An inclusive approach to crop soil moisture estimation: Leveraging satellite thermal infrared bands and vegetation indices on Google Earth engine

• Published in: Agricultural water management 2024-12, Vol.306, p.109172, Article 109172
• Main Authors: Imtiaz, Fatima, Farooque, Aitazaz A., Randhawa, Gurjit S., Wang, Xiuquan, Esau, Travis J., Acharya, Bishnu, Hashemi Garmdareh, Seyyed Ebrahim
• Format: Article
• Language: English
• Subjects: Google Earth Engine; Irrigation control; Land surface temperature; Normalized difference vegetation index; Remote sensing; Soil moisture
• ISSN: 0378-3774; 1873-2283
• DOI: 10.1016/j.agwat.2024.109172

Soil moisture estimation is critical for environmental and agricultural sustainability, with its spatial and temporal variation playing a key role in drought monitoring and understanding climate change. The region of Prince Edward Island (PEI), Atlantic Canada's largest potato producer, is facing irregular precipitation patterns that stress crop water supplies. This study aims to estimate field-scale soil moisture utilizing satellite-based reflective and thermal infrared bands from Landsat-8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) and Moderate-resolution Imaging Spectroradiometer (MODIS) over the cloud-based Google Earth Engine (GEE) platform. The GEE data catalog's pre-processed data endured to calculate various indicators for the agricultural seasons of 2021 and 2022 across three designated plots: A, B, and C. The indicators are land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference moisture index (NDMI). NDVI and LST were used to calculate the soil moisture index (SMI), representing the real-time soil moisture at the field scale. The soil moisture data was validated using in situ measurements. The analysis showed good Root Mean Square Error values of 1.43 % (Plot A), 2.12 % (Plot B), and 2.60 % (Plot C). A weak negative association between LST and NDVI was noticed in the study, with R² values of 0.25, 0.38 and 0.26 for Plots A, B and C, respectively. As the LST rises, vegetation declines due to the elevated temperatures in the study area. Second, a significant (p < 0.05) negative correlation (R2 =1) existed between SMI and LST in both the 2021 and 2022 seasons, showing a decrease in the top layer soil moisture with LST. The NDWI exhibited a significant inverse correlation with soil moisture, while NDMI and NDVI are effective predictors. Hence, based on the current study, optical and thermal remote sensing offers valuable insights into soil moisture dynamics and can be a good tool for irrigation control and water conservation. [Display omitted] •Used thermal/optical indices to estimate soil moisture in potato crops via remote sensing.•Combined SMI, NDVI, NDMI, NDWI, and LST for soil moisture quantification.•Used Landsat 8 OLI/TIRS and MODIS imagery for soil moisture data collection.•Validated soil moisture with satellite data and in situ measurements over two seasons.•Achieved high accuracy with low RMSE, confirming the effectiveness of indice