2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)最新文献_第4页

Crop Yield Prediction Using Integration Of Polarimteric Synthetic Aperture Radar And Optical Data 基于偏振合成孔径雷达与光学数据集成的作物产量预测

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358978

M. Hosseini, I. Becker-Reshef, R. Sahajpal, Lucas Fontana, P. Lafluf, G. Leale, E. Puricelli, M. Varela, C. Justice

{"title":"Crop Yield Prediction Using Integration Of Polarimteric Synthetic Aperture Radar And Optical Data","authors":"M. Hosseini, I. Becker-Reshef, R. Sahajpal, Lucas Fontana, P. Lafluf, G. Leale, E. Puricelli, M. Varela, C. Justice","doi":"10.1109/InGARSS48198.2020.9358978","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358978","url":null,"abstract":"In this study, double-bounce parameter derived from Sentinel-1 was integrated with Difference vegetation index (DVI) derived from Landsat-8 for prediction of soybean yield at field level over central Argentina. Artificial Neural Network (ANN) model was trained using time series of Synthetic Aperture Radar (SAR) and optical features during the growing season. For comparison of SAR versus optical versus their integration for soybean yield prediction, the ANN model was trained and tested for three scenarios of SAR-only, optical-only and SAR-optical integration. Accuracies of yield prediction including correlation of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) are 0.80, 0.589 t/ha, 0.445 t/ha for SAR-only; 0.65, 0.800 t/ha, 0.546 t/ha for optical-only; and 0.85, 0.554 t/ha, 0.389 t/ha for SAR-optical integration scenarios, respectively. These accuracies demonstrate of high potential of SAR and SAR-optical integration for soybean yield prediction at field level.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"35 1","pages":"17-20"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80632464","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}

引用次数: 0

Analysis of Geochemical Data of Mica for the Development of Mineral Resources: Case of Southern Madagascar, Beraketa. 云母地球化学资料在矿产资源开发中的应用——以马达加斯加南部为例。

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358964

Miora Harivony Rakotondrabe, A. Mohandas, E. Rasolomanana

引用次数: 0

Sar And Optical Data Fusion Based On Anisotropic Diffusion With Pca And Classification Using Patch-Based Svm With Lbp 基于Pca的各向异性扩散Sar与光学数据融合与基于patch的Svm分类

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358949

Achala Shakya, M. Biswas, M. Pal

引用次数: 1

InGARSS 2020 Committees

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/ingarss48198.2020.9358914

引用次数: 0

Identification of Water-Stressed Area in Maize Crop Using Uav Based Remote Sensing 基于无人机的玉米作物缺水区遥感识别

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358930

A. Kumar, Shreeshan S, Tejasri N, P. Rajalakshmi, W. Guo, B. Naik, B. Marathi, U. Desai

{"title":"Identification of Water-Stressed Area in Maize Crop Using Uav Based Remote Sensing","authors":"A. Kumar, Shreeshan S, Tejasri N, P. Rajalakshmi, W. Guo, B. Naik, B. Marathi, U. Desai","doi":"10.1109/InGARSS48198.2020.9358930","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358930","url":null,"abstract":"Agronomic inputs such as water , nutrients and fertilisers play a vital role in the health, growth and yield of crops. The lack of each of these inputs induces biotic and abiotic stress in the crop. Farmers are relying on groundwater because of decreased rainfall. The irrigation method can be improved by acquiring awareness of the health of crops and soils. In general, crop and soil quality is controlled by means of manual observation, which is time-consuming, labour-intensive and contributes to incorrect choices and substantial waste of resources. There is also an immediate need to automate the inspection process that will finally benefit farmers and agricultural scientists. In this paper, the identification of the water-stressed areas in the crop(maize) field has been studied, and an Unmanned Aerial Vehicle (UAV) based remote sensing is used to automate the crop health-monitoring process. We proposed a framework (model) based on Convolutional Neural Networks (CNN) to identify the stressed and normal/healthy areas in the maize crop field. The performance of the proposed framework has been compared with different models of CNN, such as ResNet50, VGG-19, and Inception-v3. The results show that the proposed model outperforms the baseline models and successfully classify stressed and normal areas with 95 % accuracy on train data and 93 % accuracy with 0.9370 precision and 0.9403 F1 score on test data.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"24 1","pages":"146-149"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90107983","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}

引用次数: 7

InGARSS 2020 Cover Page InGARSS 2020封面

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/ingarss48198.2020.9358937

引用次数: 0

Land Use Changes and Their Effects on Urban Ecosystem Services Value: A Study of Khulna City, Bangladesh 土地利用变化及其对城市生态系统服务价值的影响——以孟加拉国库尔纳市为例

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358927

M. Patwary, Sadia Ashraf, F. Shuvo

引用次数: 1

UAV-Thermal Imaging: A Robust Technology to Evaluate in-field Crop Water Stress and Yield Variation of Wheat Genotypes 无人机热成像:一种评估小麦基因型田间作物水分胁迫和产量变化的可靠技术

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358955

Sumanta Das, J. Christopher, A. Apan, Malini Roy Choudhury, S. Chapman, N. Menzies, Y. Dang

{"title":"UAV-Thermal Imaging: A Robust Technology to Evaluate in-field Crop Water Stress and Yield Variation of Wheat Genotypes","authors":"Sumanta Das, J. Christopher, A. Apan, Malini Roy Choudhury, S. Chapman, N. Menzies, Y. Dang","doi":"10.1109/InGARSS48198.2020.9358955","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358955","url":null,"abstract":"In recent years, unmanned aerial vehicle (UAV) - based thermal imaging techniques have become increasingly popular in precision agriculture, especially in monitoring crop biotic and abiotic stresses, and soil water, irrigation scheduling, and residue mapping. However, studies are limited on thermal imaging techniques in yield estimation and in-field variability assessment. Here we evaluate the potential of UAV thermal imaging techniques to assess crop water stress and predict grain yield of 18 contrasting wheat genotypes. We conducted an airborne campaign close to crop flowering to capture thermal imagery for a rain fed wheat experimental field in southern Queensland, Australia. Plot wise canopy temperatures (°C) (Tcanopy) were extracted from thermal imagery to determine crop water stress index (CWSI). Wheat grain yield was significantly correlated with CWSI (R2= 0.63; RMSE= 0.34 t/ha). The results suggest potential for UAV thermal imaging techniques to measure crop water status and predict yield under water-limited environments.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"150 6 1","pages":"138-141"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83150101","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}

引用次数: 7

Long Term Trend of Aerosol Optical Depth (AOD) over Ahmedabad and Gandhinagar: A Satellite Approach 艾哈迈达巴德和甘地那加尔气溶胶光学深度(AOD)的长期趋势:卫星方法

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358958

Khushi Chanllawala, Tejas Turakhia, Rajesh C. Iyer

引用次数: 0

Surface Deformation of The 2019 Mirpur Earthquake Estimated from Sentinel-1 Insar Data 从Sentinel-1 Insar数据估计2019年Mirpur地震的地表变形

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358915

Divya Sekhar Vaka, Y. S. Rao, T. Singh

引用次数: 5