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Urban Agglomeration (UA) is regarded as an emerging complex urban system in China. The development of UA demands a reasonable scale structure, which can be investigated by Zipf's law. However, few studies have been conducted to quantify the optimal scale of UA and how its development deviates from the optimal scale. With the continuous urban expansion, the problem of UAs' scale structure has received increasing attention. In this study, we propose a method based on Zipf's law for estimating the theoretical optimal scale of UAs in China and assessing the deviation rate from their optimal scales. Twelve typical UAs in China are selected, and their development is assessed via urban impervious surface data from 2000 to 2018. The results show that the average deviation rate of the investigated UAs decreased from 3.40% in 2000 to 2.32% in 2018, demonstrating that these UAs are on a positive evolution trajectory. Furthermore, according to the development stage, we make recommendations on "large cities vs. medium/small-sized cities and promoting vs. restraining" to each UA based on its size. The conceptual and analytical knowledge, as well as the results from this study, are expected to offer valuable insights and new references for regulating and managing UAs' development in China. [ABSTRACT FROM AUTHOR]

A precise forecast of rice yields at the plot scale is essential for both food security and precision agriculture. In this work, we developed a novel technique to integrate UAV-based vegetation indices (VIs) with brightness, greenness, and moisture information obtained via tasseled cap transformation (TCT) to improve the precision of rice-yield estimates and eliminate saturation. Eight nitrogen gradients of rice were cultivated to acquire measurements on the ground, as well as six-band UAV images during the booting and heading periods. Several plot-level VIs were then computed based on the canopy reflectance derived from the UAV images. Meanwhile, the TCT-based retrieval of the plot brightness (B), greenness (G), and a third component (T) indicating the state of the rice growing and environmental information, was performed. The findings indicate that ground measurements are solely applicable to estimating rice yields at the booting stage. Furthermore, the VIs in conjunction with the TCT parameters exhibited a greater ability to predict the rice yields than the VIs alone. The final simulation models showed the highest accuracy at the booting stage, but with varying degrees of saturation. The yield-prediction models at the heading stage satisfied the requirement of high precision, without any obvious saturation phenomenon. The product of the VIs and the difference between the T and G (T − G) and the quotient of the T and B (T/B) was the optimum parameter for predicting the rice yield at the heading stage, with an estimation error below 7%. This study offers a guide and reference for rice-yield estimation and precision agriculture. [ABSTRACT FROM AUTHOR]

As an important carrier of human productive activities, the extraction of buildings is not only essential for urban dynamic monitoring but also necessary for suburban construction inspection. Nowadays, accurate building extraction from remote sensing images remains a challenge due to the complex background and diverse appearances of buildings. The convolutional neural network (CNN)-based building extraction methods, although increased the accuracy significantly, are criticized for their inability for modeling global dependencies. Thus, this article applies the vision transformer (ViT) for building extraction. However, the actual utilization of the ViT often comes with two limitations. First, the ViT requires more GPU memory and computational costs compared with CNNs. This limitation is further magnified when encountering large-sized inputs like fine-resolution remote sensing images. Second, spatial details are not sufficiently preserved during the feature extraction of the ViT, resulting in the inability for fine-grained building segmentation. To handle these issues, we propose a novel ViT (BuildFormer), with a dual-path structure. Specifically, we design a spatial-detailed context path to encode rich spatial details and a global context path to capture global dependencies. Besides, we design a window-based linear multihead self-attention whose complexity is linear with the window size. Such a design allows the BuildFormer to apply large windows for capturing global context, which greatly improves its potential in processing large-sized remote sensing images. The proposed method yields the state-of-the-art performance (75.74% IoU) on the Massachusetts building dataset. Code will be available at https://github.com/WangLibo1995/BuildFormer. [ABSTRACT FROM AUTHOR]

Urban–rural land transition and the coordination of coupled human–environmental systems are two important issues in the process of global urban–rural development. Although existing studies have explored the coupling coordination degree (CCD) of human–environmental interactions under the context of urbanization, few studies have taken land transitions into consideration. In this study, we investigated the dynamics of CCD in China from 2001 to 2018 using multisource remote sensing data and quantified the CCD changes in land transitions among urban construction land (UCL), rural residential land (RRL), and non-construction land (NCL). The CCD alterations mainly occurred in the decline in NCL stock, the increase in UCL stock, and especially the losses during RRL to NCL transfers. We urge academics and government decision-makers to pay more attention to the CCD transfers and losses during urban–rural transitions. This study provides scientific guidance for the development of urban–rural integration and is expected to assist the coordinated evaluation of human–environmental interactions in the process of sustainable development. [ABSTRACT FROM AUTHOR]

SAR parameters have been used many times to characterize crops. However, it was found that the scattering mechanisms derived from quad-pol SAR were different from that of the dual-pol SAR data. The objective of this study is to better understand the scattering mechanism of the maize field to dual-pol (VV, VH) SAR data and propose a new radar vegetation index to improve the inversion accuracy of the crop height. We first analyzed the temporal variation of SAR parameters before the maize height stopped increasing. It showed that the sensitivity of SAR parameters to the plant height was strong in the early stage, but the correlation became weak later. Then, according to the scattering mechanism of the maize field to the dual pol (VV, VH) SAR data, we combined use of the backscattering coefficient of σ0VH and the depolarization parameter proposed the mPRVI index. In this paper, the simple linear regression model was used to fit the six parameters separately to estimation the plant height, including σ0VH (R = 0.84), VIRatio (R = 0.82), RVI (R = 0.83), DOD+RVI (R = 0.85), PRVI (R = 0.86) and mPRVI (R = 0.88), which showed a relatively high Pearson correlation coefficient (R) with the plant height. According to adjusted coefficient of determination R2 (0.70, 0.70, 0.71, 0.74, 0.76, 0.80) and RMSE (62.8 cm, 62.7 cm, 61.8 cm, 57.5 cm, 56.3 cm, 51.2 cm) for σ0VH, VIRatio, RVI, DOD+RVI, PRVI, mPRVI, respectively, the mPRVI index outperforms other parameters throughout the maize growing season. And this outperformance is not only manifested in the early growth stage of the maize, but also in the later stage before the plant height stopped increasing. [ABSTRACT FROM AUTHOR]

Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC (r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination (R 2) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype. [ABSTRACT FROM AUTHOR]

The accurate assessment of rice yield is crucially important for China's food security and sustainable development. Remote sensing (RS), as an emerging technology, is expected to be useful for rice yield estimation especially at regional scales. With the development of unmanned aerial vehicles (UAVs), a novel approach for RS has been provided, and it is possible to acquire high spatio-temporal resolution imagery on a regional scale. Previous reports have shown that the predictive ability of vegetation index (VI) decreased under the influence of panicle emergence during the later stages of rice growth. In this study, a new approach which integrated UAV-based VI and abundance information obtained from spectral mixture analysis (SMA) was established to improve the estimation accuracy of rice yield at heading stage. The six-band image of all studied rice plots was collected by a camera system mounted on an UAV at booting stage and heading stage respectively. And the corresponding ground measured data was also acquired at the same time. The relationship of several widely-used VIs and Rice Yield was tested at these two stages and a relatively weaker correlation between VI and yield was found at heading stage. In order to improve the estimation accuracy of rice yield at heading stage, the plot-level abundance of panicle, leaf and soil, indicating the fraction of different components within the plot, was derived from SMA on the six-band image and in situ endmember spectra collected for different components. The results showed that VI incorporated with abundance information exhibited a better predictive ability for yield than VI alone. And the product of VI and the difference of leaf abundance and panicle abundance was the most accurate index to reliably estimate yield for rice under different nitrogen treatments at heading stage with the coefficient of determination reaching 0.6 and estimation error below 10%. [ABSTRACT FROM AUTHOR]

The dry aboveground biomass (AGB) is an important parameter in assessing crop growth and predicting yield. This study aims to ascertain the optimal methods for the spectroscopic estimation of winter oilseed rape (WOR) biomass. The different fertilizer-N gradients WOR were planted to collect biomass data and canopy hyperspectral data in two years of field experiments. Correlation analyses and partial least squares regression (PLSR) were performed between canopy hyperspectral data and AGB, and the linear and non-linear regression models simulated the quantitative relation between the vegetation indices (VIs) and AGB at four different growth stages (seeding, bolting, flowering, and pod stage). The results indicated that VIs that were derived from canopy hyperspectral data could estimate AGB accurately: (1) At the seeding and bolting stage, the CIred edge showed excellent performance with the higher accuracy (R2 ranged from 0.60–0.95) as compared to the other six VIs (Green chlorophyll index (CIgreen), normalized difference vegetation index (NDVI), Green normalized difference vegetation index (GNDVI), ratio vegetation index (RVI), DVI, and soil adjusted vegetation index (SAVI)); (2) Correlation analyses and PLSR can effectively extract the feature wavelengths (800 nm and 1200 nm) for biomass estimation. The modified vegetation indices NDVI (800, 1200) significantly improved AGB estimation accuracy (R2 > 0.80, RMSE < 1530 kg/hm2, RPD > 2.3) without saturation phenomenon at the total for four stages, and retained good robustness and reduced the influence of flower and pod for estimating AGB; (3) it was vital to pay more attention to the near-infrared (NIR) bands that could represent WOR growth phenology, and selecting suitable VIs and modeling algorithms could also have a relatively large effect on the success of AGB estimation. The overall results indicated that WOR AGB could be reliably estimated by canopy hyperspectral data, although the plant architecture and coverage of WOR were significantly different during its entire growing period. [ABSTRACT FROM AUTHOR]

Aerosols greatly influence global and regional atmospheric systems, and human life. However, a comprehensive understanding of the source regions and three-dimensional (3D) characteristics of aerosol transport over central China is yet to be achieved. Thus, we investigate the 3D macroscopic, optical, physical, and transport properties of the aerosols over central China based on the March 2007 to February 2016 data obtained from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Our results showed that approximately 60% of the aerosols distributed over central China originated from local areas, whereas non-locally produced aerosols constituted approximately 40%. Anthropogenic aerosols constituted the majority of the aerosol pollutants (69%) that mainly distributed less than 2.0 km above mean sea level. Natural aerosols, which are mainly composed of dust, accounted for 31% of the total aerosols, and usually existed at an altitude higher than that of anthropogenic aerosols. Aerosol particles distributed in the near surface were smaller and more spherical than those distributed above 2.0 km. Aerosol optical depth (AOD) and the particulate depolarization ratio displayed decreasing trends, with a total decrease of 0.11 and 0.016 from March 2007 to February 2016, respectively. These phenomena indicate that during the study period, the extinction properties of aerosols decreased, and the degree of sphericity in aerosol particles increased. Moreover, the annual anthropogenic and natural AOD demonstrated decreasing trends, with a total decrease of 0.07 and 0.04, respectively. This study may benefit the evaluation of the effects of the 3D properties of aerosols on regional climates. [ABSTRACT FROM AUTHOR]

Very high-resolution satellite image matching is a challenging task due to local distortion, repetitive structures, intensity changes and low efficiency. In this letter, a novel approach based on the block and octave constraint scale-invariant feature transform (SIFT) is proposed for very high-resolution satellite image matching. Details regarding multi-thread processing are provided. First, the corresponding matching block is observed using rational function model (RFM) prediction. The RFM prediction can provide a geometric constraint. In each block matching, keypoints are classified with the feature octave. The keypoints are matched in the group with the same feature octave. The block and octave constraint can narrow the keypoints search scope and be helpful for acquiring additional matches. Since blocks are independent, each corresponding block matching is parallelized to realize multi-thread processing with open multi-processing (OpenMP). Experiments are designed to test the matching performance and runtime. The results indicate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM PUBLISHER]

In-orbit calibration of satellite optical sensors is of crucial importance for improving the accuracy and stability of high-resolution satellite stereo mapping and positioning. For the in-orbit calibration of Ziyuan 3 (ZY-3), the first Chinese three-line stereo mapping satellite, a rigorous imaging geometric model of the three-line sensors is established and the internal systematic error is modelled during analysis of the satellite structure and charge-coupled device (CCD) array. To improve the accuracy and stability of the in-orbit calibration, a new trajectory model, piece-point with weight polynomial model, is used and compared with models in current use. In addition, an attitude constant error model is constructed and applied to reduce attitude errors and provide a more accurate initial attitude value, thus increasing the accuracy and stability of the in-orbit calibration. In addition, ground control point (GCP) stripes, established by partitioning GCPs into stripes, are proposed and used to eliminate random errors during in-orbit calibration. Finally, in-orbit calibration is performed for single and multiple sensors and the result of the calibration is assessed using GCPs and checkpoints. Based on the calibration result, variations of the three-line CCD array on ZY-3 are calculated and analysed in the along- and cross-track directions. [ABSTRACT FROM AUTHOR]

In this letter, a hierarchical hybrid decision tree (HDT) framework based on the complementary properties and processing chains is proposed. Unlike the previous HDT framework by multiple features and multiple classifiers (MFMC), the proposed method exploits the complementary score (CS) and the feature selection tools to optimize the processing chains for each node. Consequently, adaptive features are employed to identify each class. The HDT structure is then employed to predict all the pixels after the identification rules for all the nodes are determined. The proposed method and the traditional HDT are tested on the common hyperspectral data. The experimental results demonstrate the effectiveness of our method. It is shown that the overall performance of HDT is improved by optimizing the identification of each node. [ABSTRACT FROM AUTHOR]

A planet centroid is an important observable object in autonomous optical navigation. A high-accuracy algorithm is presented to extract the planet centroid from its raw image. First, we proposed a planet segmentation algorithm to segment the planet image block to eliminate noise and to reduce the computation load. Second, we developed an effective algorithm based on Prewitt-Zernike moments to detect sub-pixel real edges by determining possible edges with the Prewitt operator, removing pseudo-edges in backlit shady areas, and relocating real edges to a sub-pixel accuracy in the Zernike moments. Third, we proposed an elliptical model to fit sub-pixel edge points. Finally, we verified the performance of this algorithm against real images from the Cassini-Huygens mission and against synthetic simulated images. Simulation results showed that the accuracy of the planet centroid is up to 0·3 pixels and that of the line-of-sight vector is at 2·1 × 10−5 rad. [ABSTRACT FROM AUTHOR]

• Gray intensity descent and local gradient watershed features of infrared small target are analyzed. • A non-window structure filter is designed to enhance the target and suppress the background. • The proposed non-window filter outperforms other state-of-the-art methods. Robust infrared (IR) small target detection is an essential part of infrared search and tracking systems. Existing IR target detection methods based on human visual system usually use statistical features of rectangular region to measure the difference between target and background. In the case that the dim target size is smaller than the rectangular window cell size, since the window cell contains both the target and the background pixels, the statistical features cannot well represent the target contrast. The use of a rectangular window structure for calculation reduces the statistical difference between the target and the background, that is, reduces the saliency of the target, resulting in a slow rise in the ROC curve of the HVS method. This paper proposes a novel non-window structure filter for small target detection. Firstly, we obtain candidate target points by combining the high-frequency information of the image with the local maximum points, and at the same time, calculate the Sobel gradient map to prepare for subsequent processing. Then, a filter is designed based on gray intensity descent and local gradient watershed (GID-LGW) characteristics of the target, and it is used to calculate the contrast of each candidate target point. Finally, an adaptive threshold operation is applied to extract the target. Experiments show that compared with several baseline methods, the proposed method has a better detection rate, lower false alarm, and high speed, especially for small and weak targets with a size smaller than 3 × 2. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R2) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth. [ABSTRACT FROM AUTHOR]

• Different cultivars affect the rice yield estimation with UAV data. • Multi-temporal VIs can be used to estimate rice yield of different cultivars. • Climate change affect the rice yield estimation by using multi-temporal VIs. Timely and accurate estimation of grain yield is valuable for crop monitoring and breeding, and plays an important role in precision agriculture. In this study, we developed a method to predict grain yield based entirely on unmanned aerial vehicle (UAV) data in different rice cultivars under two contrasting climatic regions. Vegetation indices (VIs), which were derived from canopy reflectance collected by UAV, were used to correlate with rice phenotyping and estimate grain yield. It is found that the two-band enhanced vegetation index (EVI2) closely related to leaf area index (LAI) as well as canopy chlorophyll content (CCC), and the red edge chlorophyll index (CI rededge) related to above ground biomass (AGB). Thus, the phenotyping-related VIs – EVI2 and CI rededge were exploited to develop yield estimation model. Results showed that the single stage VIs weakly correlated with grain yield of different rice cultivars and was not able to estimate grain yield. By contrast, the multi-temporal VIs can be used to estimate grain yield in different rice cultivars with the estimation error below 7.1 %. In addition, the rice growth duration differed in different climatic zones, which may decrease the estimation accuracy of grain yield by using multi-temporal VIs. After adjusting the phenological stage of multi-temporal VIs used in estimation model, the estimation accuracy of grain yield in different climatic zones increased. In conclusion, this study demonstrated that the UAV-based multi-temporal VIs were reliable for grain yield estimation in different rice cultivars under contrasting climatic zones. [ABSTRACT FROM AUTHOR]

The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs and background especially at heading stage. The spectral mixture analysis (SMA) can quantitatively obtain the abundance information and mitigate the impacts. Furthermore, according to the spectral variability and information complexity caused by the rice cropping system and canopy characteristics of reflection and scattering, in this study, the multi-endmember extraction by the pure pixel index (PPI) and the nonlinear unmixing method based on the bandwise generalized bilinear mixing model (NU-BGBM) were applied for SMA, and the VIE (VIs recalculated from endmember spectra) was integrated with abundance data to establish the yield estimation model at heading stage. In two paddy fields of different cultivation settings, multispectral images were collected by an unmanned aerial vehicle (UAV) at booting and heading stage. The correlation of several widely-used VIs and rice yield was tested and weaker at heading stage. In order to improve the yield estimation accuracy of rice at heading stage, the VIE and foreground abundances from SMA were combined to develop a linear yield estimation model. The results showed that VIE incorporated with abundances exhibited a better estimation ability than VI alone or the product of VI and abundances. In addition, when the structural difference of plants was obvious, the addition of the product of VIF (VIs recalculated from bilinear endmember spectra) and the corresponding bilinear abundances to the original product of VIE and abundances, enhanced model reliability. VIs using the near-infrared bands improved more significantly with the estimation error below 8.1%. This study verified the validation of the targeted SMA strategy while estimating crop yield by remotely sensed VI, especially for objects with obvious different spectra and complex structures. [ABSTRACT FROM AUTHOR]

Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture. [ABSTRACT FROM AUTHOR]

Urbanization is changing the world's surface pattern more and more drastically, which brings many social and ecological problems. Quantifying the changes in the landscape pattern and 3D structure of the city is important to understand these issues. This research study used Melbourne, a compact city, as a case study, and focused on landscape patterns and vertical urban volume (volume mean (VM), volume standard deviation (VSD)) and investigate the correlation between them from the scope of different scales and functions by Remote Sensing (RS) and Geographic Information System (GIS) techniques. We found: (1) From 2000 to 2012, the landscape pattern had a trend of decreasing fragmentation and increasing patch aggregation. The growth of VM and VSD was more severe than that of landscape metrics, and presented a "high–low" situation from the city center to the surroundings, maintaining the structure of "large east and small west". (2) Landscape pattern was found closely associated with the urban volume. In the entire study area, landscape pattern patches with low fragmentation and high aggregation were directly proportional to VM with high value, which represented high urbanization, and patches with high connectivity and fragmentation had a positive relationship with high VSD, which represented strong spatial recognition. (3) The urban volumes of different urban functional areas were affected by different landscape patterns, and the analysis based on the local development situation can explain the internal mechanism of the interaction between the landscape pattern and the urban volume. [ABSTRACT FROM AUTHOR]

Accurate monitoring of hybrid rice phenology (RP) is crucial for breeding rice cultivars and controlling fertilizing amount. The aim of this study is to monitor the exact date of hybrid rice initial heading stage (IHSDAS) based on low-altitude remote sensing data and analyze the influence factors of RP. In this study, six field experiments were conducted in Ezhou city and Lingshui city from 2016 to 2019, which involved different rice cultivars and nitrogen rates. Three low-altitude remote sensing platforms were used to collect rice canopy reflectance. Firstly, we compared the performance of normalized difference vegetation index (NDVI) and red edge chlorophyll index (CIred edge) for monitoring RP. Secondly, double logistic function (DLF), asymmetric gauss function (AGF), and symmetric gauss function (SGF) were used to fit time-series CIred edge for acquiring phenological curves (PC), the feature: maximum curvature (MC) of PC was extracted to monitor IHSDAS. Finally, we analyzed the influence of rice cultivars, N rates, and air temperature on RP. The results indicated that CIred edge was more appropriate than NDVI for monitoring RP without saturation problem. Compared with DLF and AGF, SGF could fit CIred edge without over fitting problem. MC of SGF_CIred edge from all three platforms showed good performance in monitoring IHSDAS with good robustness, R2 varied between 0.82 and 0.95, RMSE ranged from 2.31 to 3.81. In addition, the results demonstrated that high air temperature might cause a decrease of IHSDAS, and the growth process of rice was delayed when more nitrogen fertilizer was applied before IHSDAS. This study illustrated that low-altitude remote sensing technology could be used for monitoring field-scale hybrid rice IHSDAS accurately. [ABSTRACT FROM AUTHOR]

The dimensions of phenotyping parameters such as the thickness of rice play an important role in rice quality assessment and phenotyping research. The objective of this study was to propose an automatic method for extracting rice thickness. This method was based on the principle of binocular stereovision but avoiding the problem that it was difficult to directly match the corresponding points for 3D reconstruction due to the lack of texture of rice. Firstly, the shape features of edge, instead of texture, was used to match the corresponding points of the rice edge. Secondly, the height of the rice edge was obtained by way of space intersection. Finally, the thickness of rice was extracted based on the assumption that the average height of the edges of multiple rice is half of the thickness of rice. According to the results of the experiments on six kinds of rice or grain, errors of thickness extraction were no more than the upper limit of 0.1 mm specified in the national industry standard. The results proved that edge features could be used to extract rice thickness and validated the effectiveness of the thickness extraction algorithm we proposed, which provided technical support for the extraction of phenotyping parameters for crop researchers. [ABSTRACT FROM AUTHOR]

In recent years, the acquisition of high-resolution multi-spectral images by unmanned aerial vehicles (UAV) for quantitative remote sensing research has attracted more and more attention, and radiometric calibration is the premise and key to the quantification of remote sensing information. The traditional empirical linear method independently calibrates each channel, ignoring the correlation between spectral bands. However, the correlation between spectral bands is very valuable information, which becomes more prominent as the number of spectral channels increases. Based on the empirical linear method, this paper introduces the constraint condition of spectral angle, and makes full use of the information of each band for radiometric calibration. The results show that, compared with the empirical linear method, the proposed method can effectively improve the accuracy of radiometric calibration, with the improvement range of Mean Relative Percent Error (MRPE) being more than 3% in the range of visible band and within 1% in the range of near-infrared band. Besides, the method has great advantages in agricultural remote sensing quantitative inversion. [ABSTRACT FROM AUTHOR]