Determination of evapotranspiration for citrus using SAFER algorithm in the Oriental Amazon

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-03-21 DOI:10.1016/j.rsase.2025.101526

Francisco de Assis do Nascimento Leão , Erivelto Mercante , Wendel Kaian Mendonça Oliveira , Marcio Antonio Vilas Boas , Marcus Metri Correa , Claudio Leones Bazzi , Alberto Cruz da Silva Jr.

{"title":"Determination of evapotranspiration for citrus using SAFER algorithm in the Oriental Amazon","authors":"Francisco de Assis do Nascimento Leão , Erivelto Mercante , Wendel Kaian Mendonça Oliveira , Marcio Antonio Vilas Boas , Marcus Metri Correa , Claudio Leones Bazzi , Alberto Cruz da Silva Jr.","doi":"10.1016/j.rsase.2025.101526","DOIUrl":null,"url":null,"abstract":"<div><div>A pivotal facet of irrigation management revolves around estimating evapotranspiration (ET). In this regard, the SAFER algorithm, applied to satellite imagery, emerges as a potential tool. This study aimed to quantify ET for orange and lime crops in the Amazon Region using the SAFER algorithm. Data comprised Landsat 7 and 8 satellite images coupled with meteorological station data from July to December 2021. The SAFER algorithm determines ET based on several metrics derived from satellite imagery, such as reflectance, surface albedo, Normalized Difference Vegetation Index (NDVI), spectral radiance, and surface temperature. It also uses reference evapotranspiration (ET<sub>0</sub>) from the meteorological station, which is multiplied by the quotient of ET and ET<sub>0</sub> to obtain ET<sub>SAFER</sub>. Then, the algorithm was validated based on the Penman-Monteith method, calculating mean absolute and relative errors. Average ET<sub>SAFER</sub> values for lime and orange were 3.25 (±0.05) and 3.36 (±0.01), respectively. A maximum albedo of 0.40 was observed among crops in December, and a higher density of lime crops due to larger canopy volumes increased NDVI values. Landsat 7 and 8 satellite images can be used to calculate ET using the SAFER algorithm, as they offered valuable information that helped the algorithm estimate it accurately for the studied period. The ET<sub>SAFER</sub> values obtained by the algorithm were consistent with the observed data (ET<sub>C</sub>) and had an accuracy of 75 %. However, estimation accuracy was higher for lime trees (74 %) than for orange crops (60 %).</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101526"},"PeriodicalIF":3.8000,"publicationDate":"2025-03-21","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/S2352938525000795","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

A pivotal facet of irrigation management revolves around estimating evapotranspiration (ET). In this regard, the SAFER algorithm, applied to satellite imagery, emerges as a potential tool. This study aimed to quantify ET for orange and lime crops in the Amazon Region using the SAFER algorithm. Data comprised Landsat 7 and 8 satellite images coupled with meteorological station data from July to December 2021. The SAFER algorithm determines ET based on several metrics derived from satellite imagery, such as reflectance, surface albedo, Normalized Difference Vegetation Index (NDVI), spectral radiance, and surface temperature. It also uses reference evapotranspiration (ET₀) from the meteorological station, which is multiplied by the quotient of ET and ET₀ to obtain ET_SAFER. Then, the algorithm was validated based on the Penman-Monteith method, calculating mean absolute and relative errors. Average ET_SAFER values for lime and orange were 3.25 (±0.05) and 3.36 (±0.01), respectively. A maximum albedo of 0.40 was observed among crops in December, and a higher density of lime crops due to larger canopy volumes increased NDVI values. Landsat 7 and 8 satellite images can be used to calculate ET using the SAFER algorithm, as they offered valuable information that helped the algorithm estimate it accurately for the studied period. The ET_SAFER values obtained by the algorithm were consistent with the observed data (ET_C) and had an accuracy of 75 %. However, estimation accuracy was higher for lime trees (74 %) than for orange crops (60 %).

查看原文本刊更多论文

利用SAFER算法测定亚马孙河流域柑橘的蒸散量

灌溉管理的一个关键方面是估算蒸散发（ET）。在这方面，应用于卫星图像的SAFER算法成为一种潜在的工具。本研究旨在使用SAFER算法量化亚马逊地区橙子和酸橙作物的ET。数据包括2021年7月至12月的陆地卫星7号和8号卫星图像以及气象站数据。SAFER算法基于来自卫星图像的几个指标来确定ET，如反射率、地表反照率、归一化植被指数（NDVI）、光谱辐射和地表温度。利用气象站的参考蒸散量（ET0）乘以蒸散量与ET0的商，得到ETSAFER。然后，基于Penman-Monteith方法对算法进行验证，计算平均绝对误差和相对误差。酸橙和橙的平均ETSAFER值分别为3.25（±0.05）和3.36（±0.01）。12月作物反照率最高为0.40，石灰作物密度越大，冠层体积越大，NDVI值增加。Landsat 7号和8号卫星图像可用于使用SAFER算法计算ET，因为它们提供了有价值的信息，有助于算法准确估计所研究时期的ET。该算法获得的ETSAFER值与观测数据（ETC）一致，精度为75%。然而，橙树的估计精度（74%）高于橙树（60%）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Remote Sensing Applications-Society and Environment Multiple-

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