Integrating Remote Sensing Techniques and Meteorological Data to Assess the Ideal Irrigation System Performance Scenarios for Improving Crop Productivity

Abstract

To increase agricultural productivity and ensure food security, it is important to understand the reasons for variations in irrigation over time. However, researchers often avoid investigating water productivity due to data availability challenges. This study aimed to assess the performance of the irrigation system for winter wheat crops using a high-resolution satellite, Sentinel 2 A/B, combined with meteorological data and Google Earth Engine (GEE)-based remote sensing techniques. The study area is located north of Erbil city in the Kurdistan region of Iraq (KRI) and consists of 143 farmer-owned center pivots. This study also aimed to analyze the spatiotemporal variation of key variables (Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Precipitation (mm), Evapotranspiration (ETo), Crop evapotranspiration (ETc), and Irrigation (Hours), during the wheat-growing winter season in the drought year 2021 to understand the reasons for the variance in field performance. The finding revealed that water usage fluctuated significantly across the seasons, while yield gradually increased from the 2021 winter season. In addition, the study revealed a notable correlation between soil moisture based on the (NDMI) and vegetation cover based on the (NDVI), and the increase in yield productivity and reduction in the yield gap, specifically during the middle of the growing season (March and April). Integrating remote sensing with meteorological data in supplementary irrigation systems can improve agriculture and water resource management by boosting yields, improving crop quality, decreasing water consumption, and minimizing environmental impacts. This innovative technique can potentially enhance food security and promote environmental sustainability.

Normalised difference moisture index in water stress assessment of maize crops

“…Sharma et al (2018) demonstrated the possibility of accurate identification of irrigated croplands through the combination of NDVI, NDMI, and EVI (enhanced vegetation index) within the framework of SVM (support vector machine) algorithm of machine learning. Application of NDMI is not limited to irrigation mapping, and it is also used in irrigation scheduling during crop cultivation, as Gaznayee et al (2023) reported its efficiency in irrigation control due to high sensitivity to changes in soil moisture. Verma & Verma (2023) also claim that remote sensing with NDMI could be used in precision agricultural systems to control and maintain soil moisture at the optimal level for cultivated crops.…”

Image Classification for Precision Agriculture: A Coffee Study Case

“…This technology enables the monitoring of agricultural fields at various spatial, temporal, and spectral resolutions, allowing for the identification of crop types, growth stages, and stress conditions [5]. As a result, remote sensing data can be harnessed to inform crop management strategies, such as irrigation scheduling, nutrient application, and pest control, which are essential for maintaining high productivity and environmental sustainability [6].…”