Journal of the Indian Society of Remote Sensing最新文献

{"title":"Detection of Heterobasidion Root Rot on Pinus brutia Ten. Using Different Vegetation Indices Generated from Sentinel-2 A Satellite Imagery","authors":"Tunahan Çınar, R. Ceyda Beram, Abdurrahim Aydın, Sultan Akyol, Nurzhan Tashigul, H. Tuğba Lehtijärvi, Steve Woodward","doi":"10.1007/s12524-024-01914-1","DOIUrl":"https://doi.org/10.1007/s12524-024-01914-1","url":null,"abstract":"<p>The genus <i>Heterobasidion</i> includes some of the most destructive pathogens of conifers in the Northern hemisphere. Heterobasidion root rot leads to loss of root function and visible symptoms in the crowns of most <i>Pinus</i> spp., including Turkish red pine (<i>P. brutia</i>). Infected pines will eventually die. Wind-thrown trees with decayed roots or open gaps in the stand often indicate the presence of Heterobasidion root rot. Satellite imagery has recently been utilized regularly to detect damaged areas in order to apply early management procedures to pests or diseases in forests, reducing spread within an affected site and to other places. In the work described here, Sentinel-2 A satellite imagery was tested for detecting Heterobasidion root rot in <i>P. brutia</i> regeneration in an area in south-western Turkiye, using different vegetation indices. Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Plant Senescence Reflectance Index (PSRI) indices were calculated from Sentinel-2 A satellite images in the Google Earth Engine (GEE) platform to detect disease. Calculated indices as synthetic band were added to the Sentinel-2 A satellite image on the GEE platform. Images with the added bands were classified using Random Forest (RF) before evaluation using the Kappa Coefficient and Overall Accuracy. Based on a statistical analysis, NDRE was the most useful index for detecting the disease with an overall accuracy of 89% and a Kappa Coefficient of 0.84, followed by NDVI and PSRI, respectively. After evaluation of General Accuracy and Kappa Coefficient, disease incidence in the area was determined (affected hectares), based on the indices. NDRE detected 7.21 affected hectares, NDVI 7.9 hectares and PSRI 6.49 hectares in a total of 67.8 hectares. Sentinel-2 A bands, which allow the measurement of various land and vegetation health parameters, the effect of bands on RF classification was determined according to the indices used. The most important band for classification of NDRE and NDVI was the B2 (BLUE) band of Sentinel-2 A, and the most important band with PSRI was the B5 (RED EDGE) band. Based on these bands, the best wavelengths for detecting <i>H. annosum</i> diseased areas were in the range 492.4–740.5 nm in Sentinel-2 A. The system enabled the detection of differences in crown deterioration and also wind-thrown trees with decayed roots or open gaps in the stand. This study is the first to show that Sentinel-2 A satellite imagery can be applied successfully for the detection of Heterobasidion root rot on <i>P. brutia</i>.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"32 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting Methane Emissions from Space Over India: Analysis Using EMIT and Sentinel-5P TROPOMI Datasets","authors":"Asfa Siddiqui, Suvankar Halder, Hareef Baba Shaeb Kannemadugu, Prakriti, Prakash Chauhan","doi":"10.1007/s12524-024-01925-y","DOIUrl":"https://doi.org/10.1007/s12524-024-01925-y","url":null,"abstract":"<p>Methane (CH₄) is a potent greenhouse gas and the second highest anthropogenic emissions are recorded from CH₄ on Earth. Considering its high global warming potential, the monitoring of source locations is inadvertent. The paper presented here is the first attempt (to the best of our knowledge) to comprehensively analyse the methane emissions over multiple Indian locations using satellite data. It outlays a brief background of methane emission sensors and studies carried out worldwide for estimation of the GHG. It further enumerates the potential of Earth Surface Mineral Dust Source Investigation (EMIT) and TROPOspheric Monitoring Instrument (TROPOMI) in highlighting the potential point sources of methane emissions and its concentration/emission flux in India. 17 unique plumes were identified using EMIT in the states of Maharashtra (06), Rajasthan (04), Punjab (02), Gujarat (03) and Assam (02). Gujarat, Surat, Assam Uttar Pradesh and Haryana using TROPOMI were also studied. The hotspots showcase emission sources from solid waste landfill sites, sewage treatment plants, wetlands/marshy agriculture, city sewage outlets, oil and gas fields, oil refinery and textile industry. It was observed that EMIT can effectively be used for point source identification, monitoring and enhancement while TROPOMI is best suited for regional level methane monitoring. A sewage outlet plume in Maharashtra produced the maximum emission of 6202.9 ± 691.94 kg/hr followed by solid waste (SW) sites located in Pirana Landfill, Ahmedabad and Khajod Landfill, Surat in Gujarat. Methane monitoring is an important step towards mitigating enormous methane emissions and anomalous methane sources.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"8 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Snowmelt Runoff Model (SRM) Performance Incorporating Remotely Sensed Data","authors":"Maryam Naghdi, Mehdi Vafakhah, Vahid Moosavi","doi":"10.1007/s12524-024-01921-2","DOIUrl":"https://doi.org/10.1007/s12524-024-01921-2","url":null,"abstract":"<p>Snow has a significant impact on the hydrological cycle, contributing to energy generation, meeting agricultural demands, and providing drinking water. Effective management of snowmelt runoff can help control and prevent potential risks. The purpose of the study is to evaluate the use of the remotely sensing data to improve the estimation accuracy of the snowmelt-runoff by using the Snowmelt-Runoff Model (SRM). To do this, a total of 1595 Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were prepared between 2014 and 2015 to acquire data on precipitation, minimum and maximum temperatures and Snow Cover Area (SCA). The accuracy of precipitation data was evaluated using Root Mean Squared Error (RMSE) and Root Mean Squared Log-Error (RMSLE) to ensure their reliability. Additionally, a sensitivity analysis of the SRM model’s coefficients, particularly for recession coefficient (K) and snow runoff (C_s), was conducted to understand their impact on the model’s performance. In this study, meteorological station data and satellite data from the years 2014 and 2015 were utilized for the validation and calibration stages, respectively. The model’s ability to estimate snowmelt runoff using remote sensing data was evaluated using both on-site stations and satellite data. In the calibration period, the snowmelt runoff estimation results were obtained with Nash-Sutcliffe Efficiency (NSE) index values of 0.72 and 0.70 for on-site stations and satellite data, respectively. In the validation period, the NSE index values were 0.60 and 0.93 for on-site stations and satellite data, respectively indicating improved performance when using satellite data to estimate the snowmelt runoff. The study’s findings show that remote sensing data enhances the performance of the SRM model for estimating the snowmelt-runoff.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"23 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long Term Monitoring of Ecological Status of Major Deserts of the World","authors":"Amit Kushwaha, Rimjhim Bhatnagar, Praveen Kumar, Claudio Zucca, Sanjay Srivastava, Ajai","doi":"10.1007/s12524-024-01915-0","DOIUrl":"https://doi.org/10.1007/s12524-024-01915-0","url":null,"abstract":"<p>Deserts are unique ecosystems that provides suitable habitats to many floral and faunal species and that are beneficial to human beings in many ways. Desert ecosystems are affected by several natural and anthropogenic factors, resulting in the degradation of ecosystem goods and services provided by them. Thus, there is a need to monitor them. Accordingly, the ecological status of 34 major non-polar deserts of the world have been monitored for a period of four decades. We have used (i) vegetation cover and NDVI (vegetation density/vigour) as indicators of ecological conditions, and (ii), long term rainfall and temperature patterns to monitor the extent and the effect of climatic variations. Among the 34 deserts, Taklimakan has consistently the lowest NDVI, while Tanami has the highest NDVI during the entire monitoring period. The Asian Kavir and Kharan deserts have the lowest vegetation cover; Tanami has the highest vegetation cover. Out of 34 deserts, Gobi, Kalahari, Margo, Mu Us, Simpson, Strzelecki, Taklimakan and Thar deserts have shown an increasing trend in vegetation cover. While, Chalbi, Patagonian and Sonoran deserts have shown a decreasing trend. Thar, Sechura and Sahara have shown an increasing trend in precipitation, while Namib has shown an opposite trend. 31 deserts have shown an increasing trend in the temperature. Present study is important as changes in the ecological conditions of the deserts have a profound impact on the land surface albedo, surface energy balance, regional climate, carbon sequestration, biodiversity, and global dust emissions.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"179 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Depth-Wise Separable Convolutional Model for Remote Sensing Scene Classification","authors":"Soumya Ranjan Sahu, Sucheta Panda","doi":"10.1007/s12524-024-01904-3","DOIUrl":"https://doi.org/10.1007/s12524-024-01904-3","url":null,"abstract":"<p>With the advancement in satellite and Artificial Intelligence (AI), the increase in observation of the earth is increasing dramatically. With this development, the demand in the field of Remote Sensing (RS) is also growing rapidly. The spatial resolution and textural information of remote sensing images can be improved by introducing AI and Machine Learning (ML) technology. In the modern era of computer science, Deep Learning (DL) models are more familiar in the field of scene classification. This paper aims to develop a novel depth-wise CNN model to classify the RS images with low time effort during training with higher accuracy than the existing CNN model. For comparison, three typical CNN models of VGG16, VGG19, ResNet50 and RegNet are taken and tested on the RS datasets for classification. The experimented analysis demonstrates that the proposed classification model surpasses the existing classification models by producing higher accuracy in testing by taking a minimum time duration for training the RS datasets.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Rock Glacier Dynamics and Infiltration-Driven Thinning in the Accumulation Region through SAR Interferometry with VV-Polarized Sentinel-1A/1B SAR Data","authors":"Bala Raju Nela, Girjesh Dasaundhi, Ajay Kumar, Pratima Pandey, Praveen Kumar","doi":"10.1007/s12524-024-01918-x","DOIUrl":"https://doi.org/10.1007/s12524-024-01918-x","url":null,"abstract":"<p>In this study, we estimated the spatial distribution of rock glacier velocities and elevation changes over the region of the Indian state of Himachal Pradesh. The rock glacier velocities are estimated by using the Differential Synthetic Aperture Radar Interferometric (DInSAR) technique. DInSAR is observed as accurate and optimum for rock glacier dynamic studies. From the 185 rock glaciers selected for this study, 127 of them are moving with a mean velocity of 35 cm/yr. (≈ 1 mm/day). However, none of these rock glaciers mean velocities exceed 100 cm/yr. The elevation change of rock glaciers is estimated using the DEM differencing technique. The DEM differencing with the SRTM C-band DEM and TanDEM-X DEM was employed to estimate the rock glaciers thickness change from 2000 to 2014. Among 185 glaciers, the mean thickness change is positive for 58 rock glaciers and negative for 127 rock glaciers. The elevation change of rock glaciers is ranging from − 19 m (thinning) to 10 m (mass gain) for 2000–2014 time period. The mean annual elevation change of these rock glaciers is observed as − 0.175 m. The study also compared the previous research work related to the ice glaciers velocity and thickness changes of this same region. Similar to the velocity, the elevation change of rock glaciers is smaller compared to the ice glaciers. In general, the thinning of the ice glaciers is associated with the ablation region, and mass gain with the accumulation region. Despite the significant evidence of thinning is observed mostly in the accumulation region for the rock glaciers. These elevation changes are correlated with the velocity measurements for the notable rock glaciers. Most of the rock glaciers observed high velocities in the accumulation region are also observed the thinning in the same region. The correlation of these two parameters might be associated with infiltration. However, the designated analysis of the rock glacier’s internal structure is expected to provide a correlation between the velocity and thickness change of the rock glaciers.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"227 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Prediction of Soil Salinity by Using Remote Sensing and Data Mining Algorithms at Watershed Scale, Northwest Iran","authors":"Afshin Honarbakhsh, Ebrahim Mahmoudabadi, Sayed Fakhreddin Afzali, Mohammad Khajehzadeh","doi":"10.1007/s12524-024-01906-1","DOIUrl":"https://doi.org/10.1007/s12524-024-01906-1","url":null,"abstract":"<p>Soil salinity plays an important role in agriculture production and land degradation, especially in semi-arid and arid regions. Accurate prediction of soil salinity requires evaluating crop yield, native vegetation situations, and irrigation command area management. In this study, MLR (multiple linear regression), SVMs (support vector machines) and ANNs (artificial neural networks) models were employed by using Landsat-8 OLI and GIS (Geographical Information Systems) techniques for predicting soil salinity in northwest Iran. Soil salinity was measured at 92 points (in a depth of 0–20 cm). The vegetation and soil salinity spectral indices, extracted from Landsat-8 OLI, were employed as input data. The results of this study indicated that the best-developed model for predicting soil salinity was the SVM-based model with R² (0.874) and RPD (2.32) and the lowest RMSE (11.20 dS m⁻¹). Moreover, the performance of developed models under different vegetation coverage showed that the SVM-based model yielded the best result. It was concluded that the SVM-based model is reliable for quantifying soil salinization.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"26 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the Surface Thermal Discomfort Index (STDI) in a Tropical Environments using Multi Sensors: A Case Study of East Kalimantan, The Future New Capital City of Indonesia","authors":"Parwati Sofan, Khalifah Insan Nur Rahmi, Nurwita Mustika Sari, Jalu Tejo Nugroho, Trinah Wati, Anjar Dimara Sakti","doi":"10.1007/s12524-024-01919-w","DOIUrl":"https://doi.org/10.1007/s12524-024-01919-w","url":null,"abstract":"<p>Thermal Discomfort Index has traditionally relied on parameters such as air temperature and relative humidity, obtained either from meteorological ground stations or through land-physical approaches estimated independently by satellites. These methods often fall short in adequately capturing both seasonal and detailed local spatial variations. This study addresses these limitations by establishing the Surface Thermal Discomfort Index (STDI), a composite of the Meteorological Discomfort Index (MDI) and the Discomfort Index over the land surface (DI_-Land). Focused on Ibu Kota Negara Nusantara (IKN) in East Kalimantan and neighboring cities, MDI is derived from reanalysis data (ERA5-Land), validated with ground station data, while DI_-Land is produced primarily from Landsat-8. An equal weighting factor was applied to MDI and DI_-Land for estimating STDI. Results indicate that STDI captures both seasonal and spatial variations, reaching peak level in May and October, and hitting a low point in July. The spatial distribution of STDI is influenced by landuse types. In 2023, IKN experienced an STDI of 26.2 °C, while Balikpapan and Samarinda recorded at 26.5 and 26.4 °C, respectively. Compared to previous study in Jakarta, IKN and neighboring cities’s STDI are higher up to 0.2 °C, remaining within the partially comfortable range in the tropics. Projecting IKN’s development until 2045, an annual MDI increase of 0.01 °C is anticipated. Moreover, a 4% rise in built-up areas is expected to elevate STDI by 0.1–0.2 °C. This study provides insights into the thermal discomfort status in cities across East Kalimantan, anticipating a gradual increase in discomfort levels during the development of IKN.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"127 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal and Spatial Changes and Driving Forces of Carbon Stocks and Net Ecosystem Productivity: A Case Study of Zoige County, Sichuan Province, China","authors":"Xiyang Feng, Zhe Wang, Zhenlong Zhang, Jiaqian Zhang, Qiuping Zeng, Duan Tian, Chao Li, Li Jiang, Yong Wang, Bo Yuan, Yan Zhang, Jianmei Zhu","doi":"10.1007/s12524-024-01911-4","DOIUrl":"https://doi.org/10.1007/s12524-024-01911-4","url":null,"abstract":"<p>This study analysed the spatiotemporal changes in carbon stocks and Net Ecosystem Productivity (NEP) in Zoige County, Upper Yellow River, from 2000 to 2020 in response to China’s ecological civilization ideology and sustainable development. The carbon stock module of the InVEST model and carbon source/sink calculation formula were employed, and GeoDetector was used to analyze driving forces and spatial distributions. The findings were as follows: (1) The land use in Zoige County had undergone significant changes over the past two decades, characterized by a reduction in grassland area due to its conversion into woodland and peat wetland. (2) The carbon stock in Zoige County had consistently increased, accumulating 5.19 × 106 tons. (3) Zoige County had functioned as net ecosystem productivity (NEP) over the past two decades, with increasing trends, averaging 3.335 kg C/m². (4) The primary driving force behind changes in carbon stock and NEP were identified as ‘biological abundance’.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"26 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Efficient Channel Attention and Small-Scale Layer to YOLOv5s for Wheat Ears Detection","authors":"Feijie Dai, Yongan Xue, Linsheng Huang, Wenjiang Huang, Jinling Zhao","doi":"10.1007/s12524-024-01913-2","DOIUrl":"https://doi.org/10.1007/s12524-024-01913-2","url":null,"abstract":"<p>Wheat is a crucial global grain crop that plays a vital role in ensuring food security worldwide. The automatic and accurate counting of wheat ears is essential for assessing wheat yield. However, the detection accuracy is greatly affected by the complex background and small target size. To address these challenges and improve the performance, we propose an enhanced YOLOv5s method. In the backbone, we introduce the efficient channel attention (ECA) to enhance the feature extraction capability of the original C3 module. Additionally, we incorporate a small-scale detection layer in the neck and prediction stages. This modification expands the original three-scale feature detection (20 × 20, 40 × 40, and 80 × 80) to a four-scale feature detection (20 × 20, 40 × 40, 80 × 80, and 160 × 160), thereby enhancing the recognition accuracy of small targets. Experimental results demonstrate that our method achieves an Accuracy (Acc) of 93.97%, which represents a 2.94% improvement over the YOLOv5s. Additionally, our method has a mean absolute error (MAE) of 0.57, a reduction of 0.6 from the YOLOv5s. The Acc of the improved YOLOv5s approaches that of YOLOv7; however, the giga floating-point operations per second (GFLOPs) and inference speed of the enhanced YOLOv5s are significantly lower than those of YOLOv7. Across various phases of the wheat test dataset, the enhanced model demonstrated superior performance. As a result, the enhanced YOLOv5s enhances its suitability for challenging field conditions and offers a dependable technical framework for ear detection and wheat yield estimation.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"31 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}