• V Neelima Devi
• Kakara Sai Meghana

Soil moisture plays a crucial role in various agricultural, hydrological, and environmental applications. Traditional methods for soil moisture retrieval often rely on in-situ measurements, which can be time-consuming and expensive. In recent years, machine learning techniques have emerged as a promising approach to estimate soil moisture using remote sensing data. However, most existing studies focus on using satellite-based observations, neglecting the valuable information provided by groundwater levels. This study proposes a novel approach for soil moisture retrieval using machine learning, specifically leveraging ground water data. The goal is to exploit the relationship between groundwater levels and soil moisture content to develop a reliable and accurate estimation model. The proposed method utilizes a combination of historical groundwater measurements and corresponding soil moisture data collected from in-situ sensors.

Soil moisture retrieval, groundwater dataset, machine learning, feature engineering, spatiotemporal dynamics, remote sensing, water resource management.

"SOIL MOISTURE RETRIEVAL USING GROUNDWATER USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.j206-j212, June-2023, Available :http://www.jetir.org/papers/JETIR2306928.pdf

"SOIL MOISTURE RETRIEVAL USING GROUNDWATER USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppj206-j212, June-2023, Available at : http://www.jetir.org/papers/JETIR2306928.pdf