Integrating Remotely Sensed Soil Moisture in Assessing the Effects of Climate Change on Food Production: A Review of Applications in Crop Production in Africa

Chari, Martin Munashe; Hamandawana, Hamisai; Zhou, Leocadia

doi:10.1007/978-3-030-87934-1_12

Cited by 3 publications

(1 citation statement)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Soil moisture (SM), soil organic carbon (SOC), and nitrogen content (NC) are the fundamental aspects of nature that provide territory to a broad scope of life forms, and are important for healthy food production [ 1 , 2 , 3 , 4 ]. SM contributes to plant development and deterioration, climate change, and carbon formation, and significantly controls the filtration, overflow, drought monitoring, and evaporation rates [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture, Organic Carbon, and Nitrogen Content Prediction with Hyperspectral Data Using Regression Models

Datta

Paul

Murshed

et al. 2022

Sensors

View full text Add to dashboard Cite

Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.

show abstract