- 标题
- 摘要
- 关键词
- 实验方案
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Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj district, Jharkhand (India)
摘要： Rice is an important staple food for the billions of world population. Mapping the spatial distribution of paddy and predicting yields are crucial for food security measures. Over the last three decades, remote sensing techniques have been widely used for monitoring and management of agricultural systems. This study has employed Sentinel-based both optical (Sentinel-2B) and SAR (Sentinel-1A) sensors data for paddy acreage mapping in Sahibganj district, Jharkhand during the monsoon season in 2017. A robust machine learning Random Forest (RF) classification technique was deployed for the paddy acreage mapping. A simple linear regression yield model was developed for predicting yields. The key findings showed that the paddy acreage was about 68.3–77.8 thousand hectares based on Sentinel-1A and 2B satellite data, respectively. Accordingly, the paddy production of the district was estimated as 108–126 thousand tonnes. The paddy yield was predicted as 1.60 tonnes/hectare. The spatial distribution of paddy based on RF classifier and the accuracy assessment of LULC maps revealed that SAR-based classified paddy map was more consistent than the optical data. Nevertheless, this comprehensive study concluded that the SAR data could be more pronounced in acreage mapping and yield estimation for providing timely information to decision makers.
关键词： Yield estimation,SAR data,Acreage mapping,Random Forest classifier
更新于2025-09-23 15:22:29
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[American Society of Agricultural and Biological Engineers 2018 Detroit, Michigan July 29 - August 1, 2018 - ()] 2018 Detroit, Michigan July 29 - August 1, 2018 - <i>Cotton Yield Estimation based on Plant Height From UAV-based Imagery Data </i>
摘要： Accurate estimation of crop yield before harvest, especially in early growth stages, is important for farmers and researchers to optimize field management and evaluate crop performance. However, conventional methods of using ground sensing to estimate crop yield are not efficient. The goal of this research was to evaluate the potential of using a UAV-based remote sensing system with a low-cost RGB camera to estimate yield of cotton within season. The UAV system took images at 50 m above ground level over a cotton field at the growth stage of first flower. Waypoints and flight speed were selected to allow > 70% image overlap in both forward and side directions. Images were processed to develop a geo-referenced orthomosaic image and a digital elevation model (DEM) of the field, which was then used to map plant height by calculating the difference in elevation between the crop canopy and the bare soil surface. Twelve ground control points (calibration objects) with known GPS coordinates and height were deployed in the field and were used as check points for geo-referencing and height calibration. Geo-referenced yield data were registered with the plant height map row-by-row. Correlation analysis between yield and plant height was conducted row-by-row with row registration and without row registration respectively. Pearson correlation coefficients between yield and plant height for all individual rows were in the range of 66% to 96%, higher than those without row registration (54% to 95%). A non-parametric regression used for building a yield estimation model based on image-derived plant height was able to estimate yield with less than 10% error (root mean square error of 360.4 kg ha-1 and mean absolute error of 180.9 kg ha-1). The results indicated that the UAV-based remote sensing system equipped with a low-cost digital camera was able to estimate cotton yield with acceptable errors.
关键词： yield estimation,UAV-based remote sensing,geo-registration,plant height,Cotton
更新于2025-09-23 15:21:01
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Room-Temperature Electrically Pumped InP-based $1.3\boldsymbol{\mu} \mathbf{m}$ Quantum Dot Laser on on-axis (001) Silicon
摘要： We present a method for quantifying a risk for killer defects at layer level and estimating yield for substrate packages using information from design ?les. To calculate risk ranks and predicted yield, we de?ne a risk distance that is a key parameter extracted from designs using image processing techniques. In order to validate our model, we analyze two different designs, each having multiple layers, and compare with data from baseline lots. It is shown that there is an inverse correlation between risk layer ranks and yield. Estimated yield based on our model is compared with baseline yield for four layers of the second design. The model-to-baseline yield difference is less than 1% for three layers we tested.
关键词： yield estimation,assembly,circuit analysis,metrology sampling,Yield prediction,integrated circuit packaging
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE SENSORS - Montreal, QC, Canada (2019.10.27-2019.10.30)] 2019 IEEE SENSORS - Industrial gas analytics using a compact ultraviolet laser
摘要： In this paper, we propose a simple, yet reliable methodology to expedite yield estimation and optimization of microwave structures. In our approach, the analysis of the entire response of the structure at hand (e.g., S-parameters as a function of frequency) is replaced by response surface modeling of suitably selected feature points. On the one hand, this is sufficient to determine whether a design satisfies given performance specifications. On the other, by exploiting the almost linear dependence of the feature points on the designable parameters of the structure, reliable yield estimates can be realized at low computational cost. Our methodology is verified using two examples of waveguide filters and one microstrip hairpin filter and compared with conventional Monte Carlo analysis based on repetitive electromagnetic simulations, as well as with statistical analysis exploiting linear response expansions around the nominal design. Finally, we perform yield-driven design optimizations on these filters.
关键词： microwave component modeling,yield-driven design,electromagnetic (EM) modeling,yield estimation,tolerance-aware design,Design centering,statistical analysis
更新于2025-09-23 15:19:57
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Effect of Annealing on The Bottom Cell in GaInP/GaAs/GaInNAsSb Triple Junction Solar Cells by MBE/MOCVD Hybrid Growth
摘要： We present a method for quantifying a risk for killer defects at layer level and estimating yield for substrate packages using information from design ?les. To calculate risk ranks and predicted yield, we de?ne a risk distance that is a key parameter extracted from designs using image processing techniques. In order to validate our model, we analyze two different designs, each having multiple layers, and compare with data from baseline lots. It is shown that there is an inverse correlation between risk layer ranks and yield. Estimated yield based on our model is compared with baseline yield for four layers of the second design. The model-to-baseline yield difference is less than 1% for three layers we tested.
关键词： yield estimation,assembly,circuit analysis,metrology sampling,Yield prediction,integrated circuit packaging
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - The Luminescent Down Shifting Effect of Single-Junction GaAs Solar Cell with Perovskite Quantum Dots
摘要： We present a method for quantifying a risk for killer defects at layer level and estimating yield for substrate packages using information from design ?les. To calculate risk ranks and predicted yield, we de?ne a risk distance that is a key parameter extracted from designs using image processing techniques. In order to validate our model, we analyze two different designs, each having multiple layers, and compare with data from baseline lots. It is shown that there is an inverse correlation between risk layer ranks and yield. Estimated yield based on our model is compared with baseline yield for four layers of the second design. The model-to-baseline yield difference is less than 1% for three layers we tested.
关键词： metrology sampling,circuit analysis,assembly,yield estimation,integrated circuit packaging,Yield prediction
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Ultra-Stable Optical Oscillator Transfer to the UV for Primary Thermometry
摘要： To improve the performance of crop models for regional crop yield estimates, a particle filter (PF) was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)—Wheat model. Two experiments involving winter wheat yield estimations were conducted at a field plot and on a regional scale to test the feasibility of the PF-based data assimilation strategy and to analyze the effects of the PF parameters and spatio-temporal scales of assimilating observations on the performance of the crop model data assimilation. The significant improvements in the yield estimation suggest that PF-based crop model data assimilation is feasible. Winter wheat yields from the field plots were forecasted with a determination coefficient (R2) of 0.87, a root-mean-square error (RMSE) of 251 kg/ha, and a relative error (RE) of 2.95%. An acceptable yield at the county scale was estimated with a R2 of 0.998, a RMSE of 9734 t, and a RE of 4.29%. The optimal yield estimates may be highly dependent on the reasonable spatiotemporal resolution of assimilating observations. A configuration using a particle size of 50, LAI maps with a moderate spatial resolution (e.g., 1 km), and an assimilation interval of 20 d results in a reasonable tradeoff between accuracy and effectiveness in regional applications.
关键词： particle filter (PF),yield estimation,data assimilation,Crop model,leaf area index,remote sensing
更新于2025-09-19 17:13:59
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Assimilating SAR and Optical Remote Sensing Data into WOFOST Model for Improving Winter Wheat Yield Estimation
摘要： Sufficient remote sensing observation data during crop main growing season is of great importance in improving the accuracy of data assimilation of crop model. The optical remote sensing data are susceptible to cloud and rain, so the amount of clear optical data is very limited in cloudy weather or rainy day. Synthetic Aperture Radar (SAR) is not dependent on cloud cover or light conditions, it can penetrate through clouds and have all-weather capabilities. This allows for a more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. So, the aim of this article is to improve the accuracy for winter wheat yield estimation by joint assimilation of SAR and optical satellite images into crop model. In this study, SAR images are acquired by C-band SAR sensor boarded on Sentinel-1 satellites, and optical images are obtained from Sentinel-2 satellites. Remote sensing data and ground data are all collected during the main growth and development stages of winter wheat. Both normalized difference vegetation index (NDVI) derived from Sentinel-2 images and backscattering coefficients and polarimetric from Sentinel-1 images are used in water cloud model to derive soil moisture (SM) time series images. To improve the prediction of crop yields at filed scale, we incorporate remotely sensed soil moisture into the WOrld FOod STudies (WOFOST) model using Ensemble Kalman filter (EnKF) algorithm. In general, the results show that data assimilation schemes of remotely sensed soil moisture slightly improved the correlation of observed and simulated yields (R2 = 0.30; RMSE =782 kg ha-1) compared to the situation without data assimilation (R2 = 0.14; RMSE = 1398 kg ha-1). Results of this study indicate that the potential for assimilation SAR and optical remote sensing data to improve field yield estimates is relatively low, limitations are due to insufficient no-cloud optical remotely sensed data and root zone indicators computed soil moisture information. Consequently, the results of this study demonstrate the potential and usefulness of assimilating both SAR and optical remote sensing data into crop model for crop monitoring and yield estimation. Moreover, this also provides the reference for crop yield estimation with data assimilation in other agricultural landscapes.
关键词： SAR,WOFOST,EnKF,yield estimation,LAI,winter wheat,optical
更新于2025-09-11 14:15:04
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Automated detection of individual clove trees for yield quantification in northeastern Madagascar based on multi-spectral satellite data
摘要： There is an increasing demand for clove products, mainly dried buds and essential oil on global markets. Consequently, the importance of clove trees as a provisioning service is increasing at the local level, particularly for smallholders cultivating clove trees as cash crops. Due to limited availability of data on local production, using remote sensing-based methods to quantify today's clove production is of key interest. We estimated the clove bud yield in a study site in northeastern Madagascar by detecting individual clove trees and determining relevant production systems, including pasture and clove, clove plantation and agroforestry systems. We implemented an individual tree detection method based on two machine learning approaches. Specifically, we proposed using a circular Hough transform (CHT) for the automated detection of individual clove trees. Subsequently, we implemented a tree species classification method using a random forests (RF) classifier based on a set of features extracted for relevant trees in the above production systems. Finally, we classified and mapped different production systems. Based on the number of detected clove trees growing in a clove production system, we estimated the production system-dependent clove bud yield. Our results show that 97.9% of all reference clove trees were detected using a CHT. Classifying clove and non-clove trees resulted in a producer accuracy of 70.7% and a user accuracy of 59.2% for clove trees. The classification of the clove production systems resulted in an overall accuracy of 77.9%. By averaging different clove tree yield estimates obtained from the literature, we estimated an average total yield of approximately 575 tons/year for our 25,600 ha study area. With this approach, we demonstrate a first step towards large-scale clove bud yield estimation using remote sensing data and methodologies.
关键词： Random forest,Tree species classification,Very high-resolution satellite image,Pléiades satellite,LULC classification,Single tree detection,Circular Hough transform,Clove bud yield estimation
更新于2025-09-09 09:28:46