Raja Inoubli;Daniel Enrique Constantino-Recillas;Alejandro Monsiváis-Huertero;Lilia Bennaceur Farah;Imed Riadh Farah
{"title":"评估水云模型中的地表散射模型,利用哨兵-1 号和哨兵-2 号图像进行土壤水分检索","authors":"Raja Inoubli;Daniel Enrique Constantino-Recillas;Alejandro Monsiváis-Huertero;Lilia Bennaceur Farah;Imed Riadh Farah","doi":"10.1109/JSTARS.2024.3462591","DOIUrl":null,"url":null,"abstract":"The agricultural productivity and the optimized use of water resources rely on the soil moisture (SM) retrieval to achieve some of the sustainable development goals, such as ensuring food security and monitoring climate change. One of the main aspects to provide accurate SM retrieval results is the selection of the most effective models. This study is carried out to exhibit the impact of three different soil formulations [i.e., Linear, Oh, and improved integral equation model (I2EM)] within the water cloud model (WCM). The experiments are conducted based on the combined use of Sentinel-1 and Sentinel-2 images. The in-situ measurements used in this work are collected from five different fields in Huamantla, Central Mexico. The experiments focus on the complete growing season of corn taking into consideration the soil and the vegetation contribution. The best \n<italic>Bias</i>\n and \n<italic>unbiased root mean squared difference (ubRMSD)</i>\n values obtained by the Oh-WCM are equal to −0.437 and 0.295 dB, respectively at VV in PX1. The I2EM-WCM achieved \n<italic>Bias</i>\n and \n<italic>ubRMSD</i>\n values equal to −0.760 and 0.379 dB at VV, respectively. The linear-WCM also obtained low \n<italic>Bias</i>\n and \n<italic>ubRMSD</i>\n values equal to −0.297 and 0.322 dB, respectively. Therefore, the combination of the Oh model within the WCM is considered as the appropriate combination for the SM retrieval due to its high achieved accuracy. The sensitivity analysis of changes in \n<inline-formula><tex-math>$\\sigma ^{0}_{pq,\\text{tot}}$</tex-math></inline-formula>\n due to changes in SM found that it is possible to capture changes higher than 0.06 m\n<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>\n/m\n<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>\n in SM over the complete growing season of corn using C-band backscatter observations.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681514","citationCount":"0","resultStr":"{\"title\":\"Assessment of Surface Scattering Models Within the Water Cloud Model Toward Soil Moisture Retrievals Using Sentinel-1 and Sentinel-2 Images\",\"authors\":\"Raja Inoubli;Daniel Enrique Constantino-Recillas;Alejandro Monsiváis-Huertero;Lilia Bennaceur Farah;Imed Riadh Farah\",\"doi\":\"10.1109/JSTARS.2024.3462591\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The agricultural productivity and the optimized use of water resources rely on the soil moisture (SM) retrieval to achieve some of the sustainable development goals, such as ensuring food security and monitoring climate change. One of the main aspects to provide accurate SM retrieval results is the selection of the most effective models. This study is carried out to exhibit the impact of three different soil formulations [i.e., Linear, Oh, and improved integral equation model (I2EM)] within the water cloud model (WCM). The experiments are conducted based on the combined use of Sentinel-1 and Sentinel-2 images. The in-situ measurements used in this work are collected from five different fields in Huamantla, Central Mexico. The experiments focus on the complete growing season of corn taking into consideration the soil and the vegetation contribution. The best \\n<italic>Bias</i>\\n and \\n<italic>unbiased root mean squared difference (ubRMSD)</i>\\n values obtained by the Oh-WCM are equal to −0.437 and 0.295 dB, respectively at VV in PX1. The I2EM-WCM achieved \\n<italic>Bias</i>\\n and \\n<italic>ubRMSD</i>\\n values equal to −0.760 and 0.379 dB at VV, respectively. The linear-WCM also obtained low \\n<italic>Bias</i>\\n and \\n<italic>ubRMSD</i>\\n values equal to −0.297 and 0.322 dB, respectively. Therefore, the combination of the Oh model within the WCM is considered as the appropriate combination for the SM retrieval due to its high achieved accuracy. The sensitivity analysis of changes in \\n<inline-formula><tex-math>$\\\\sigma ^{0}_{pq,\\\\text{tot}}$</tex-math></inline-formula>\\n due to changes in SM found that it is possible to capture changes higher than 0.06 m\\n<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>\\n/m\\n<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>\\n in SM over the complete growing season of corn using C-band backscatter observations.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681514\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10681514/\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10681514/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Assessment of Surface Scattering Models Within the Water Cloud Model Toward Soil Moisture Retrievals Using Sentinel-1 and Sentinel-2 Images
The agricultural productivity and the optimized use of water resources rely on the soil moisture (SM) retrieval to achieve some of the sustainable development goals, such as ensuring food security and monitoring climate change. One of the main aspects to provide accurate SM retrieval results is the selection of the most effective models. This study is carried out to exhibit the impact of three different soil formulations [i.e., Linear, Oh, and improved integral equation model (I2EM)] within the water cloud model (WCM). The experiments are conducted based on the combined use of Sentinel-1 and Sentinel-2 images. The in-situ measurements used in this work are collected from five different fields in Huamantla, Central Mexico. The experiments focus on the complete growing season of corn taking into consideration the soil and the vegetation contribution. The best
Bias
and
unbiased root mean squared difference (ubRMSD)
values obtained by the Oh-WCM are equal to −0.437 and 0.295 dB, respectively at VV in PX1. The I2EM-WCM achieved
Bias
and
ubRMSD
values equal to −0.760 and 0.379 dB at VV, respectively. The linear-WCM also obtained low
Bias
and
ubRMSD
values equal to −0.297 and 0.322 dB, respectively. Therefore, the combination of the Oh model within the WCM is considered as the appropriate combination for the SM retrieval due to its high achieved accuracy. The sensitivity analysis of changes in
$\sigma ^{0}_{pq,\text{tot}}$
due to changes in SM found that it is possible to capture changes higher than 0.06 m
$^{3}$
/m
$^{3}$
in SM over the complete growing season of corn using C-band backscatter observations.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.