Integrating Remote Sensing, Proximal Sensing, and Probabilistic Modeling to Support Agricultural Project Planning and Decision-Making for Waterlogged Fields
نویسندگان
چکیده
Waterlogging in agriculture poses severe threats to soil properties, crop yields, and farm profitability. Remote sensing data coupled with drainage systems offer solutions monitor manage waterlogging agricultural systems. However, implementing projects such as is associated high uncertainty risk, substantial negative impacts on profitability if not well planned. Cost–benefit analyses can help allocate resources more effectively; however, scarcity, uncertainty, risks the sector make it difficult use traditional approaches. Here, we combined a wide range of field remote data, unsupervised machine learning, Bayesian probabilistic models to: (1) identify potential sites susceptible at scale, (2) test whether installation would yield positive benefit for farmer. Using K-means clustering algorithm water vegetation indices derived from Sentinel-2 multispectral imagery, were able detect investigated (elbow point = 2, silhouette coefficient 0.46). combination statistical model A/B test, show that system increase by 1.7 times per year compared existing management. The posterior effect size yield, cropping area, time (year) was 0.5, 1.5, 1.9, respectively. Altogether, our results emphasize importance data-driven decision-making project planning resource management wake smart food security adaptation climate change.
منابع مشابه
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ژورنال
عنوان ژورنال: Water
سال: 2023
ISSN: ['2073-4441']
DOI: https://doi.org/10.3390/w15071340