A Conceptual Approaches to Assessment of Soil Quality Parameters Using Remote Sensing, GIS, Google Earth and Drone Imagery Techniques

Abstract

Soil Quality (SQ) is a cornerstone of sustainable agricultural ecosystem functioning and environmental sustainability. While traditional field sampling and laboratory methods are accurate they are constrained by high costs time consuming processes and limited geographical coverage. This conceptual review examines modern geospatial approaches such as Remote Sensing (RS), Geographic Information Systems (GIS), Google Earth Engine (GEE), and drone (UAV) imagery to assess key soil quality parameters, including physical (texture, moisture, erosion), chemical (pH, salinity, nutrients, soil organic carbon), and biological indicators. Assessing soil quality is crucial for sustainable agriculture, land management, and environmental conservation, as it directly impacts productivity ecosystem services and resilience against degradation. Traditional field methods for evaluating parameters like pH, organic matter content, nutrient levels (N, P, K), texture, salinity, moisture, and erosion risk are often labour intensive, time-consuming, and limited in geographical scope. This paper presents a conceptual approach for efficient, scalable, and non-invasive soil quality monitoring by integrating Remote Sensing (RS), Geographic Information Systems (GIS), Google Earth, and drone (UAV) imaging technologies. These geospatial technologies enable multi-level analysis: satellite remote sensing and Google Earth provide extensive regional data on vegetation indices (e.g., NDVI, SAVI), soil moisture, and landforms; GIS facilitates the geospatial modelling and integration of multi-source data for the Soil Quality Index (SQI), while high-resolution drone imaging enables detailed, site-specific mapping of properties such as organic carbon and surface variations. To estimate and predict soil properties, these conceptual frameworks often incorporate spectral indices, machine learning algorithms, Digital Soil Mapping (DSM) and terrain attributes derived from Digital Elevation Models (DEMs). This approach overcomes the limitations of traditional methods by providing cost-effective timely and repeatable assessments, particularly over heterogeneous or large landscapes. The text also discusses challenges such as atmospheric interference, the need for calibration, and the integration of multi-sensor data, as well as future directions involving the incorporation of AI-enhanced models to improve accuracy and decision support in precision agriculture.