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Vegetation indexes (VIs) are a key variable for monitoring the crop growth and estimating crop productivity. The spectral traits of the tassels were significantly different from those of the canopy leaves, which had an effect on the accuracy of using vegetation indices of canopy features to monitor crop growth. Advances in deep learning in computer vision provide opportunities for rapid and nondestructive tassels removal. The objective of this study was to remove tassels from images using a rapid and non-destructive method and to analyze and quantify the effect of tassels on canopy spectral information. This study proposed the color space transformation to divide tassels images into different growth stages and analyzed the performance of three convolutional neural networks (U-Net, PSPNet and DeeplabV3+) for tassel segmentation, and the analysis and quantification of the effect of tassels on canopy spectral information by calculating VIs and contributions. The results showed that the HSV (Hue, Saturation, Value) color space transformation divided images into three growth stages with robustness in the field environment. The U-Net model had the best segmentation precision (PA = 82.14%, MIoU = 84.43%). Moreover, the tassels significantly reduce canopy reflectance in the green wavelength range and the contribution of tassels to each of the VIs was different. In addition, the contribution of tassels to VIs is dynamic in different stages of tassels growth. This study provides a fast and non-destructive approach to quantify the effect of tassels on canopy spectral information in the field environment.

According to a bodily view of pain, pains are objects which are located in body parts. This bodily view is supported by the locative locutions for pain in English, such as that “I have a pain in my back.” Recently, Liu and Klein (Analysis, 80(2), 262–272, 2020) carry out a cross-linguistic analysis, and they claim that (1) Mandarin has no locative locutions for pain and (2) the absence of locative locutions for pain puts the bodily view at risk. This paper rejects both claims. Regarding the philosophical claim, I argue that a language without locative locutions for pain only poses a limited challenge to the bodily view. Regarding the empirical claim, I identify the possible factors which might have misled Liu and Klein about the locative locutions for pain in Mandarin, and argue that Mandarin has a wide range of locative locutions for pain by conducting a corpus analysis. I conclude that compared to English, Mandarin lends no less, if not more, support to the bodily view of pain.

As a unique natural satellite of the Earth, the Moon has always attracted much attention, and one subject of interest is the establishment of an Earth observation platform on the Moon. Compared with the existing spaceborne and airborne observation platforms, a Moon-based Earth observation platform would have the unique advantage of the ability to obtain global and large-scale observation data. At present, the study on Moon-based Earth observations is in theory, so it is necessary to use simulation methods to understand the observation performance of a Moon-based Earth observation platform. In this study, a method for Moon-based Earth observation imaging simulation in the thermal infrared band is developed to study the Moon-based thermal infrared imaging process. The method comprises three parts, including the estimation of Moon-based imaging coverage, the acquisition of the radiation intensity at the entrance pupil, and the simulated image output from the Moon-based thermal infrared sensor. Then, the simulated results are validated with the existing spaceborne observation data. Results show that the absolute error of Moon-based thermal infrared simulations is between 1.3–5.7 K, the RMSE is between 1.38 – 3.12 K, and the relative error is between 0.44% – 2.12%, indicating that the thermal infrared imaging model can more realistically simulate the true conditions of ground surface and that a Moon-based Earth observation platform could observe the Earth in the thermal infrared band.

Measuring leaf area index (LAI) is essential for evaluating crop growth and estimating yield, thereby facilitating high-throughput phenotyping of maize (Zea mays). LAI estimation models use multi-source data from unmanned aerial vehicles (UAVs), but using multimodal data to estimate maize LAI, and the effect of tassels and soil background, remain understudied. Our research aims to (1) determine how multimodal data contribute to LAI and propose a framework for estimating LAI based on remote-sensing data, (2) evaluate the robustness and adaptability of an LAI estimation model that uses multimodal data fusion and deep neural networks (DNNs) in single- and whole growth stages, and (3) explore how soil background and maize tasseling affect LAI estimation. To construct multimodal datasets, our UAV collected red–green–blue, multispectral, and thermal infrared images. We then developed partial least square regression (PLSR), support vector regression, and random forest regression models to estimate LAI. We also developed a deep learning model with three hidden layers. This multimodal data structure accurately estimated maize LAI. The DNN model provided the best estimate (coefficient of determination [R2] = 0.89, relative root mean square error [rRMSE] = 12.92%) for a single growth period, and the PLSR model provided the best estimate (R2 = 0.70, rRMSE = 12.78%) for a whole growth period. Tassels reduced the accuracy of LAI estimation, but the soil background provided additional image feature information, improving accuracy. These results indicate that multimodal data fusion using low-cost UAVs and DNNs can accurately and reliably estimate LAI for crops, which is valuable for high-throughput phenotyping and high-spatial precision farmland management.

Global change affected by multiple factors, the consequences of which continue to be far-reaching, has the characteristics of large spatial scale and long-time scale. The demand for Earth observation technology has been increasing for large-scale simultaneous observations and stable global observation over the long-term. A Moon-based observation platform, which uses sensors on the nearside lunar surface, is considered a reasonable solution. However, owing to a lack of appropriate processing methods for optical sensor data, global change study using this platform is not sufficient. This paper proposes two optical sensor imaging processing methods for the Moon-based platform: area imaging processing method (AIPM) and global imaging processing method (GIPM), primarily considering global change characteristics, optical sensor performance, and motion law of the Moon-based platform. First, the study proposes a simulation theory which includes the construction of a Moon-Sun elevation angle model and a global image mosaicking method. Then, coverage images of both image processing methods are simulated, and their features are quantitatively analyzed. Finally, potential applications are discussed. Results show that AIPM, whose coverage is mainly affected by lunar revolution, is approximately between 0% and 50% with a period of 29.5 days, which can help the study of large-scale instant change phenomena. GIPM, whose coverage is affected by Earth revolution, is conducive to the study of long term global-scale phenomena because of its sustained stable observation from 67°N-67°S on the Earth. AIPM and GIPM have great advantages in Earth observation of tripolar regions. The existence of top of the atmosphere (TOA) albedo balance line is verified from the GIPM perspective. These two imaging methods play a significant role in linking observations acquired from the Moon-based platform to Earth large-scale geoscience phenomena, and thus lay a foundation for using this platform to capture global environmental changes and new discoveries.

The land surface temperature (LST) is a key parameter for the Earth’s energy balance. As a natural satellite of the Earth, the orbital of the moon differs from that of current Earth observation satellites. It is a new way to measure the land surface temperature from the moon and has many advantages compared with artificial satellites. In this paper, we present a new method for simulating the LST measured by moon-based Earth observations. Firstly, a modified land-surface diurnal temperature cycle (DTC) method is applied to obtain the global LST at the same coordinated universal time (UTC) using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The lunar elevation angles calculated using the ephemeris data (DE405) from the Jet Propulsion Laboratory (JPL) were then applied to simulate the Earth coverage observed from the moon. At the same time, the modified DTC model was validated using in situ data, MODIS LST products, and the FengYun-2F (FY-2F) LST, respectively. The results show that the fitting accuracy (root-mean-square error, RMSE) of the modified DTC model is not greater than 0.72 °C for eight in situ stations with different land cover types, and the maximum fitting RMSE of the modified model is smaller than that of current DTC models. By the comparison of the simulated LST with MODIS and FY-2F LST products, the errors of the results were feasible and accredited, and the simulated global LST has a reasonable spatiotemporal distribution and change trend. The simulated LST data can therefore be used as base datasets to simulate the thermal infrared imagery from moon-based Earth observations in future research.

An Earth radiation observatory on the nearside of the Moon could provide long-term measurements complementary to the satellite-based Earth radiation observation data. This study is intended as a pr...

Sensitivity analysis (SA) can be used to identify the effects of crop model input parameters on model results. Previous studies have indicated that the ranges, distributions, and properties of many parameters are difficult to estimate, though some may be acquired indirectly from literature. Parameter ranges are often adjusted to fit specific applications, but some are not representative of crop properties. The objectives of this study were to: (i) comprehensively quantify the effects of parameter variation on the sensitivity of AquaCrop output variables based on the extended Fourier amplitude sensitivity test, (ii) study the effects of parameter variation on the sensitivity of output variables over time, and (iii) calibrate and simplify the AquaCrop model based on the SA results under different agro-meteorological conditions. The results demonstrated that output variable sensitivity varied in response to crop parameter variation. The first order sensitivity index (FOSI) demonstrated that some crop parameters were sensitive to yield and maximum dry biomass. The results from the time-dependent FOSI analysis revealed that some parameters were insensitive to yield and maximum dry biomass but were sensitive to canopy cover and dry biomass during certain wheat growth stages. The total sensitivity index (TSI) and time-dependent TSI of crop parameters were more sensitive than the FOSI and time-dependent FOSI of crop parameters. The sensitivities of winter and spring wheat simulations based on FOSI and time-dependent FOSI were consistent; however, some differences existed between simulations based on TSI and time-dependent TSI. Simulations based on SA were more accurate than previously reported results. Results from the calibrated and simplified AquaCrop model revealed good consistency between simulated and measured canopy cover, biomass, and yield. Future studies should focus on analyzing the sensitivity of crop parameters to dynamic output variables over time to better calibrate and simplify the AquaCrop model.

The accurate and timely estimation of winter-wheat yield at the field and regional scales is critical to developing agricultural management strategies and reducing the effect of changes in environmental conditions on crop yield. Growth status and trend (GST) monitoring has been widely applied to estimate agronomic parameters using remote sensing methods. Many studies have employed GST monitoring, however, most of them were based on a single agronomic parameter and can therefore only represent one-sided or local GST information. Additionally, each agronomic parameter is interactive. Meanwhile, little studies have systemically combined multiple agronomic parameters into one comprehensive indicator to estimate crop yield using remote sensing data. Thus, the objectives of the current research were to build a comprehensive yield evaluation indicator (CYEI) using the improved fuzzy comprehensive evaluation (FCE) method and evaluate the performance of CYEI to monitor GST and estimate yield. The results showed that the CYEI can fully reflect the information of the leaf area index, leaf biomass, leaf water content, and leaf nitrogen content. Compared with various agronomic parameters, the CYEI based on the improved FCE method was more closely correlated with the yield (the R2 values of the validation set were 0.63, 0.69, and 0.63 at the booting stage, anthesis stage, and milk development stage.). The CYEI was estimated using a linear model constructed using the optimal VIs, and the results for the three growth stages achieved a higher precision (R2 = 0.74, 0.74, and 0.68 for the booting, anthesis, and milk development stages, respectively) than the traditional single agronomic parameter. The CYEI and Bayesian information criterion were then used to select VIs and then build a partial least squares regression model to estimate the yield. The estimation accuracy was found to be satisfactory, with R2 values of 0.55, 0.64, and 0.66 at the booting, anthesis, and milk development stages, respectively. Finally, a more intuitive image-scale yield monitoring method was obtained based on unmanned aerial vehicle remote sensing hyperspectral imagery. In the future, the proposed method can be used to obtain wheat growth information and provide a new prediction indicator to better estimate yield in precision agriculture.

INTRODUCTION: Two-factor theory suggests delusions require two neuropsychological impairments, one in perception (which furnishes content), and a second in belief evaluation (that augers formation and maintenance). Capgras delusion; the belief that one’s loved one has been replaced by an imposter, then entails two independent processes; first a lack of skin conductance response to familiar faces so the loved one feels different. This has been demonstrated in four patients with damage to ventromedial prefrontal cortex (vmPFC) but who do not have delusions. Thus two-factor theorists demand a second factor: a change in belief evaluation, which is associated with damage to the right dorsolateral prefrontal cortex (rDLPFC). METHODS: Literature review of foundational and related papers on the cognitive neuropsychology of delusions, perception and belief. RESULTS: The four vmPFC patients appear together in another publication, uncited by two-factor theorists, in which the full extent of their damage is documented. These four cases not only lack skin responses to familiar faces, but lack responses to salient psychological stimuli more generally, which challenges factor one. They also have damage outside vmPFC, including damage to rDLPFC, which challenges factor two. CONCLUSION: Two factor theory is found lacking and should be reappraised.

A Moon-based Earth radiation observatory (MERO) could provide a longer-term continuous measurement of radiation exiting the Earth system compared to current satellite-based observatories. In order to parameterize the detector for such a newly-proposed MERO, the evaluation of the instrument’s entrance pupil irradiance (EPI) is of great importance. The motivation of this work is to build an EPI estimating model for a simplified single-pixel MERO instrument. The rationale of this model is to sum the contributions of every location in the MERO-viewed region on the Earth’s top of atmosphere (TOA) to the MERO sensor’s EPI, taking into account the anisotropy in the longwave radiance at the Earth TOA. Such anisotropy could be characterized by the TOA anisotropic factors, which can be derived from the Clouds and the Earth’s Radiant Energy System (CERES) angular distribution models (ADMs). As an application, we estimated the shortwave (SW) (0.3–5 µm) and longwave (LW) (5–200 µm) EPIs for a hypothetic MERO instrument located at the Apollo 15 landing site. Results show that the SW EPI varied from 0 to 0.065 W/m2, while the LW EPI ranged between 0.061 and 0.075 W/m2 from 1 to 29 October, 2017. We also utilized this model to predict the SW and LW EPIs for any given location within the MERO-deployable region (region of 80.5°W–80.5°E and 81.5°S–81.5°N on the nearside of the Moon) for the future 18.6 years from October 2017 to June 2036. Results suggest that the SW EPI will vary between 0 and 0.118 W/m2, while the LW EPI will range from 0.056 to 0.081 W/m2. Though the EPI estimating model in this study was built for a simplistic single-pixel instrument, it could eventually be extended and improved in order to estimate the EPI for a multi-pixel MERO sensor.

Four chemometric techniques for estimating LNC in winter wheat were compared by spectral features. The predictive power and impact of sample size were evaluated. Key results include: (1) partial least squares regression (PLSR) and support vector machines regression (SVR) performed better than the other two methods, with coefficient of determination (r 2) values in the calibration set of 0.82 and 0.81 and the normalized root mean square error (NRMSE) values in the validation set of 5.48 and 5.94%, respectively; (2) the lowest accuracy was achieved using stepwise multiple linear regression (SMLR), with r 2 and NRMSE values of 0.78 and 6.52%, respectively; (3) the predictive power of the back propagation neural network (BPN) was enhanced as sample size increased. Sample size less than 80 is not recommended when using BPN. These results suggest that PLSR and SVR are preferred choices to estimate LNC in winter wheat, and BPN is recommended when a sufficient sample size is available.

Leaf area index LAI and leaf chlorophyll content LCC are major considerations in management decisions, agricultural planning, and policy-making. When a radiative transfer model RTM was used to retrieve these biophysical variables from remote-sensing data, the ill-posed problem was unavoidable. In this study, we focused on the use of agronomic prior knowledge APK, constructing the relationship between LAI and LCC, to restrict and mitigate the ill-posed inversion results. For this purpose, the inversion results obtained using the SAILH+PROSPECT PROSAIL canopy reflectance model alone no agronomic prior knowledge, NAPK and those linked with APK were compared. The results showed that LAI inversion had high accuracy. The validation results of the root mean square error RMSE between measured and estimated LAI were 0.74 and 0.69 for NAPK and APK, respectively. Compared with NAPK, APK improved LCC estimation; the corresponding RMSE values of NAPK and APK were 13.36 µg cm–2 and 9.35 µg cm–2, respectively. Our analysis confirms the operational potential of PROSAIL model inversion for the retrieval of biophysical variables by integrating APK.

International audience; Yellow rust (YR) is one of the most destructive diseases of wheat. We introduced the Bayesian network analysis as a core method and develop a large-scale YR forecasting model based on several important meteorological variables that associate with disease occurrence. To guarantee an effective model calibration and validation, we used multiple years (2010–2012) of meteorological data and the ground survey data in Gansu Province where the YR intimidated most severely in China. The validation results showed that the disease forecasting model is able to produce a reasonable risk map to indicate the disease pressure across the region. In addition, the temporal dispersal of YR can also be delineated by the model. Through a comparison with some classic methods, the Bayesian network outperformed BP neutral network and FLDA in accuracy, which thereby suggested a great potential of Bayesian network in disease forecasting at a regional scale.

The prevalence of powdery mildew (PM) in winter wheat field has a severe impact on crop production. An effective and timely forecast of the disease at a regional scale is necessary to control and prevent it. In this study, both meteorological and remotely sensed observations associated with crop characteristics and habitat traits were integrated for modeling the PM occurrence probability. With an effective feature selection procedure, four meteorological factors, including precipitation, temperature, sun radiation, humidity, and two remotely sensed features including reflectance of red band ( ${\rm R}_{\rm R}$ ) demonstrate that the disease risk maps were able to depict the approximately spatial distribution of PM and its temporal dynamic in the study area. Compared with the model constructed with meteorological data only, the integrated model constructed with both remote sensing and meteorological data has produced a higher accuracy (increasing overall accuracy from 69% to 78%) of forecasting the PM occurrence. This suggests that there would be a great potential for predicting the PM occurrence probability by integrating both meteorological and remote sensing data at a regional scale. In the future, multiple forms of information (e.g., Web sensors networks data) are expected to be incorporated in the disease-forecasting model to further improve its performance for forecasting the disease occurrence (e.g., PM) at a regional scale.

Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher's linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level.

Remote sensing has great potential to serve as a useful means in crop disease detection at regional scale. With the emerging of remote sensing data on various spectral settings, it is important to choose appropriate data for disease mapping and detection based on the characteristics of the disease. The present study takes yellow rust in winter wheat as an example. Based on canopy hyperspectral measurements, the simulative multi-spectral data was calculated by spectral response function of ten satellite sensors that were selected on purpose. An independent t-test analysis was conducted to access the disease sensitivity for different bands and sensors. The results showed that the sensitivity to yellow rust varied among different sensors, with green, red and near infrared bands been identified as disease sensitive bands. Moreover, to further assess the potential for onboard data in disease detection, we compared the performance of most suitable multi-spectral vegetation index (MVI)-GNDVI and NDVI based on Qu...

International audience; Yellow rust (YR) is one of the most destructive diseases of wheat. To prevent the prevalence of the disease more effectively, it is important to forecast it at an early stage. To date, most disease forecasting models were developed based on meteorological data at a specific site with a long-term record. Such models allow only local disease prediction, yet have a problem to be extended to a broader region. However, given the YR usually occurs in a vast area, it is necessary to develop a large-scale disease forecasting model for prevention. To answer this call, in this study, based on several disease sensitive meteorological factors, we attempted to use Bayesian network (BNT), BP neural network (BP), support vector machine (SVM), and fisher liner discriminant analysis (FLDA) to develop YR forecasting models. Within Gansu Province, an important disease epidemic region in China, a time series field survey data that collected on multiple years (2010-2012) were used to conduct effective calibration and validation for the model. The results showed that most methods are able to produce reasonable estimations except FLDA. In addition, the temporal dispersal process of YR can be successfully delineated by BNT, BP and SVM. The three methods of BNT, BP and SVM are of great potential in development of disease forecasting model at a regional scale. In future, to further improve the model performance in disease forecasting, it is important to include additional biological and geographical information that are important for disease spread in the model development.

Leaf area index (LAI) and LCC, as the two most important crop growth variables, are major considerations in management decisions, agricultural planning and policy making. Estimation of canopy biophysical variables from remote sensing data was investigated using a radiative transfer model. However, the ill-posed problem is unavoidable for the unique solution of the inverse problem and the uncertainty of measurements and model assumptions. This study focused on the use of agronomy mechanism knowledge to restrict and remove the ill-posed inversion results. For this purpose, the inversion results obtained using the PROSAIL model alone (NAMK) and linked with agronomic mechanism knowledge (AMK) were compared. The results showed that AMK did not significantly improve the accuracy of LAI inversion. LAI was estimated with high accuracy, and there was no significant improvement after considering AMK. The validation results of the determination coefficient (R2) and the corresponding root mean square error (RMSE) between measured LAI and estimated LAI were 0.635 and 1.022 for NAMK, and 0.637 and 0.999 for AMK, respectively. LCC estimation was significantly improved with agronomy mechanism knowledge; the R2 and RMSE values were 0.377 and 14.495 μg cm -2 for NAMK, and 0.503 and 10.661 μg cm -2 for AMK, respectively. Results of the comparison demonstrated the need for agronomy mechanism knowledge in radiative transfer model inversion.

To overcome the problem of lacking apparent features, e.g. color or shape, in the process of identifying wheat stripe rust from powdery mildew using computer vision algorithms, a novel directional feature based on Improved Rotation Kernel Transformation (IRKT) is proposed. IRKT can calculate the statistics of the direction distribution of infected leaf images in spatial domain. The statistics calculated from IRKT are insensitive to noise and can lead to a good description of directional distribution of object, which is suitable for the recognition of wheat stripe rust and powdery mildew and provides a novel method to represent other plant disease. As showed in experimental results, the proposed IRKT directional feature is fit for the recognition of wheat stripe rust and powdery mildew, and the accuracy can achieve 97.5%.