Remote Sensing Applications-Society and Environment最新文献

{"title":"Soil and vegetation types are predisposition factors controlling greenness changes: A shift of paradigm in greening and browning modelling?","authors":"Luís Flávio Pereira ,&nbsp;Elpídio Inácio Fernandes-Filho ,&nbsp;Lucas Carvalho Gomes ,&nbsp;Daniel Meira Arruda ,&nbsp;Guilherme Castro Oliveira ,&nbsp;Carlos Ernesto Gonçalves Reynald Schaefer ,&nbsp;José João Lelis Leal de Souza ,&nbsp;Márcio Rocha Francelino","doi":"10.1016/j.rsase.2024.101366","DOIUrl":"10.1016/j.rsase.2024.101366","url":null,"abstract":"<div><div>Increases (greening) and losses (browning) of vegetation greenness related to climatic and anthropic changes are processes well documented in the literature. However, the control exerted by predisposition factors on the response of vegetation to these changes has been little studied, and appears to be especially important in anthropized regions. The present study aimed to map greening and browning processes, as well as to characterize and analyze their distribution in heavily anthropized regions regarding two main predisposition factors: soil and vegetation types. The Brazilian Semiarid region was used as a model area, using two novel approaches: a readily reproducible cloud computing approach to map consistent greening and browning processes, and a disaggregation approach in homogeneous units of vegetation, soil and land use types. The results showed that stable greenness dominates (66.8%), but browning is more frequent (29.1%) and intense than greening (4.1%), and may be related to desertification processes in native and anthropized areas. The distribution of greening and browning processes is zonal and heterogeneous. Environmental predisposition factors, mainly the water supply capacity, regionally control the distribution of greening and browning zones. Human-environment interplays locally regulate the intensity and distribution of the processes. We defend the need of a paradigm shift in greening and browning modelling. Further studies should consider the simultaneous and balanced use of predictors related to both predisposition and changes. The need for advances in the interpretability of these models is also evident, given that current approaches fail to elucidate the regulating mechanisms of greening and browning processes.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101366"},"PeriodicalIF":3.8,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite-based measurements of temporal and spatial variations in C fluxes of irrigated and rainfed cotton grown in India","authors":"Desouza Blaise ,&nbsp;Nirmala D. Desouza ,&nbsp;Amarpreet Singh","doi":"10.1016/j.rsase.2024.101365","DOIUrl":"10.1016/j.rsase.2024.101365","url":null,"abstract":"<div><div>The small number of carbon dioxide (CO₂) observation networks and the prohibitively high equipment cost restrict the estimation of net ecosystem CO₂ exchange (NEE). Satellite-based remote sensing techniques have made it possible to obtain NEE and component carbon (C) fluxes. One-third of the world's cotton area is in India, but the information on NEE is limited. We used the Level 4 Carbon (L4_C) product from the Soil Moisture Active Passive (SMAP) mission to estimate C fluxes based on satellite-derived soil moisture, weather, and vegetation data. For our study (2018-19 to 2020-21), we chose two ecosystems (rainfed central India vs. irrigated northern India). Seasonal variations were observed in C fluxes. Gross primary productivity was the highest during the boll formation phase. This phase was the strongest sink and coincided with the highest CO₂ uptake, followed by the flowering and square formation phases. The cotton crop was a C source during the initial vegetative phase and after the boll opening. Overall, the cotton crop was a sink for atmospheric CO₂ with an average NEE value of −189.6 g C m⁻² under irrigated and −245.6 g C m⁻² in rainfed cotton. Higher ecosystem respiration in irrigated cotton resulted in lower C sink strength than rainfed cotton. Our studies indicate that the SMAP L4_C product model estimates can be used to obtain information on C fluxes in real-world situations. Moreover, such satellite-based remote sensing techniques will enable large-scale environmental monitoring with different cropping systems and support policymaking.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101365"},"PeriodicalIF":3.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectrometric and remote sensing investigations of some granitic rocks in the Egyptian north Eastern Desert: Insights on environmental radiogenic heat production","authors":"Osama K. Dessouky ,&nbsp;Yasser S. Badr ,&nbsp;Mahmoud M. Hassan","doi":"10.1016/j.rsase.2024.101360","DOIUrl":"10.1016/j.rsase.2024.101360","url":null,"abstract":"<div><div>Granitic rocks dominate the Neoproterozoic outcrops in the northern Egyptian Eastern Desert, prominently featuring two main categories: Arc Granitoids (AG) and late-to post-collision granites (LPCG). The AG range from granodiorite and tonalite to quartz-diorite. In contrast, LPCG comprise syenogranite, monzogranite, and alkali-feldspar granite. This study leverages Landsat-8 remote sensing data to effectively discriminate between these rock types using several advanced image processing techniques. False color composite and decorrelation stretch methods highlighted geological and structural features, revealing distinct spectral signatures for each rock type. Principal Component Analysis and band rationing further refined distinguishing various varieties within the LPCG and detailed mapping. Supervised classification using the Support Vector Machine method yielded precise delineation of rock units. The investigated granitic rocks exhibited estimated radiogenic heat production values ranging from 6.02 to 1.41 μW/m³, surpassing the average values observed in the Earth's crust. The reason behind these noteworthy surpassing values of radiogenic heat production can be directly attributed to the relatively high Gamma-ray measurements in the LPCG outcrops. Gamma-ray spectrometric analysis indicated varying distributions of radioelements, particularly between AG and LPCG. The equivalent uranium (eU) concentrations range from 2.8 to 7 ppm in AG, while LPCG exhibited broader variability from 5.1 to 34 ppm. The equivalent thorium (eTh) values range from 16.1 to 34.1 ppm, with an overall average of 23 ppm. Conversely, within the ferruginated-silicified domains, the LPCG display slightly elevated levels of eU, reaching 31.4, 35.5, and 27.8 ppm for the monzogranites, syenogranites, and alkali-feldspar granites, respectively. These elevated levels suggest the potential for iron oxy-hydroxide minerals to adsorb uranium within alteration zones. Additionally, radioactive minerals such as zircon, columbite, uranothorite, allanite, euxenite, and samarskite contribute to the observed spot anomalies.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101360"},"PeriodicalIF":3.8,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new algorithm to determine the spatial coverage of carob (Neltuma piurensis) by ecological floor: Chira-Piura River Basin case","authors":"Cristhian Aldana ,&nbsp;Jaime Lloret ,&nbsp;Wilmer Moncada ,&nbsp;Joel Rojas Acuña ,&nbsp;Yesenia Saavedra ,&nbsp;Vicente Amirpasha Tirado-Kulieva","doi":"10.1016/j.rsase.2024.101363","DOIUrl":"10.1016/j.rsase.2024.101363","url":null,"abstract":"<div><div>The carob tree (<em>Neltuma piurensis</em>) is characteristic of the forests of northern Peru, withstand extreme climatic events such as “El Niño” and droughts, in addition to the influence of climate change, affecting its distribution of coverage at different altitudes. The objective of this article is to propose an algorithm to determine the Spatial Coverage of Carob by Ecological Floor (SCCEF) in the Chira-Piura River Basin, Peru. The method used consisted of measuring the spectral signature of the carob tree with the FieldSpec4 spectroradiometer at three sampling points corresponding to the localities of Cardal, Lancones and Macacará, located on different ecological floors. The comparison of the spectral signatures for Cardal and Lancones gives an R² = 0.9459, for Cardal and Macacará an R² = 0.9866 and for Lancones with Macacará an R² = 0.9469, which allows an accurate identification of the carob tree in the satellite images. The Mann-Whitney-Wilcoxon <em>U</em> test validates the spectral signatures extracted from the satellite images with the spectral signatures measured with the spectroradiometer at Lancones (p-value = 0.9705 &gt;α = 0.05), Cardal (p-value = 0.9819 &gt; 0.05) and Macacará (p-value = 0.7959 &gt; 0.05). The results show that the SCCEF in the Tropical (T) ecological floor represents 1.55 % of the T area, in the Tropical Pre-Montane (TPM) ecological floor it is 1.47 % of the TPM area, in the Low Tropical Montane (LTM) ecological floor it is 0.78 % of the LTM area, in the Montane (M) ecological floor it is 0.69 % of the M area and in the Paramo (P) ecological floor it is 0.35 % of the P area. Therefore, the SCCEF decreases in each ecological floor as its altitude increases.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101363"},"PeriodicalIF":3.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven approach for land surface temperature retrieval with machine learning and sentinel-2 data","authors":"Aymen Zegaar ,&nbsp;Abdelmoutia Telli ,&nbsp;Samira Ounoki ,&nbsp;Himan Shahabi ,&nbsp;Francisco Rueda","doi":"10.1016/j.rsase.2024.101357","DOIUrl":"10.1016/j.rsase.2024.101357","url":null,"abstract":"<div><p>This research endeavors to advance land surface temperature (LST) prediction accuracy through the development of a sophisticated machine learning model. Leveraging the potential of Sentinel 2 data and atmospheric parameters, we augment Landsat-based LST with MODIS-based LST, enriching the temporal dimensions of our dataset. A distinctive feature of our study is the pioneering use of Sentinel 2 data as inputs for LST prediction, a facet scarcely explored in the existing literature. Our investigation delves into the correlation dynamics between LST and atmospheric parameters. Notably, the study employs a diverse set of machine learning models, including Extra Trees, Random Forests, LightGBM, XGBoost, and Support Vector Regressor. These models collectively exhibit superior performance, with Extra Trees emerging as a standout performer, with a minimal mean absolute error (MAE) of 0.423, a root mean square error (RMSE) of 1.340 °C, and an impressive coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) of 0.984. The exploration of Sentinel 2 data as an input source for LST prediction not only refines predictive accuracy but also opens novel research avenues in the realm of LST dynamics. This study contributes to the existing body of knowledge by introducing innovative methodologies and providing a comprehensive understanding of the intricate correlations influencing LST.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101357"},"PeriodicalIF":3.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cloud computing and spatial hydrology for monitoring the Buyo and Kossou reservoirs in Côte d'Ivoire","authors":"Valère-Carin Jofack Sokeng ,&nbsp;Sekouba Oulare ,&nbsp;Koffi Fernand Kouamé ,&nbsp;Benoit Mertens ,&nbsp;Tiémoman Kone ,&nbsp;Thibault Catry ,&nbsp;Benjamin Pillot ,&nbsp;Pétin Edouard Ouattara ,&nbsp;Diakaria Kone ,&nbsp;Massiré Sow","doi":"10.1016/j.rsase.2024.101353","DOIUrl":"10.1016/j.rsase.2024.101353","url":null,"abstract":"<div><div>The Buyo and Kossou reservoirs are crucial for water supply, agricultural irrigation, and hydroelectric power generation in Côte d'Ivoire. However, climate change threatens the stability and availability of these water resources by increasing rainfall variability, extending drought periods, and intensifying extreme weather events. These challenges underscore the need for precise and continuous monitoring of water levels and surface areas to ensure sustainable management. Due to the scarcity of gauging stations, the objective of this study is to leverage cloud computing technologies along with altimetric and satellite data, for effective reservoir monitoring. Tools like the EO-Africa program's Innovation Lab and Google Earth Engine (GEE), along with advanced image processing software such as PyGEE-SWToolbox and AlTis, were used to process large datasets from the Sentinel-1, Sentinel-2, and Sentinel-3 satellites. These satellites delivered extensive, high-resolution imagery and altimetric data, crucial for monitoring changes in the reservoirs. The processed data were validated with in-situ measurements, yielding a Root Mean Square Error (RMSE) of less than 0.4 m and a correlation coefficient exceeding 0.90. The results highlighted water surface and level changes from 2016 to 2022, with downward trends and seasonal variations closely aligning with in-situ measurements. The study also revealed that the relationship between water levels and surface areas is influenced by both precipitation and the hydrological regimes of the Bandama and Sassandra rivers, demonstrating the complexity of water dynamics in these reservoirs. This research emphasizes the effectiveness of integrating spatial hydrology with cloud computing tools for fast and accurate monitoring of large reservoir. The use of these advanced technologies provides near real-time, reliable, and easily accessible data, offering a significant advantage for water resource management in Côte d'Ivoire.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101353"},"PeriodicalIF":3.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of spaceborne synthetic aperture radar for invasive alien plant research","authors":"Glen Shennan,&nbsp;Richard Crabbe","doi":"10.1016/j.rsase.2024.101358","DOIUrl":"10.1016/j.rsase.2024.101358","url":null,"abstract":"<div><p>Recently, a strong international focus has been placed on invasive species and their ecological, economic, and social impacts. Satellite remote sensing (SRS) for the detection of invasive alien plants (IAPs) is a promising and actively researched application of satellite-derived earth observation data. Despite its all-day, all-weather detection and mapping capability, synthetic aperture radar (SAR) data is underrepresented in these efforts. This review discussed the foundational elements and capabilities of spaceborne SAR for IAP monitoring and investigated the current state of the scientific literature concerning the detection and monitoring of IAPs by spaceborne SAR. Twenty-six published articles were discovered and analysed for trends.</p><p>The analysis revealed several key findings regarding the current state of SAR in the detection and monitoring of IAPs. Data fusion techniques, especially those combining SAR with multispectral data, are gaining popularity due to their improved performance compared to single-sensor approaches. However, the full potential of SAR imagery, particularly polarimetric SAR (PolSAR), remains underutilised in multi-sensor studies. SAR analyses demonstrated strong performance in scenarios where the IAP structure exhibited distinct characteristics compared to its surroundings, such as plants isolated on water surfaces or palms displacing mangroves, due to the unique interactions of microwave radiation with the structural characteristics of targets.</p><p>Several key principles in the deployment of SAR were identified, including band and polarisation selection, basic techniques such as grey-level thresholding, and more advanced analyses such as polarimetry. Also noted are the capabilities of SAR in enabling indirect methods, such as inundation mapping and soil modelling. Suggestions are made for future directions in consideration of recently launched and forthcoming spaceborne SAR sensors. Significant among these are fully polarimetric systems which will provide freely accessible data, offering huge opportunities for sophisticated PolSAR analyses. This data will need to be fully exploited to advance species-level IAP detection and monitoring. Examples of IAPs which may benefit from SAR approaches are given, with special attention paid to the Australian Weeds of National Significance (WoNS).</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101358"},"PeriodicalIF":3.8,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352938524002222/pdfft?md5=e1a8fa93f828beab2c58ded7bcf83c70&pid=1-s2.0-S2352938524002222-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring long term Impervious Surface Areas (ISA) dynamics using Landsat imagery, Μachine Learning and GEE: The case of Attica, Greece","authors":"Aikaterini Dermosinoglou,&nbsp;George P. Petropoulos","doi":"10.1016/j.rsase.2024.101338","DOIUrl":"10.1016/j.rsase.2024.101338","url":null,"abstract":"<div><div>Accurate data on Impervious Surface Areas (ISA) are essential for various studies concerning urban environments, as the constant proliferation of these surfaces is a noticeable result of urbanization, especially in metropolitan cities. The present study proposes a methodology approach in performing a long-term mapping of ISA changes in Attica Prefecture, Greece, from 1984 to 2022, exploiting the Landsat archive and contemporary machine learning (ML) methods of geospatial data processing, namely Support Vector Machines (SVM) and Random Forests (RF). Using Google Earth Engine cloud platform, the SVM and RF classifiers are developed and implemented for four single dates (in years 1984, 1999, 2013 and 2022). Accuracy assessment of the classification maps was based on the computation of a series of statistical metrics based on the confusion matrix, ans the McNemar's chi-square test which was used to evaluate the statistical significance of the difference in the classification maps, derived from SVM and RF classifiers. Both SVM and RF provided very accurate results, with Overall Accuracy (OA) higher than 90% and kappa coefficient (Kappa) higher than 0.8 for all classification maps, with SVM performing better in 1984 and 2022 and RF outperforming SVM in 2013. In addition, the McNemar's test confirmed the statistical significance of the research findings reported herein. Change detection results, highlighted the wide sprawl of the urban fabric, especially in sub-urban areas, surrounding the metropolitan center of Athens. The employed methodology represents a significant advancement in the application of GEE, beyond their general use, by integrating cutting-edge ML techniques with available remote sensing data to create an automated analysis process. This innovative fusion not only enhances the precision and efficiency of ISA mapping but also establishes the basis for a pioneering standard in the field by harnessing the power of advanced technologies and accessible data sources.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101338"},"PeriodicalIF":3.8,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forest fragmentation and forest cover dynamics: Mining induced changes in the West Singhbhum District of Jharkhand","authors":"Md Saharik Joy,&nbsp;Priyanka Jha,&nbsp;Pawan Kumar Yadav,&nbsp;Taruna Bansal,&nbsp;Pankaj Rawat,&nbsp;Shehnaz Begam","doi":"10.1016/j.rsase.2024.101350","DOIUrl":"10.1016/j.rsase.2024.101350","url":null,"abstract":"<div><p>Forests play a crucial role in the global climate system by acting as important carbon storage sinks and controlling the flow of carbon between land and the atmosphere. They provide a wide range of ecosystem services, including the supply of resources and biodiversity conservation. Deforestation is a significant issue leading to the release of carbon dioxide and greenhouse gases. The destruction and fragmentation of existing habitats pose significant threats to biodiversity. This study examined land use/land cover (LULC) alterations in the West Singhbhum district between 1987 and 2021, specifically emphasizing the influence of mining operations on the local forest ecosystem. This study used Landsat satellite imagery to examine data from 1987 to 2021, emphasizing five primary classifications: water body, mining area, built-up areas, open/cropland, and forest/vegetation. The maps were reclassified into two categories, namely, “No-Forest\" and “Forest. Forest fragmentation maps were created using Landscape Fragmentation Tool (LFT) v2.0. A regression analysis was conducted to ascertain the correlation between mining growth and the reduction in forest cover. The analysis revealed increased mining areas, developed buildings, and cultivated land accompanied by a decline in forested areas and vegetation. There were substantial changes in land use, with mining areas expanding by 31.14 km² and open/cropland increasing by 30.39 km². The conversion of forested areas into agricultural zones and mining regions resulted in a 1.08% reduction in forest coverage.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101350"},"PeriodicalIF":3.8,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques","authors":"Atefeh Nouraki ,&nbsp;Mona Golabi ,&nbsp;Mohammad Albaji ,&nbsp;Abd Ali Naseri ,&nbsp;Saeid Homayouni","doi":"10.1016/j.rsase.2024.101354","DOIUrl":"10.1016/j.rsase.2024.101354","url":null,"abstract":"<div><p>Accurate root zone soil moisture (RZSM) estimation using remote sensing (RS) in areas with dense vegetation is essential for real-time field monitoring and precise irrigation scheduling. Traditional methods often face challenges due to the dense crop cover and the complexity of soil and climate interactions. These challenges include the coarse spatial resolution of available soil moisture products, the influence of vegetation and surface roughness, and the difficulty of estimating RZSM from surface data. Aiming to overcome these limitations, two RZSM estimation methods were developed by combining synthetic aperture radar (SAR) data from Sentinel-1 (VV and VH polarizations) and optical and thermal RS data from Landsat-8. These data sources were used in conjunction with various machine learning (ML) models such as M5-pruned (M5P), support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest regression (RFR) to improve the accuracy of soil moisture estimation. In addition to RS data, soil physical and hydraulic properties, meteorological variables, and topographical parameters were selected as inputs to the ML models for estimating the RZSM of sugarcane crops in Khuzestan, Iran. This study identified the temperature vegetation dryness index (TVDI) as a critical parameter for estimating RZSM in combination with the Sentinel-1 SAR data under high vegetation conditions. In both methods, the RFR algorithm outperformed, with similar performance, the XGBoost, SVR, and M5P algorithms in estimating soil surface moisture (R² = 0.89, RMSE = 0.04 cm³cm⁻³). However, the accuracy of the RFR algorithm decreased with increasing depth for both the optical-thermal and combined SAR and optical-thermal RS data. This decrease was more pronounced in the combined approach, particularly for the root zone, where the RMSE reached approximately 0.073 cm³cm⁻³. Accordingly, the key findings demonstrated that the optical-thermal RS data outperformed the SAR RS data for retrieving RZSM in high-vegetated areas. However, combining TVDI with SAR data is a substantial improvement that opens a new path in radar-based RZSM estimation methods under high vegetation conditions.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101354"},"PeriodicalIF":3.8,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}