Document Type
Article
Author ORCID Identifier
Lia Ramos-Fernández https://orcid.org/0000-0003-3946-7188
Maria Gonzales-Quiquia https://orcid.org/0009-0002-5048-9484
José Huanuqueño-Murillo https://orcid.org/0009-0003-8511-4524
Elizabeth Heros-Aguilar https://orcid.org/0000-0002-0179-3124
Alfonso Torres-Rua https://orcid.org/0000-0002-2238-9550
Journal/Book Title/Conference
Remote Sensing
Volume
16
Issue
5
Publisher
MDPI AG
Publication Date
2-24-2024
Journal Article Version
Version of Record
First Page
1
Last Page
21
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
In the face of the climate change crisis, the increase in air temperature negatively impacts rice crop productivity due to stress from water scarcity. The objective of this study was to determine the rice crop water stress index (CWSI) and stomatal conductance (Gs) under different irrigation regimes, specifically continuous flood irrigation treatments (CF) and irrigations with alternating wetting and drying (AWD) at water levels of 5 cm, 10 cm, and 20 cm below the soil surface (AWD5, AWD10, and AWD20) in an experimental area of INIA-Vista Florida and in six commercial areas of the Lambayeque region using thermal images captured with thermal sensors. The results indicated that AWD irrigation generated more water stress, with CWSI values between 0.4 and 1.0. Despite this, the yields were similar in CF and AWD20. In the commercial areas, CWSI values between 0.38 and 0.51 were obtained, with Santa Julia having the highest values. Furthermore, a strong Pearson correlation (R) of 0.91 was established between the CWSI and Gs, representing a reference scale based on Gs values for evaluating water stress levels.
Recommended Citation
Ramos-Fernández, L.; Gonzales-Quiquia, M.; Huanuqueño-Murillo, J.; Tito-Quispe, D.; Heros-Aguilar, E.; Flores del Pino, L.; Torres-Rua, A. Water Stress Index and Stomatal Conductance under Different Irrigation Regimes with Thermal Sensors in Rice Fields on the Northern Coast of Peru. Remote Sens. 2024, 16, 796. https://doi.org/10.3390/rs16050796