Satellite-based estimation of net radiation to support evapotranspiration modeling in agriculture

IF 4.5 Q2 ENVIRONMENTAL SCIENCES
Chutimon Phoemwong, Rungrat Wattan, Somjet Pattarapanitchai, Serm Janjai
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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.
基于卫星的净辐射估算以支持农业蒸散模拟
净辐射(Rn)是地表能量平衡的一个基本变量,是估算蒸散发(ET0)的关键输入,对农业用水管理和灌溉规划至关重要。在地面测量有限或无法获得的大规模农业区,准确估计氮含量尤为重要。本研究旨在研究地表氮的时空变化,并利用卫星大气变量建立简化的多元线性回归模型。直接或间接影响ET0的输入变量包括MODIS的向下短波辐射(Sd)和31和32波段之间的亮度温差,其表示大气水蒸气含量(WP)。这些数据由NCEP/NCAR再分析数据获得的相对湿度(RH)、气温(Tair)和云量(C)补充。利用地面观测数据作为参考数据来开发和验证该模型。数据集分为两部分:2017-2021年用于模型开发,2022-2024年用于验证。所建立的线性模型具有较高的准确度,R2为0.96,RMSE为21.6%,MBE为−6.4%。将该模型应用到空间Rn地图中,结果与现场数据吻合较好,有效地反映了时空变化。这种建模方法增强了在大型农业区估算碳排放系数的能力,从而支持更可靠的ET0估算和水资源管理。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
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
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