Improved Yield Prediction of Ratoon Rice Using Unmanned Aerial Vehicle-Based Multi-Temporal Feature Method

Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture.However,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead to inconsistent rice phenology,which had a significant impact on yield prediction of ratoon rice.Multi-temporal unmanned aerial vehicle(UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods.Thus,in this study,we explored the performance of combination of agronomic practice information(API)and single-phase,multi-spectral features[vegetation indices(VIs)and texture(Tex)features]in predicting ratoon rice yield,and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice.The results showed that the integrated use of VIs,Tex and API(VIs&Tex+API)improved the accuracy of yield prediction than single-phase UAV imagery-based feature,with the panicle initiation stage being the best period for yield prediction(R^(2) as 0.732,RMSE as 0.406,RRMSE as 0.101).More importantly,compared with previous multi-temporal UAV-based methods,our proposed multi-temporal method(multi-temporal model VIs&Tex:R^(2) as 0.795,RMSE as 0.298,RRMSE as 0.072)can increase R^(2) by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield forecasting.This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture,which is of great significance to take timely means for ensuring ratoon rice production and food security.

An Edge-assisted, Object-oriented Random Forest Approach for Refined Extraction of Tea Plantations Using Multi-temporal Sentinel-2 and High-resolution Gaofen-2 Imagery

As a consumed and influential natural plant beverage,tea is widely planted in subtropical and tropical areas all over the world.Affected by(sub)tropical climate characteristics,the underlying surface of the tea distribution area is extremely complex,with a variety of vegetation types.In addition,tea distribution is scattered and fragmentized in most of China.Therefore,it is difficult to obtain accurate tea information based on coarse resolution remote sensing data and existing feature extraction methods.This study proposed a boundary-enhanced,object-oriented random forest method on the basis of high-resolution GF-2 and multi-temporal Sentinel-2 data.This method uses multispectral indexes,textures,vegetable indices,and variation characteristics of time-series NDVI from the multi-temporal Sentinel-2 imageries to obtain abundant features related to the growth of tea plantations.To reduce feature redundancy and computation time,the feature elimination algorithm based on Mean Decrease Accuracy(MDA)was used to generate the optimal feature set.Considering the serious boundary inconsistency problem caused by the complex and fragmented land cover types,high resolution GF-2 image was segmented based on the MultiResolution Segmentation(MRS)algorithm to assist the segmentation of Sentinel-2,which contributes to delineating meaningful objects and enhancing the reliability of the boundary for tea plantations.Finally,the object-oriented random forest method was utilized to extract the tea information based on the optimal feature combination in the Jingmai Mountain,Yunnan Province.The resulting tea plantation map had high accuracy,with a 95.38%overall accuracy and 0.91 kappa coefficient.We conclude that the proposed method is effective for mapping tea plantations in high heterogeneity mountainous areas and has the potential for mapping tea plantations in large areas.

Detecting land subsidence near metro lines in the Baoshan district of Shanghai with multi-temporal interferometric synthetic aperture radar

Land subsidence is a major factor that affects metro line （ML） stability. In this study, an improved multi- temporal interferometric synthetic aperture radar （InSAR） （MTI） method to detect land subsidence near MLs is presented. In particular, our multi-temporal InSAR method provides surface subsidence measurements with high observation density. The MTI method tracks both point-like targets and distributed targets with temporal radar back- scattering steadiness. First, subsidence rates at the point targets with low-amplitude dispersion index （ADI） values are extracted by applying a least-squared estimator on an optimized freely connected network. Second, to reduce error propagation, the pixels with high-ADI values are classified into several groups according to ADI intervals and processed using a Pearson correlation coefficient and hierarchical analysis strategy to obtain the distributed targets. Then, nonlinear subsidence components at all point-like and distributed targets are estimated using phase unwrapping and spatiotemporal filtering on the phase residuals. The proposed MTI method was applied to detect land subsidence near MLs of No. 1 and 3 in the Baoshan district of Shanghai using 18 TerraSAR-X images acquired between April 21, 2008 and October 30, 2010. The results show that the mean subsidence rates of the stations distributed along the two MLs are -12.9 and -14.0 ram/year. Furthermore, three subsidence funnels near the MLs are discovered through the hierarchical analysis. The testing results demonstrate the satisfactory capacity of the proposed MTI method in providing detailed subsidence information near MLs.

Multi-Temporal Analysis of Land Subsidence in Toluca Valley (Mexico) through a Combination of Persistent Scatterer Interferometry (PSI) and Historical Piezometric Data

The Toluca Valley Aquifer (TVA) is considered one of the most overexploited aquifers in Mexico because of the high rate of groundwater extraction for supplying urban and industrial water to Mexico City and Toluca City, which causes land subsidence in urban and suburban areas. In this paper, we propose a multi-temporal analysis that uses persistent scatterer interferometry (PSI) method to evaluate the subsidence processes in Toluca Valley. The PSI results revealed differential movements of the ground of as much as 83 mm/year. A spatial variation of PSI results was identified with respect to previous studies using the conventional Din SAR methodology. The spatial distribution and density suggested the possibility of an expanding trend of subsidence process at north, northeast and east of the TVA, which corresponds to the region with the highest density of pumping wells for industrial and agricultural use.

Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression

Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC （grain protein content） estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.

Multi-temporal InSAR for Urban Deformation Monitoring:Progress and Challenges

Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. Much research has been carried out to apply MT-InSAR to monitor ground and infrastructure deformation in urban areas related to land reclamation, underground construction and groundwater extraction.This paper reviews the progress in the research and identifies challenges in applying the technology, including the inconsistency in coherent point identification when different approaches are used, the reliability issue in parameter estimation, difficulty in accurate geolocation of measured points, the one-dimensional line-of-sight nature of InSAR measurements, the inability of making complete measurements over an area due to geometric distortions, especially the shadowing effects, the challenges in processing large SAR datasets, the decrease of the number of coherent points with the increase of the length of SAR time series, and the difficulty in quality control of MT-InSAR results.

Research and application of real-time monitoring and early warning thresholds for multi-temporal agricultural products information

Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.

Characterization of land cover types in Xilin River Basin using multi-temporal Landsat images

This study conducted computer-aided image analysis of land use and land cover in Xilin River Basin, Inner Mongolia, using 4 sets of Landsat TM/ETM+ images acquired on July 31, 1987, August 11, 1991, September 27, 1997 and May 23, 2000, respectively. Primarily, 17 sub-class land cover types were recognized, including nine grassland types at community level: F.sibiricum steppe, S.baicalensis steppe, A.chinensis+ forbs steppe, A.chinensis+ bunchgrass steppe, A.chinensis+ Ar.frigida steppe, S.grandis+ A.chinensis steppe, S.grandis+ bunchgrass steppe, S.krylavii steppe, Ar.frigida steppe and eight non-grassland types: active cropland, harvested cropland, urban area, wetland, desertified land, saline and alkaline land, cloud, water body + cloud shadow. To eliminate the classification error existing among different sub-types of the same gross type, the 17 sub-class land cover types were grouped into five gross types: meadow grassland, temperate grassland, desert grassland, cropland and non-grassland. The overall classification accuracy of the five land cover types was 81.0% for 1987, 81.7% for 1991, 80.1% for 1997 and 78.2% for 2000.

Study of Forest Cover Change Dynamics between 2000 and 2015 in the Ikongo District of Madagascar Using Multi-Temporal Landsat Satellite Images

Satellite images are considered reliable data that preserve land cover information. In the field of remote sensing, these images allow relevant analyses of changes in space over time through the use of computer tools. In this study, we have applied the “discriminant” change detection algorithm. In this, we have verified its effectiveness in multi-temporal studies. Also, we have determined the change in forest dynamics in the Ikongo district of Madagascar between 2000 and 2015. During the treatments, we have used the Landsat TM satellite images for the years 2000, 2005 and 2010 as well as ETM+ for 2015. Thus, analyses carried out have allowed us to note that between 2000-2005, 1.4% of natural forest disappeared. And, between 2005-2010, forests degradation was 1.8%. Also, between 2010-2015, about 0.5% of the natural forest conserved in 2010 disappeared. Furthermore, we have found that the discriminant algorithm is considerably efficient in terms of monitoring the dynamics of forest cover change.

Contribution of MODIS NDVI 250 m Multi-Temporal Imagery Dataset for the Detection of Natural Forest Distribution of Java Island, Indonesia

As landmass of the world is covered by vegetation, taking into account phenology when performing land cover classification may yield more accurate maps. The availability of no-cost Moderate Resolution Imaging Spectrometer (MODIS) NDVI dataset that provides high-quality continuous time series data is representing a potentially significant source of land cover information especially for detection natural forest distribution. This study intends to assess the advantage of MODIS 250 m Normalized Difference Vegetation Index (NDVI) multi-temporal imagery for detection of densely vegetation cover distribution in Java and then for identification of remaining natural forest in Java from densely vegetation cover distribution. Result of this study successfully demonstrated the contribution of MODIS NDVI 250 m for detection the natural forest distribution in Java Island. Therefore, the approach described herein provided classification accuracy comparable to those of maps derived from higher resolution data and will be a viable alternative for regional or national classifications.

Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images

Constrained by complex imaging mechanism and extraordinary visual appearance,change detection with synthetic aperture radar(SAR)images has been a difficult research topic,especially in urban areas.Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information,there are still two problems to be solved in practical applications.First,change indicators constructed from incoherent feature only cannot characterize the change objects accurately.Second,the results of pixel-level methods are usually presented in the form of the noisy binary map,making the spatial change not intuitive and the temporal change of a single pixel meaningless.In this study,we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images.The coefficients of variation in timeseries incoherent features and the man-made object index(MOI)defined with coherent features are first combined to identify the initial change pixels.Afterwards,an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise(DBSCAN)and dynamic time warping(DTW),which can transform the initial results into noiseless object-level patches,and take the cluster center as a representative of the man-made object to determine the change pattern of each patch.An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.

Intensity-Dependent Dipole Phase in High-Order Harmonic Interferometry

High-order harmonics are ideal probes to resolve the attosecond dynamics of strong-field recollision processes.An easy-to-implement phase mask is utilized to covert the Gaussian beam to TEM01 transverse electromagnetic mode,allowing the realization of two-source interferometry of high-order harmonics.We experimentally measure the intensity dependence of dipole phase directly with high-order harmonic interferometry,in which the driving laser intensity can be precisely adjusted.The classical electron excursion simulations reproduce the experimental findings quite well,demonstrating that Coulomb potential plays subtle roles on movement of electrons for harmonics near the ionization threshold.This work is of great importance for precision measurements of ultrafast dynamics in strong-field physics.

Achieving 1.2 fm/Hz^(1/2)Displacement Sensitivity with Laser Interferometry in Two-Dimensional Nanomechanical Resonators:Pathways towards Quantum-Noise-Limited Measurement at Room Temperature

Laser interferometry is an important technique for ultrasensitive detection of motion and displacement.We push the limit of laser interferometry through noise optimization and device engineering.The contribution of noises other than shot noise is reduced from 92.6%to 62.4%,demonstrating the possibility towards shotnoise-limited measurement.Using noise thermometry,we quantify the laser heating effect and determine the range of laser power values for room-temperature measurements.With detailed analysis and optimization of signal transduction,we achieve 1.2 fm/Hz^(1/2)displacement measurement sensitivity at room temperature in twodimensional(2D)Ca Nb_(2)O_(6)nanomechanical resonators,the best value reported to date among all resonators based on 2D materials.Our work demonstrates a possible pathway towards quantum-noise-limited measurement at room temperature.

Wide-spectrum optical synthetic aperture imaging via spatial intensity interferometry

High resolution imaging is achieved using increasingly larger apertures and successively shorter wavelengths.Optical aperture synthesis is an important high-resolution imaging technology used in astronomy.Conventional long baseline amplitude interferometry is susceptible to uncontrollable phase fluctuations,and the technical difficulty increases rapidly as the wavelength decreases.The intensity interferometry inspired by HBT experiment is essentially insensitive to phase fluctuations,but suffers from a narrow spectral bandwidth which results in a lack of effective photons.In this study,we propose optical synthetic aperture imaging based on spatial intensity interferometry.This not only realizes diffraction-limited optical aperture synthesis in a single shot,but also enables imaging with a wide spectral bandwidth,which greatly improves the optical energy efficiency of intensity interferometry.And this method is insensitive to the optical path difference between the sub-apertures.Simulations and experiments present optical aperture synthesis diffraction-limited imaging through spatial intensity interferometry in a 100 nm spectral width of visible light,whose maximum optical path difference between the sub-apertures reaches 69λ.This technique is expected to provide a solution for optical aperture synthesis over kilometer-long baselines at optical wavelengths.

Measurement of Shear Stress Acting on Flat Plate Using Oil Film Interferometry

Measurements of frictional resistance play an important role in engineering practice. There are several types of air resistance acting on an aircraft, for example. One of them, frictional resistance, accounts for half of the air resistance. Oil film interferometry is one of methods for measuring the frictional resistance. Oil dropped on an object is thinly stretched by the frictional resistance. The bright and dark fringe pattern is generated when monochromatic light is applied to the oil film. The gradient of the oil thickness decreases with the lapse of time, and thus the spacing between neighboring the dark lines increases. The rate at which the spacing increases is proportional to the frictional resistance. In this study, the frictional resistance acting on a small area on a plate was measured and compared with the theoretical value. As a result, these results qualitatively agree well with each other.

利用InSAR形变反演滑坡滑面运动特征——以汶川县布瓦村滑坡为例

当前滑坡滑面运动特征获取多采用接触式手段,费时费力、成本高昂,且仅能获取稀疏点位滑面运动状态。因此,本文提出一种非接触式滑坡滑面运动特征获取方法,其基本思路是:基于位错理论建立滑坡表面形变与滑面运动间的关系模型,同时引入滑面运动平滑约束条件,最终以InSAR等技术获取的地表形变为约束,反演获得滑面运动特征。本文选择四川省汶川县布瓦村滑坡作为研究对象,以时序InSAR技术获取的滑坡坡面升降轨形变数据为约束,基于发展的理论方法反演获得布瓦村滑坡滑面几何与运动学参数,并结合野外勘查、Lidar观测以及模型残差较好地验证了反演结果的可靠性,其中反演升降轨模型残差仅3.1 mm·a^(-1)和3.4 mm·a^(-1)。反演结果显示,布瓦村滑坡滑面位于坡表以下平均~15.0 m深度处,滑面整体以沿坡向运动为主,最大运动量级达~80 mm·a^(-1),同时在坡体西侧中下部发现微量的垂直坡向运动。本文提出方法可方便、快速的获取空间连续的滑坡滑面运动特征,为滑坡灾害预警和失稳风险评估提供更为直接可靠的科学参考。

大视场白光干涉测量系统及性能研究

基于国产化的2倍迈克尔逊型干涉物镜组,优选配置0.5倍适配镜,对白光LED照明光源进行带通滤波参数的仿真估算和实验性能比较,构建了整套大视场白光干涉精密测量装置系统并进行了实验测试,通过白光干涉轴向响应实验曲线确定了中心波长。实验结果表明:通过光谱滤波获得了较为理想的白光干涉轴向响应曲线;系统的水平最大视场达到了14 mm;高度为2.04μm和20.43μm的标准台阶样品的测量结果分别为2.05μm和20.47μm,10次测量重复精度(标准差)分别为12 nm和16 nm。对粗糙度样板、微机电系统传感结构和半导体晶圆膜层进行了实测,表明所研制的系统装置在三维光学无损精密检测领域的应用具有可行性。

联合SAR影像相位和幅度信息的多形变量级滑坡监测——以金沙江白格滑坡为例

近年来,雷达遥感被广泛用于提取滑坡表面的高精度形变信息,所用技术包括基于相位的干涉测量和基于幅度的像素偏移量追踪;然而,大型复杂滑坡的形变量在时间演化以及空间分布上都存在较大差异,单一雷达遥感手段难以实现滑坡形变的全面监测。该文在雷达遥感形变探测能力分析的基础上,提出联合SAR(synthetic aperture Radar)影像的相位和幅度信息进行滑坡的全过程监测。以2018年发生的金沙江白格滑坡为例,基于2014—2021年Sentinel-1数据和2014—2018年ALOS-2数据,联合时序InSAR(interferometric SAR)分析和时序像素偏移量追踪(pixel offset tracking,POT),获取滑坡灾前灾后时序形变。结果显示白格滑坡灾前后缘的年平均速率在20 mm/a,主滑坡区2014年12月—2018年7月形变量可达45 m左右;灾后滑坡逐渐向后缘扩张,年平均形变速率可达200 mm/a,已经威胁到部分民房。2种方法的联合可以实现大型复杂滑坡的时空维多形变量级提取。

高洁净度超光滑非球面的自动化干涉检测方法

为了实现应用于高能激光等领域对表面洁净度有着极高要求的超光滑非球面检测,排除检测人员带来的洁净度与空气扰动的影响,研究了高洁净度超光滑非球面的自动化干涉检测方法。通过建立补偿干涉法非球面失调量与波前像差之间的灵敏度矩阵,实现利用波前像差求解被测非球面失调量。以理想干涉系统的离焦、彗差与像散为优化目标,进行反馈控制,实现被测非球面的自动化调整,进而实现高洁净度超光滑非球面的自动化干涉检测。实验结果表明,在干涉图可测范围内,利用灵敏度矩阵通过几步迭代即可实现非球面失调量的收敛。结合Stewart六自由度调整台,分别实现2μm精度的平移误差调整、2"精度的光轴自动化对准,最终完成被测非球面的精密调整,实现高洁净度超光滑非球面的自动化干涉检测。采用灵敏度矩阵与六自由度调整的非球面自动化干涉检测方法可实现被测非球面失调量的快速求解与自动调整,降低人员和环境带来的扰动影响,提高了非球面的检测速度,并实现了高洁净度超光滑非球面的自动化干涉检测。

激光回馈精密测量技术及应用

介绍了激光回馈干涉的理论模型,阐述了激光回馈测量灵敏度高、装置结构简单、可自准直、能够对非合作目标进行精密测量的技术特点。分析了激光回馈测量相较传统干涉测量的优势,探讨了激光回馈测量技术在工业和科研领域中的应用方向,包括位移、角度、振动、绝对距离测量等,指出利用频率复用、偏振复用、(准)共路等技术可提高回馈测量的实际应用性能。最后展望了激光回馈技术的发展前景,为推动回馈技术的广泛应用提供借鉴。