{"title":"Satellite-based estimation of net radiation to support evapotranspiration modeling in agriculture","authors":"Chutimon Phoemwong, Rungrat Wattan, Somjet Pattarapanitchai, Serm Janjai","doi":"10.1016/j.rsase.2025.101746","DOIUrl":null,"url":null,"abstract":"<div><div>Net radiation (<em>R</em><sub><em>n</em></sub>) is a fundamental variable in the surface energy balance and serves as a key input for estimating evapotranspiration (<em>ET</em><sub><em>0</em></sub>), which is critical for agricultural water management and irrigation planning. Accurate estimation of <em>R</em><sub><em>n</em></sub> is particularly important in large-scale agricultural regions where ground-based measurements are limited or unavailable. This study aims to investigate the spatiotemporal variation of surface <em>R</em><sub><em>n</em></sub> and to develop simplified multiple linear regression models for its estimation using satellite-derived atmospheric variables. The selected input variables chosen for their direct or indirect influence on <em>ET</em><sub><em>0</em></sub> include downward shortwave radiation (<em>S</em><sub><em>d</em></sub>) and the brightness temperature difference between bands 31 and 32 from MODIS, which indicates atmospheric water vapor content (<em>WP</em>). These are supplemented by relative humidity (<em>RH</em>), air temperature (<em>T</em><sub><em>air</em></sub>), and cloud cover (<em>C</em>), obtained from NCEP/NCAR reanalysis data. Ground-based observations of <em>R</em><sub><em>n</em></sub> were used as reference data to develop and validate the model. The dataset was divided into two parts: 2017–2021 for model development and 2022–2024 for validation. The resulting linear model showed high accuracy, with an <em>R</em><sup><em>2</em></sup> of 0.96, <em>RMSE</em> of 21.6 %, and <em>MBE</em> of −6.4 %. The validated model was applied to produce spatial <em>R</em><sub><em>n</em></sub> maps, which demonstrated strong agreement with in-situ data and effectively represented spatial and temporal variation. This modeling approach enhances the ability to estimate <em>R</em><sub><em>n</em></sub> over large agricultural areas, thereby supporting more reliable <em>ET</em><sub><em>0</em></sub> estimation and water resource management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101746"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S235293852500299X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Net radiation (Rn) is a fundamental variable in the surface energy balance and serves as a key input for estimating evapotranspiration (ET0), which is critical for agricultural water management and irrigation planning. Accurate estimation of Rn is particularly important in large-scale agricultural regions where ground-based measurements are limited or unavailable. This study aims to investigate the spatiotemporal variation of surface Rn and to develop simplified multiple linear regression models for its estimation using satellite-derived atmospheric variables. The selected input variables chosen for their direct or indirect influence on ET0 include downward shortwave radiation (Sd) and the brightness temperature difference between bands 31 and 32 from MODIS, which indicates atmospheric water vapor content (WP). These are supplemented by relative humidity (RH), air temperature (Tair), and cloud cover (C), obtained from NCEP/NCAR reanalysis data. Ground-based observations of Rn were used as reference data to develop and validate the model. The dataset was divided into two parts: 2017–2021 for model development and 2022–2024 for validation. The resulting linear model showed high accuracy, with an R2 of 0.96, RMSE of 21.6 %, and MBE of −6.4 %. The validated model was applied to produce spatial Rn maps, which demonstrated strong agreement with in-situ data and effectively represented spatial and temporal variation. This modeling approach enhances the ability to estimate Rn over large agricultural areas, thereby supporting more reliable ET0 estimation and water resource management.
期刊介绍:
The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems