplete. This understanding can be significantly advanced with the aid of computational intelligence, more often referred to as artificial intelligence (AI) models, especially those leveraging machine learning and deep learning tools. However, there is an imminent and continuous need for omics and AI integration. Yet, a foundational step to this integration is the clear contextualization of drought—a task that has long posed challenges for the scientific community, including plant breeders. Nonetheless, literature indicates significant progress in all omics fields, with large amounts of potentially informative omics data being produced daily. Despite this, it remains questionable whether the reported big datasets have met food security expectations, as translating omics data into pre‐breeding initiatives remains a challenge, which is likely due to data accessibility or reproducibility issues, as interpreting omics data poses big challenges to plant breeders. This review, therefore, focuses on these omics perspectives and explores how AI might act as an interface to make this data more insightful. We examine this in the context of drought stress, with a focus on wheat.

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Cropland Exposure to Extreme Dryness and Wetness in China Under Shared Socioeconomic Pathways

Yang,

Wang,

Zhang

et al. 2024

Intl Journal of Climatology

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Global warming by human activities have exacerbated the occurrence of extreme climatic events, which have taken a huge toll on human production and livelihoods. Predicting future changes in extreme wetness and dryness, along with the extent of cropland exposure to these conditions under various scenarios, is essential for effective climate adaptation and achieving sustainable development goals. This study employs the Standardised Antecedent Precipitation Evapotranspiration Index (SAPEI) method to identify extreme dryness and extreme wetness in China for future projections (2021–2100), and to analyse the characteristics of changes in the pixel‐day, intensity, and affected area by extreme dryness and wetness, as well as the cropland exposures to them under different shared socioeconomic pathways (SSPs). We find that the intensity and spatial extent of extreme dryness and wetness significantly increase in future climate projections, especially under high‐emission scenario compared to low‐emission scenario. Under the scenarios with increased emissions, the cropland exposure increased in most parts of China. Therefore, it is particularly urgent to keep the low‐emission scenario in order to minimise the cropland damage caused by extreme drought and wetness in China in the future.

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Ensemble learning based on remote sensing data for monitoring agricultural drought in major winter wheat-producing areas of China

Cited by 4 publications

References 59 publications

Spatiotemporal Drought Assessment in Ningxia Autonomous Region: A Machine Learning and Remote Sensing Approach

Spatiotemporal Drought Assessment in Ningxia Autonomous Region: A Machine Learning and Remote Sensing Approach

Unlocking Wheat Drought Tolerance: The Synergy of Omics Data and Computational Intelligence

Cropland Exposure to Extreme Dryness and Wetness in China Under Shared Socioeconomic Pathways

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