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• A novel model (LabTNet) is proposed to estimate crop biochemical traits. • LabTNet jointly estimates multiple wheat traits by hyperspectral data. • LabTNet exhibits superior predictive performance than existing methods. • Grad-CAM analyzed hyperspectral attention regions of LabTNet. Accurate estimation of crop leaf and canopy biochemical traits, such as leaf dry matter content (Cm), leaf equivalent water thickness (Cw), leaf area index (LAI), dry leaf biomass (DLB), leaf total water content (LW), and fresh leaf biomass (FLB), is essential for monitoring crop growth accurately. The vegetation spectral feature technique combined with statistical regression methods is widely employed for remote sensing crop biochemical traits mapping. However, the crop canopy spectral reflectance is influenced by various crop biochemical traits and uncertainties in geometric changes of light and soil background effects. Consequently, the remote-sensing estimation of crop biochemical traits is limited. A potential solution involves training a deep learning model to understand the physical relationship between crop biochemical traits and canopy spectral reflectance based on a physical radiative transfer model (RTM). The primary focus of this study is to propose a winter-wheat leaf and canopy biochemical traits analysis and mapping method based on hyperspectral remote sensing, utilizing a deep learning network for leaf area index and leaf biochemical traits deep learning network (LabTNet). This study consists of four main tasks: (1) Field-based measurements of winter-wheat spectra and biochemical traits were conducted in two growing seasons. A PROSAIL RTM was also employed to generate a simulated dataset representing comprehensive and complex winter-wheat field conditions. (2) The LabTNet deep learning model was pre-trained using the simulated spectra dataset to acquire knowledge of the physical relationship between crop biochemical traits and canopy spectral reflectance derived from the RTM. Subsequently, the model was re-trained using the field-based spectra dataset from two growing seasons, employing a transfer learning technique. (3) An analysis was conducted to assess the performance of LabTNet against traditional statistical regression methods in estimating crop leaf and canopy biochemical traits. The study used the gradient-weighted class activation mapping (Grad-CAM) technique to analyze the attention regions of input spectra (454:8:950 nm, 960:10:1300 nm, 1450:10:1750 nm, 2000:10:2350 nm) by different convolutional neural network layers in LabTNet, aiming to enhance the interpretability of deep learning models. (4) Winter-wheat leaf and canopy biochemical traits (Cw, Cm, LAI, DLB, LW, and FLB) were mapped using the LabTNet deep learning model. Our research has the following conclusions: (1) Combining the RTM and deep learning techniques yields higher winter-wheat biochemical trait estimates than traditional statistical regression methods. (2) Different LabTNet deep learning model layers focus on distinct areas of canopy reflectance, corresponding to the sensitive regions for various winter-wheat biochemical traits. (3) LabTNet demonstrates similar winter-wheat leaf and canopy biochemical traits estimation performance using visible and near-infrared (VNIR) reflectance data and full-spectral (FS) range hyperspectral reflectance as inputs (Cw: R 2 = 0.603–0.653, RMSE = 0.0015–0.0015 cm; Cm: R 2 = 0.511–0.560, RMSE = 0.0006–0.0007 g/m2; LAI: R 2 = 0.773–0.793, RMSE = 0.65–0.66 m2/m2; LW: R 2 = 0.842–0.847, RMSE = 67.93–70.73 g/m2; DLB: R 2 = 0.747–0.762, RMSE = 21.10–21.89 g/m2; FLB: R 2 = 0.831–0.840, RMSE = 86.26–90.30 g/m2). The combined use of UAV hyperspectral remote sensing and the LabTNet model proves effective in providing high-performance winter-wheat leaf and canopy biochemical trait maps, offering valuable insights for agricultural management. [ABSTRACT FROM AUTHOR]

• The increasing trend of vapor pressure deficit (VPD) change in 1997–2020 is ten times higher than in 1981–1996. • Increasing VPD promoted cropland and grassland growth in Arid, while inhibited the growth of shrubland. • Significant positive or negative difference in the sensitivity to VPD for the same vegetation under different climatic zones. • The vast majority of vegetation showed a significant increase in the inter-annual trend of the sensitivity to VPD. • The relationship of higher sensitivity with lower VPD is not stable under different vegetation and climatic zones. Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p < 0.05), except for the SL type, which showed a significant decreasing trend (p < 0.05). The trends of sensitivity are not affected by the differences in vegetation types (all of them show an increasing trend) under arid and semi-arid conditions, while dry sub-humid and humid have a greater impact on sensitivity trends. The finding of an overall increase in sensitivity suggests a mechanism of erratic change in vegetation growth under climate change. Notably, the increased sensitivity of certain vegetation types (especially GL and CL) suggests that these may become progressively vulnerable to increased VPD as global climate change persists, with the risk of moving from facilitation to inhibition of photosynthesis. [ABSTRACT FROM AUTHOR]

Phase II clinical trials in oncology are used to initially evaluate the therapeutic efficacy of a new treatment. In the past, the total response was a frequently used endpoint to access the effectiveness of the treatment. When the total response is modest, clinicians may also be interested in disease control (defined as the total response or stable disease) since it may better predict clinical outcomes. Thus, formally testing both the total response and disease control in a phase II trial has an important clinical implication. In this paper, we propose a new method to construct optimal subset designs for one-stage and two-stage phase II clinical trials with two binary endpoints, i.e. the total response and disease control. A new set of hypotheses under the framework of intersection-union tests is provided in which the treatment is considered promising if both the total response and disease control are good. We show that the power function for each test in a large family of tests is nondecreasing in both the total response rate and disease control rate; identify the parameter configurations at which the maximum Type I error rate and minimum power are achieved and derive level- α tests. We also provide optimal one-stage designs with the minimum sample size and optimal two-stage designs with the least expected total sample size. • A new method of constructing optimal subset designs is proposed. • The new method has potential of establishing more precise inferential conclusions. • The proposed optimal design usually yields a smaller sample size. [ABSTRACT FROM AUTHOR]

In order to solve the pollution problem of organic pollutants in water, this study proposed a photocatalyst that efficiently utilizes sunlight, and TiO 2 /HTiOF 3 photocatalytic materials were successfully prepared by a hydrothermal method and used for the degradation of rhodamine B (RhB) solution under simulated sunlight. The effect of time and urea addition on the performance of the material was investigated, and the results showed that the sample with a reaction time of 12 h and a urea addition of 0.05 g exhibited 97 % degradation efficiency and the best photocatalytic degradation activity for RhB solution, which was attributed to the construction of a traditional type II heterojunction of the TiO 2 /HTiOF 3 nanocomposite, which improved the carrier separation rate and maintained a strong redox capacity. This work contributes to the construction of efficient semiconductor photocatalytic nano-heterostructures. The preparation of TiO 2 /HTiOF 3 composites by one-step hydrothermal method provides a new idea for the development of catalysts. • Stable HTiOF 3 intermediates were synthesized by introducing urea, and TiO 2 /HTiOF 3 photocatalytic materials were constructed by a one-step hydrothermal method. • The construction of type II heterojunction effectively reduces the interfacial resistance while greatly extending the lifetime of the photogenerated carriers. • Photocatalytic performance of TiO 2 /HTiOF 3 composites is greatly improved over pure TiO 2. [ABSTRACT FROM AUTHOR]

• Introduction of a novel model that integrates OBIA and the attention mechanism for precise orchard mapping. • The contribution of SAR is studied in orchard classification. • The model obtains more accurate classification results than RNN-based and CNN-based deep learning models. • The uncertainty of the super-pixel size on classification accuracy is investigated. Reliable and accurate classification of orchards is important for the dynamic monitoring of large-scale orchards and food security evaluation. At present, the very similar spectral profiles of different fruit trees and the high susceptibility of optical data to interference by weather conditions limit the resolution of orchard classification. Synthetic Aperture Radar (SAR) imagery provides an advanced solution to this problem, with the advantages of being immune to weather conditions and advances in deep learning techniques. This paper presents an orchard classification model (STCM) with optical and SAR fusion, which integrates the advantages of the Simple Non-Iterative Clustering (SNIC) super-pixel algorithm with a deep learning algorithm based on a multi-headed attention mechanism, and it achieves the best classification accuracy of above 0.82 in comparison with existing deep learning models. Meanwhile, the model has exceptional classification accuracy and robustness in a study area with fragmented plots, many fruit tree species, and various distributions of optical images. In the absence of optical data, the classification accuracy of the STCM model using only SAR data is around 0.70, which makes the model have a promising potential application value. This study provides a technical solution for accurately obtaining different orchard categories in high-resolution remotely sensed orchard images. The study helps to improve the management and operation of orchards and provides a strong basis for decision-making in the fruit industry to enhance the sustainability of the global fruit industry. [ABSTRACT FROM AUTHOR]

Herein, the synthesis of MnO 2 nanowires decorated with transition metal-doped carbon quantum dots (MCQDs) is described for use as binder-free flexible solid-state supercapacitors. In particular, the water-soluble Fe and N co-doped MCQDs prepared by amidation reaction with ferric ammonium citrate and urea had ultrafine particle morphologies and showed super-hydrophilicity in aqueous solution, which greatly improved the contact probability between the electrode and the electrolyte. In addition, the co-doped MCQDs increased both the electrical conductivity and specific capacitance of the MnO 2 /carbon-based nanocomposites and limited the collapse and exfoliation of the MnO 2 structure during extended cycling. Compared with pristine MnO 2 electrode, the MCQDs/MnO 2 composite electrode exhibited faster charge-discharge rate and improved cycle stability with increased specific capacitance. The flexible symmetric supercapacitor constructed from the MCQDs/MnO 2 composite had a capacitance retention of 85.6 % after 10,000 charge/discharge cycles at a current density of 2 mA cm−2. In addition, the device showed a maximum power density of 4.8 W cm−2 at an energy density of 2.3 mW h cm−2, and a maximum energy density of 11.16 mW h cm−2 at a power density of 311.6 mW cm−2. The data that support the findings of this study are available from the corresponding author upon reasonable request. • A metal carbon quantum dots (MCQDs)/MnO 2 nanowire composite is described. • The MCQDs/MnO 2 composite was used to construct flexible solid-state supercapacitors. • Super-hydrophilic MCQDs improved the contact probability of the interface. • MCQDs improved the conductivity, specific capacitance and cycling stability of MnO 2. • The flexible symmetric supercapacitor exhibited good electrochemical performance. [ABSTRACT FROM AUTHOR]

Abstract When dealing with multiple growth stages, estimates of above-ground biomass (AGB) based on optical vegetation indices (VIs) are difficult for two reasons: (i) optical VIs saturate at medium-to-high canopy cover, and (ii) organs that grow vertically (e.g., biomass of reproductive organs and stems) are difficult to detect by canopy spectral VIs. Although several significant improvements have been made for estimating AGB by using narrow-band hyperspectral VIs, synthetic aperture radar, laser intensity direction and ranging, the crop surface model technique, and combinations thereof, applications of these new techniques have been limited by cost, availability, data-processing difficulties, and high dimensionality. The present study thus evaluates the use of ultrahigh-ground-resolution image textures, VIs, and combinations thereof to make multiple temporal estimates and maps of AGB covering three winter-wheat growth stages. The selected gray-tone spatial-dependence matrix-based image textures (e.g., variance, entropy, data range, homogeneity, second moment, dissimilarity, contrast, correlation) are calculated from 1-, 2-, 5-, 10-, 15-, 20-, 25-, and 30-cm-ground-resolution images acquired by using an inexpensive RGB sensor mounted on an unmanned aerial vehicle (UAV). Optical-VI data were obtained by using a ground spectrometer to analyze UAV-acquired RGB images. The accuracy of AGB estimates based on optical VIs varies, with validation R 2: 0.59–0.78, root mean square error (RMSE): 1.22–1.59 t/ha, and mean absolute error (MAE): 1.03–1.27 t/ha. The most accurate AGB estimate was obtained by combining image textures and VIs, which gave R 2: 0.89, MAE: 0.67 t/ha, and RMSE: 0.82 t/ha. The results show that (i) the eight selected textures from ultrahigh-ground-resolution images were significantly related to AGB, (ii) the combined use of image textures from 1- to 30-cm-ground-resolution images and VIs can improve the accuracy of AGB estimates as compared with using only optical VIs or image textures alone; and (iii) high AGB values from winter-wheat reproductive growth stages can be accurately estimated by using this method; (iv) high estimates of winter-wheat AGB (8–14 t/ha) using the proposed combined method (DIS1, SE30, B460, B560, B670, EVI2 using MSR) show a 22.63% (nRMSE) improvement compared with using only spectral VIs (LCI, NDVI using MSR), and a 21.24% (nRMSE) improvement compared with using only image textures (COR1, DIS1, SE30, EN30 using MSR). Thus, the combined use of image textures and VIs can help improve estimates of AGB under conditions of high canopy coverage. [ABSTRACT FROM AUTHOR]

Abstract A simple hydrothermal method is developed for the growth of NiCo 2 O 4 nano-needle arrays on a filter paper carbon (FC) substrate for use as a high performance electrode for supercapacitors. The as-synthesized NiCo 2 O 4 /C composites exhibited high specific capacitance (995.2 F g−1 at a current density of 10 A g−1) and excellent cycling performance (no capacitance loss after 3000 cycles). The 3D framework of the FC substrate not only prevents the aggregation of NiCo 2 O 4 nanoparticles, but also improves its electrical conductivity, thus leading to excellent electrochemical performance. Moreover, an asymmetric supercapacitor was assembled by using the NiCo 2 O 4 /C composites and activated carbon as the positive and negative electrode, respectively, and provided a maximum energy density of 20.87 Wh kg−1 at a power density of 374.6 W kg−1 and a maximum power density of 7.48 kW kg−1 at an energy density of 11.43 Wh kg−1 within a potential window of 0–1.5 V, demonstrating the potential of the composites for use in high performance energy storage systems. Highlights • Filter paper derived carbon (FC)/NiCo 2 O 4 was prepared by hydrothermal method. • The FC with open channels increased the electron and ion transport rates. • The FC-based supercapacitor exhibited good capacity and cycling stability. [ABSTRACT FROM AUTHOR]

MnO 2 /biomass-derived porous carbon (BPC) composites have been prepared by a hydrothermal method, in which the BPC 3D porous carbon structure was based on a banana peel. The banana peel, after freeze drying, can maintain its hierarchical natural porous structure, which provides enough growth space for MnO 2 and reduces the agglomeration of MnO 2 particles. The MnO 2 /BPC composites were characterized by XRD, FT-IR, XPS, TGA, SEM, TEM, BET. The electrochemical performance of the composites was tested in three-electrode supercapacitors using 1 M Na 2 SO 4 aqueous solution as an electrolyte. Due to the large amounts of hierarchical pores and large pore volume, the as-prepared composites exhibited good electrochemical performance. Electrochemical measurements indicated that the MnO 2 /BPC composites applied in supercapacitors had a specific capacitance of 139.6 F g −1 at 300 mA g −1 , and exhibited a good cycling stability with a capacitance retention ratio of 92.3% after 1000 cycles (at 1 A g −1 ). The MnO 2 /BPC composites with 3D porous structure are promising materials in the application of supercapacitors. [ABSTRACT FROM AUTHOR]

• We introduce leaf N profile characteristic coefficient K m on its remote estimates. • Diverse datasets are used for developing robust models for remote estimates of K m. • Four regression algorithms are tested with various possible sets of predictors. • A robust method is proposed to estimate K m from canopy reflectance for winter wheat. The characteristic coefficient of vertical leaf nitrogen (N) profile is a canopy parameter that indicates the attenuation steepness of leaf N from the top of canopy downward. It is sensitive not only to crop production, grain yield and quality, and light- and N-use efficiency but also to N deficiency in the crop. We introduce this coefficient by exploring a robust method to estimate it from canopy spectral reflectance. We analyze comprehensively various approaches based on diverse datasets of winter wheat. We test and compare the accuracy and stability of models by using the adjusted and weighted coefficient of determination (wR adj 2), the mean absolute error (MAE), and the mean and coefficient of variation (CV) over multiple seasons. The analysis focuses mainly on the coefficient of mass-based leaf N profile (K m); nevertheless, a comparison with the coefficient of area-based leaf N profile is presented. The results indicate that the most robust model to estimate K m of winter wheat is K m = (1.8037 R G V I - 0.9702 + 0.0786 e x p (0.6315 / D A S F)) / 2 , where RGVI is the red and green ratio vegetation index, and DASF is the directional area scattering factor. The mean wR adj 2 is 0.663 with CV = 8.2% and the mean MAE is 0.117 with CV = 12.8% over three seasons including various situations. This makes it possible to assess K m at large areas and follow its dynamics over multiple periods in a timely and nondestructive manner. [ABSTRACT FROM AUTHOR]

Crop evapotranspiration (ET c ) is an important indicator used in managing agriculture water and monitoring crop growth. The objectives of this study were to: (1) analyze the seasonal dynamics of crop coefficients (K c ) and basal crop coefficient (K cb ) derived from vegetation indices (VIs) based on a time series of Huanjing (HJ) satellite images during 2011 and 2013; (2) investigate daily and monthly variations of ET c at key growth stages of winter wheat using lysimeter or eddy covariance systems; (3) compare the performance of two Huanjing-1A/B satellite-based FAO-56 models (the FAO-56 dual-crop coefficient model and the vegetation indices-reference evapotranspiration (VIs-ET o ) method) to the ET c measurements; (4) select the best ET c model for estimating daily ET c (mm/day) at the Xiaotangshan experimental site and its surrounding farmland in conjunction with HJ satellite overpasses from March to May 2011. The VIs and concurrent ET c were acquired at the Xiaotangshan experimental site, Beijing, China, during the 2011 and 2013 winter wheat growing seasons. The results showed that the overall tendencies of crop coefficient patterns (K cb and K c ), ET c and ET o first increased and then decreased at key growth stages of winter wheat. The cumulative ET c of water consumption was highest at the heading-filling stage in May. Similar changes in cumulative ET c were found during April–May 2011 and 2013. The estimation accuracy of ET c was better based on FAO-56 dual-crop coefficient model ( R 2 = 0.88 and RMSE = 1.06 mm/day in 2011 and R 2 = 0.84 and RMSE = 0.55 mm/day in 2013) than the VI-ET o method ( R 2 = 0.77 and RMSE = 1.22 mm/day in 2011 and R 2 = 0.67 and RMSE = 0.81 mm/day in 2013). The results indicated that the FAO-56 dual-crop coefficient model and VI-ET o methods were used to estimate ET c in winter wheat. Two Huanjing-1A/B satellite-based FAO-56 models were used to timely estimate ET c during the winter wheat mid-season, and ET c was used to adjust agricultural water management practices. [ABSTRACT FROM AUTHOR]

• Multispectral data are effective in discriminating tea plant varieties. • Texture information are essential elements in discriminating tea plant varieties. • Data fusion of multispectral image can improve tea plant discrimination rate. Discrimination of tea plant (Camellia sinensis L.) varieties is of significant value for the efficient management of cultivation and resources optimization of tea industry. Phenotypic and spectral characteristics of tea plants are important indicators for determining the quality of tea cultivars to a certain extent. However, few studies have used spectral image information to identify tea plant varieties. The aim of this research was the discrimination of 16 types of high-yield tea plant varieties using a multispectral camera. This methodology involved image registration, calibration, segmentation, information extraction, and data fusion. The hue (H), saturation (S), and value (V), texture information, and several spectral vegetation indices were acquired from the multispectral image of the tea plant canopy. The successive projection algorithm (SPA) was used to analyze the original parameters. Three classification methods were applied to tea plant variety discrimination: Bayes discriminant analysis (BDA), support vector machine (SVM), and extreme learning machine (ELM). The results indicated that SPA based on fusing data combined with the SVM classification model, achieved a feasible method to identify tea plant varieties. Additionally, the method achieved accuracy in the training, test, and validation sets, reaching 97.00%, 90.52%, and 88.67%, respectively. This study proposed a new perspective on multispectral image information as an identifier of tea plant varieties. The explored model will be helpful for the development of portable instruments for commercial applications in variety identification and phenotype recognition of tea plants. [ABSTRACT FROM AUTHOR]

• Vegetation indices show a strong correlation with nitrogen status in tea plants. • The combination of multispectral data and hyperspectral data is effective in monitoring nitrogen level. • Fusing data of image information and spectral information has remarkable ability to predict nitrogen content in tea plants. Nitrogen (N) plays a pivotal role in management of tea plantation, with significant impacts on the growth, productivity, and nutrition status of tea plants. The existing methods for N content monitoring of tea leaves are complicated and can not realize in suite and in real time way. This study proposed a method for estimating the N content of tea plants in field conditions based on a combination of a multispectral imaging system and hyperspectral data. A total of 32 parameters were extracted from five tea gardens using calibrated multispectral images of the tea plant canopy, and 27 indices were selected by Pearson correlation analysis. A total of 28 wavelengths selected by competitive adaptive reweighted sampling from hyperspectral data were combined with 27 multispectral indices as the original data. Subsequently, five variables of fused data (H, VOG, BGI, 1664 nm and 1665 nm) were selected by variable combination population analysis based on the 55 combination parameters. Partial least squares regression, random forest regression, and support vector machine regression (SVR) models all showed excellent performance for both the calibration and prediction sets. The overall results indicated that the infused data of multispectral and hyperspectral data combined with SVR are effective in monitoring the N level under field conditions, and the R2 (coefficient of determination) and root mean square error values of the prediction were 0.9186 and 0.0560, respectively. The findings of this study are important in retaining the nutritional and quality attributes of agricultural commodities. [ABSTRACT FROM AUTHOR]

• Accurately forecast the first flowering period of two typical apple producing areas in China. • It fills in the research of forecasting the first flowering period of apple from point scale to regional scale. • The phenological survey method of replacing time with space is very effective for the calibration of sequential model. • The prediction of the apple first flowering on a regional scale has important application value for optimizing orchard management. China is one of the largest apple-producing countries in the world, with large orchards and diverse climates. Accurately forecasting the first-flowering time of apple trees can assist orchard managers in their deciding when to apply anti-freeze. The temperature-driven sequential model from previous studies can be used to forecast the flowering phenology of deciduous fruit trees. However, this model requires many years of observational data for calibration, so flowering forecasts based on traditional phenological models cannot be implemented in areas that lack such historical data. To overcome this problem, the present work combines a spatial rather than a temporal phenological survey method with 1-km-gridded temperature products to calibrate the chill and heat requirement parameters of the sequential model. We then use the model to forecast the first-flowering on a regional scale for Luochuan and Linyi, which are two main apple-producing areas of China. The results show that the proposed method accurately forecasts regional flowering. The root mean squared errors (RMSE) for Luochuan and Linyi were 4.7 and 4.4 days, respectively, and the normalized RMSEs were all less than 5.19%. We expect the proposed regional first-flowering forecast method to be an important aid to optimize orchard management. [ABSTRACT FROM AUTHOR]

• Synthetic Minority Oversampling Technique and Edited Nearest Neighbors are applied. • A SMOTE-ENN-XGBoost model is developed to detect lodging in imbalanced datasets. • The developed model is explained with SHAP values. • Deeper insight is obtained into how the model behaves with these explanations. • Complex and interrelated impacts of the selected features are captured and analyzed. Remote sensing image is becoming an increasingly popular tool for crop lodging detection because it conveniently provides features for building machine learning models and predicting lodging. However, difficulties in interpreting machine learning models and their predictions limit the confidence of using remote sensing images to detect lodging. In addition, the lodging datasets used for modeling are difficult to balance under natural conditions. Designing a robust and interpretable classification model for the detection of lodging in an imbalanced distribution dataset poses a particularly difficult challenge. In this study, visible and multi-spectral images were collected with a UAV to extract relevant features from remote sensing images. In a preliminary step, Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) method were used to treat imbalanced datasets. The SMOTE-ENN-XGBoost model is proposed for the efficient identification of maize lodging at the plot scale. The SMOTE-ENN-XGBoost model achieved an F1-score of 0.930 and a recall of 0.899 on a testing set, suggesting that it can be used for modeling lodging detection. Additionally, the SHapley Additive exPlanations (SHAP) approach was employed to interpret the identification and prioritization of features that determine lodging classification and activity prediction. The results showed that canopy structure and textural features are relatively stable compared with spectral features, which are susceptible to the external environment when modeling is employed to detect lodging. This work also showed that canopy structural, spectral, and textural information should be considered simultaneously rather than separately when detecting crop lodging in a crop breeding program in order to prevent differences in expression controlled by the interaction between genotype and environment obscuring the change in a single feature before and after lodging. For practical applications of machine learning models in crop lodging detection, such insights are of critical relevance. Taken together, the results of this study encourage further applications of remote sensing techniques to build interpretable machine learning models. [ABSTRACT FROM AUTHOR]

Highlights: [•] The complementary relationship model (CR) was employed to estimate actual ET. [•] The high temporal ET was validated by Lysimeter observation. [•] The CR model was combined with IKONOS image to estimate high spatial resolution instantaneous and daily ET. [Copyright &y& Elsevier]

Highlights: [•] PLSR of band depth ratio together with optimal NDVI-like estimated biomass best. [•] The PLSR model based on BDR was better than optimal NDVI-like/SAVI-like. [•] The REP got lower estimation accuracy than optimal NDVI-like/SAVI-like. [•] The performance of REP was better than that of NDVI/SAVI. [ABSTRACT FROM AUTHOR]

Abstract: Fractional vegetation cover (FVC) is an important indicator of mountain ecosystem status. A study on the seasonal changes of FVC can be beneficial for regional eco-environmental security, which contributes to the assessment of mountain ecosystem recovery and supports mountain forest planning and landscape reconstruction around megacities, for example, Beijing, China. Remote sensing has been demonstrated to be one of the most powerful and feasible tools for the investigation of mountain vegetation. However, topographic and atmospheric effects can produce enormous errors in the quantitative retrieval of FVC data from satellite images of mountainous areas. Moreover, the most commonly used analysis approach for assessing FVC seasonal fluctuations is based on per-pixel analysis regardless of the spatial context, which results in pixel-based FVC values that are feasible for landscape and ecosystem applications. To solve these problems, we proposed a new method that incorporates the use of a revised physically based (RPB) model to correct both atmospheric and terrain-caused illumination effects on Landsat images, an improved vegetation index (VI)-based technique for estimating the FVC, and an adaptive mean shift approach for object-based FVC segmentation. An array of metrics for segmented FVC analyses, including a variety of area metrics, patch metrics, shape metrics and diversity metrics, was generated. On the basis of the individual segmented FVC values and landscape metrics from multiple images of different dates, remote sensing of the seasonal variability of FVC was conducted over the mountainous area of Beijing, China. The experimental results indicate that (a) the mean value of the RPB–NDVI in all seasons was increased by approximately 10% compared with that of the atmospheric correction-NDVI; (b) a strong consistency was demonstrated between ground-based FVC observations and FVC estimated through remote sensing technology (R 2 =0.8527, RMSE=0.0851); and (c) seasonal changes in the landscape characteristics existed, and the landscape diversity reached its maximum in May and June in the study area. [Copyright &y& Elsevier]

Abstract: Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990m and 90m resolutions, respectively. Secondly, the relationship between the 990m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90m data (R 2 =0.709 and RMSE=2.702K). [Copyright &y& Elsevier]

Abstract: This paper shows that the join designs [Butler, N.A., 2005. Classification of efficient two-level fractional factorial design of resolution IV or more. J. Statist. Plann. Inference 131, 145–159] include all designs with the maximum number of clear two-factor interactions (2FIs) for . The designs are shown to have concise grid representations which provide simple interpretations of the 2FIs in a design. In addition, a better upper bound on the maximum number of clear 2FIs in a design than the one obtained by Tang et al. [2002. Bounds of the maximum members of clear factor interactions for designs of resolution III and IV. Canad. J. Statist. 30, 127–136] is given. [Copyright &y& Elsevier]

Abstract: It is known by Zhang and Park (J. Statist. Plann. Inference 91 (2000) 107) that there are no minimum aberration (MA) designs with respect to both treatments and blocks for blocked regular mixed-level factorial designs. So it should be compromised between the block wordlength pattern and treatment wordlength pattern. Two methods are considered in this article. The first is MA blocking scheme of an MA design. The other is to combine the components of the two wordlength pattern vectors into one combined wordlength pattern according to the modified hierarchical assumptions and an appropriate ordering of the numbers of alias or confounding relations. The relationship between the two types of optimal blocked designs is investigated. A complete catalogue of optimal blocked regular mixed factorial designs of the above two types with 16 or 32 runs is given. [Copyright &y& Elsevier]

[Display omitted] • Design strategies for 2D/2D NH 2 -TiO 2 /ReS 2 heterojunction photocatalyst. • The transition of NH 2 -TiO 2 /ReS 2 from type-Ⅰ to S-scheme heterojunction by amination modification. • An innovative construction engineering for 2D/2D heterojunction The 2D/2D heterojunction photocatalyst demonstrates a broad range of applications owing to its large interface contact area. At present, most of the current 2D/2D structures are constructed by self-assembly, which mainly forms a relatively weak van der Waals force at the interface. This poses a challenge to achieving effective carrier separation. Here, we proposed an innovative strategy of molecular-connected heterojunctions to address this challenge. The strategy uses (3-aminopropyl) trimethoxysilane (APTMS) to modify the surface of TiO 2 and construct NH 2 -TiO 2 /ReS 2 heterojunction, which not only alters the material's Zeta potential but also uses molecular chains connecting TiO 2 and ReS 2. Simultaneously, the design of molecular-connected heterojunction changes the work function of TiO 2 , leading to the transformation of the TiO 2 /ReS 2 heterojunction from type-Ⅰ to S-scheme, thereby achieving a selective carrier separation. The constructed NH 2 -TiO 2 /ReS 2 photocatalytic heterojunction composite has a hydrogen production rate of 451.3 μmol g-1 h−1, which is over 11 and 3 times than TiO 2 and TiO 2 /ReS 2 , respectively, and exhibits excellent long-term photocatalytic stability. This study presents a novel approach for achieving strong interface bonding and introduces an innovative approach for constructing 2D/2D photocatalytic heterojunctions. [ABSTRACT FROM AUTHOR]

• Determined the optimal UAV wide-band vegetation indices (VIs) for LAI and LCC retrievals. • Analyzed the sensitivity of VIs used for retrieval to influencing factors. • Constructed a hybrid inversion method of LAI and chlorophyll for individual apple tree crowns. • Quantitatively characterized the effect of canopy shading on inversion. The structural and chemical characteristics of individual apple tree crowns can indicate the nutritional and growth status of the trees, making them crucial for advancing orchard management practices. In this study, we collected multispectral imagery and ground validation data from two representative apple orchards in Beijing, China. We employed a hybrid inversion method to estimate the Leaf Area Index (LAI), Leaf Chlorophyll Content (LCC), and Canopy Chlorophyll Content (CCC) of individual apple tree crowns. Furthermore, we quantitatively evaluated the impact of canopy shading on the inversion results and mapped these traits at the scale of individual tree crowns (ITCs). To determine the optimal broad-band Vegetation Indices (VIs) for estimating LAI and LCC, we empirically analyzed 22 VIs using the PROSAIL simulation dataset. We constructed two measured datasets of canopy reflectance by masking canopy shadows, one containing shaded pixels and the other consisting of sunlit-only pixels. Using the Artificial Neural Network (ANN) algorithm and PROSAIL model, we developed a hybrid inversion model to assess the performance of the filtered VIs on the two measured datasets. The results demonstrated that TCARI/OSAVI and SR3 were the most accurate VIs for estimating LAI (including shaded pixels: R2 = 0.67, RMSE = 0.31 m2/m2; sunlit-only pixels: R2 = 0.74, RMSE = 0.28 m2/m2) and LCC (including shaded pixels: R2 = 0.70, RMSE = 7.11 μg/cm2; sunlit-only pixels: R2 = 0.73, RMSE = 6.63 μg/cm2) in the two measured reflectance datasets, respectively. Removing canopy shadows significantly improved the accuracy of LAI and LCC retrieval, although there was no significant difference in CCC retrieval accuracy (including shaded pixels: R2 = 0.78, RMSE = 31.25 μg/cm2; sunlit-only pixels: R2 = 0.79, RMSE = 28.48 μg/cm2). Moreover, we utilized UAV imaging multispectral data to map the estimated variability of leaf and canopy traits. The results revealed trait variability among different apple tree canopies, highlighting the potential of UAV imaging multispectral techniques in characterizing and mapping individual apple tree crown traits while capturing variability among crowns. We recommend performing canopy shading pixel masking to enhance the accuracy of ITCs trait retrieval. [ABSTRACT FROM AUTHOR]

• Existing crop N estimation mainly relies on the correlation of chlorophyll with N. • A comprehensive survey of N-related hyperspectral VIs was made. • Combined use of feature mining method and machine learning was crucial. • Synergy of crop model and hyperspectra could help monitoring of dynamic N status. • Multi-source data integration might reduce uncertainty in crop N status retrieval. Nitrogen (N) is significantly related to crop photosynthetic capacity. Over-and-under-application of N fertilizers not only limits crop productivity but also leads to negative environment impacts. With such a dilemma, a feasible solution is to match N supply with crop needs across time and space. Hyperspectral remote sensing has been gradually regarded as a cost-effective alternative to traditional destructive field sampling and laboratory testing for crop N status determination. Hyperspectral vegetation indices (VIs) and linear nonparametric regression have been the dominant techniques used to estimate crop N status. Machine learning algorithms have gradually exerted advantages in modelling the non-linear relationships between spectral data and crop N. Physically-based methods were rarely used due to the lack of radiative transfer models directly involving N. The existing crop N retrieval methods rely heavily on the relationship between chlorophyll and N. The underlying mechanisms of using protein as a proxy of N and crop protein retrieval from canopy hyperspectral data need further exploration. A comprehensive survey of the existing N-related hyperspectral VIs was made with the aim to provide guidance in VI selection for practical application. The combined use of feature mining and machine learning algorithms was emphasized in the overview. Some feature mining methods applied in the field of classification and chemometrics might be adapted for extracting crop N-related features. The deep learning algorithms need further exploration in crop N status assessment from canopy hyperspectral data. Finally, the major challenges and further development direction in crop N status assessment were discussed. The overview could provide a theoretical and technical support to promote applications of hyperspectral remote sensing in crop N status assessment. [ABSTRACT FROM AUTHOR]

• The method was successfully tested in RGB image dataset. • Measurements by the proposed method are highly correlated with ground truth data. • The method can classify diseases automatically. • The results of the method can realize High-throughput phenotypic information acquisition. Accurately identifying black point disease in wheat kernels from random samples within digital images is a fundamental and challenging task in disease identification. The performance of traditional methods is satisfactory in homogeneous environments, but their performance decreases when they are applied to images acquired in dynamic ones. In this paper, a multifeature-based machine learning method is proposed to identify and evaluate the incidence of black point disease. Ten wheat cultivars with different resistances to disease were selected to verify the accuracy of the method. First, a marker-based watershed algorithm was used to separate wheat kernels from the background to accomplish the coarse segmentation. After patches were generated from the coarse segmentation results, the patches were labeled manually and divided into two categories: black point areas and healthy areas. Gabor and Canny operators were used for texture and shape features respectively to build a feature matrix. Then, a classification model based on a naive Bayes classifier was trained to recognize the differences between the two types of patches by their features. The proposed model finally achieved the correct classification of each pixel from the testing sample and output the results in the form of a binary image, thus accomplishing the segmentation of the image. Finally, the severity of the disease was calculated according to the proportion of minimum circumscribed areas of the disease and the total area of the wheat kernel. Through the above operations, the incidence of black point disease in random samples can be determined. Five indicators, Q seg , S r , Precision, Recall, and F-measure, were used to evaluate the segmentation effects. The average accuracy of segmentation results for the testing samples were 0.85, 0.89, 0.87, 0.86, and 83% respectively. Compared with other segmentation approaches, including the excess green method, the excess green minus excess red method, the color index of vegetation extraction, and two traditional threshold segmentation methods known as Otsu and maximum entropy threshold, the proposed algorithm had greater segmentation accuracy. Moreover, this method was demonstrated to be robust enough to be used for different illumination conditions, shooting angles, and image resolutions. [ABSTRACT FROM AUTHOR]

• Intermittent disturbance favored the colony morphology of microcystis. • Intermittent disturbance promoted the biomass of microcystis. • Intermittent disturbance favored the dominance of microcystis. Wind-driven wave disturbance is one of the environmental factors that shapes the formation of Microcystis blooms. Here we present data on the effect of different disturbance modes (continuous vs intermittent disturbances) on colony size, biomass and dominance of Microcystis in Lake Taihu under field conditions. Small submersible pumps were used to simulate different disturbance modes at turbulent dissipation rate (ε) of 2.98 × 10−6 m2 s–3. Our results show that the mean colony sizes of Microcystis in intermittent and continuous disturbance group were 1.94 and 1.23 times that of the control group, respectively. The mean densities of Microcystis in intermittent and continuous disturbance group were 4.23 and 2.91 times that of the control group, respectively. The mean proportion of Microcystis to total algae abundance in control group and continuous disturbance group changed from 78.3% at beginning of the experiment to 4.5% and 9.1% at the end of the experiment. However, the proportion of Microcystis to total algae cells in intermittent disturbance group was 65.7–94.3% during the whole experiment. The results demonstrated intermittent disturbances favored colony morphology, biomass and dominance of Microcystis. Our results suggested that intermittent disturbance benefited the formation of Microcystis bloom and was important in the development of predictive models for toxic cyanobacterial blooms under changing climates in shallow lakes. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Nanoflower-like NiCo 2 O 4 is grown directly on a three-dimensional gelatin-based carbon–nickel foam (GCNF), which is prepared by a facile hydrothermal strategy with annealing treatment. The as-synthesized binder-free NiCo 2 O 4 /GCNF composites are used for high-performance supercapacitors. The binder-free design can avoid the "dead surface" caused by binders and additives in traditional electrodes, thus achieving more efficient charge and mass exchange, leading to improved conductivity of electrodes. Furthermore, the proposed design provides a wider interface area and abundant active sites, which significantly improve the electrochemical capacitance performance. The electrode has a high specific capacitance of 1416 F g−1 at a current density of 1 A g−1, and excellent cycling performance. An asymmetric supercapacitor (ASC) was assembled with the structure NiCo 2 O 4 /GCNF//activated carbon and a maximum voltage of 1.5 V delivers a high energy density of 48.6 W h kg−1 at a power density of 749.3 W kg−1. Moreover, the ASC shows good cycling performance with 88.5% capacitance retention after 10,000 cycles at a high rate of 5 A g−1. Image 1 • Nanoflower-like NiCo 2 O 4 was grown on a 3D gelatin-based carbon–nickel foam. • Edible gelatin as a carbon source and contributes to the capacitance of electrode. • The NiCo 2 O 4 /GCNF composites were used as binder-free electrodes for supercapacitors. • The assembled asymmetric supercapacitor exhibits good electrochemical performance. [ABSTRACT FROM AUTHOR]

• In orchards, acquiring the maximum data possible is essential for growers. • Propose a deep learning model to help growers measure and acquire crop data. • Construct a large apple tree detection, segmentation, and measurement dataset. • The proposed model achieved a high count accuracy of 94.1% and a high segmentation accuracy of 97.1%. • The average measurement accuracy for the proposed model is over 92%. Manual measurement and visual inspection is a common practice for acquiring crop data in orchards and is a labor-intensive, time-consuming, and costly task. Accurate and rapid acquisition of crop data is vital for monitoring the dynamics of tree growth and optimizing farm management. In this work, we present a technique for orchard data acquisition and analysis that uses remote imagery acquired from unmanned aerial vehicles (UAVs) combined with deep learning convolutional neural networks to automatically detect and segment individual trees and measure the crown width, perimeter, and crown projection area of apple trees. By using an UAV platform, 50 high-resolution images of apple trees were collected from an orchard during dormancy (bare branches), and then each apple tree was detected by using a Faster R-CNN object detector. Based on these results, each tree was segmented by using a U-Net deep learning network. After convex tree boundaries were extracted from the semantic segmentation results by using an efficient pruning strategy, the crown parameters were automatically calculated, and the accuracy was compared with that obtained by manual delineation. The results show that the proposed remote sensing technique can be used to detect and count apple trees with precision and recall of 91.1% and 94.1%, respectively, segment their branches with an overall accuracy of 97.1%, and estimate crown parameter with an overall accuracy exceeding 92%. We conclude that this method not only saves labor by avoiding field measurements but also allows growers to dynamically monitor the growth of orchard trees. [ABSTRACT FROM AUTHOR]

• A framework for hyperspectral remote sensing of crop N status is presented. • Recommendations on the choice of crop N status indicators are given. • Inconsistency in selected wavebands by different methods is illustrated. • Determination of optimal mother wavelet, scale and wavelength is discussed. • Pros and cons of crop N retrieval methods are summarized and analyzed. Nitrogen (N) is the most limiting nutrient for cereal crop production, which often results in over-application of N fertilization to maximize crop yield. Negative environmental impacts and long-term reductions in productivity has encouraged site-specific N fertilization approaches, but these require timely and accurate crop N monitoring. The advent of hyperspectral remote sensing potentially provides a fast and economic way to accomplish this. A framework for hyperspectral remote sensing of cereal crop N is introduced, based on a comprehensive literature survey, to help inform monitoring best practices. Existing and potential crop N status indicators are summarized, with some recommendations provided. Hyperspectral analysis techniques for extracting N-related features are also examined and categorized into spatial domain and frequency domain based methods. In-depth analyses are conducted regarding: (1) the inconsistency in selected wavebands by different band selection methods and (2) determination of optimal wavelet, scale and wavelength in continuous wavelet transformations. Characteristics and deployment of machine learning based regression methods are also presented for crop N monitoring. Further, existing strategies to alleviate the ill-posed problem in physical and hybrid methods are outlined with some examples. Finally, the strengths and weaknesses of crop N retrieval methods are summarized to improve the understanding of how these methods affect prediction quality. Existing limitations and future areas of research emphasize on the fusion of crop N-related features from different domain spaces and the improved combination of empirical and physical methods. [ABSTRACT FROM AUTHOR]

Abstract The increased frequency and intensity of insect-induced forest disturbances necessitates effective methods to precisely monitor and map the degree of disaster. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technology for surveying and monitoring forest health. In this article, a novel framework that utilizes a UAV-based hyperspectral image is proposed to identify the degree of damage caused by Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) in Jianping county of Liaoning province, China. First, data reduction of the hyperspectral image is achieved by comparing three waveband selection algorithms: principal components analysis (PCA), the successive projection algorithm (SPA), and the instability index between classes (ISIC). On this basis, a joint algorithm, ISIC-SPA, which demonstrates the best waveband selection efficiency and good cross-validation accuracy, is proposed. ISIC-SPA is used to select only three sensitive wavebands from 125 original wavebands with a root mean square error of 0.1535. Then, according to analysis of the three sensitive wavebands' reflectance and the corresponding defoliation rate, the piecewise index (PI, B(710 + 738 - 522)) was constructed and the threshold of PI was found to divide the defoliation level. Finally, a piecewise partial least-squares regression model was established to quantitatively estimate the defoliation using the optimal wavebands to identify and demarcate the damage level to individual trees. The assessment accuracy of damage caused by D. tabulaeformis at the tree level reached 95.23% using the ISIC-SPA-P-PLSR framework. Highlights • A UAV-based hyperspectral image is used to estimate defoliators' damage. • A joint algorithm, ISIC-SPA, is proposed to select the optimum sensitive wavebands. • A P-PLSR model using three wavebands is provided to assess damage at tree level. • A piecewise index with three optimum wavebands is constructed. • The proposed ISIC-SPA-P-PLSR framework is effective for damage degree evaluation. [ABSTRACT FROM AUTHOR]

• The averaged dry stem mass content (C sm) is defined. • The crop leaf dry matter content (C m), height (C h), and density (C d) were considered. • C sm , C h , and C d improved vertically growing crop stem AGB (VGC-AGB) estimation. • The VGC-AGB = LAI C m + C d C h C sm did not saturate at medium-to-high crop covers. The accurate estimation of crop above-ground biomass (AGB) can assist in crop growth monitoring and grain yield prediction. Remote sensing has been widely used for AGB estimation at regional and local scales in recent years. However, optical remote sensing spectral indices (SIs) become saturated at medium-to-high crop covers. The combined use of remote sensing techniques and statistical regression models is not based on an understanding of how crop leaves and vertical organs contribute to the crop AGB. This causes difficulties in measuring the biomass stored in vertical organs (e.g., plant stem, wheat-spike, maize-tassel; abbreviated as AGB v) using optical remote sensing. This study aims to develop an unmanned aerial vehicle (UAV)-based vertically growing crop AGB (VGC-AGB) model. We defined C sm (g/m) to describe the crop stem and reproductive organs' average dry mass content. This was done to improve the estimation of AGB v. The crop leaf area index (LAI, m2/m2), leaf dry matter content (C m , g/m2), height (C h , m), and density (C d , m−2) were used in the VGC-AGB. The VGC-AGB calculated crop leaf AGB (AGB l) using LAI × C m (g/m2) and AGB v using C d × C h × C sm (g/m2). The proposed VGC-AGB (AGB = LAI × C m + C d × C h × C sm) was verified using field and UAV-based hyperspectral datasets of winter-wheat and summer-maize at three growth stages. Our results indicate that UAV-based VGC-AGB (R 2 = 0.92–0.93, RMSE = 68.82–75.15 g/m2) is superior to the statistical regression model that is based on remote sensing SIs and CSMs (R 2 = 0.77, RMSE = 134.94 g/m2). The results indicate that the UAV-based VGC-AGB supports the analysis of crop photosynthetic product transfers and high-performance UAV-based high-performance non-destructive AGB monitoring. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of winter wheat yield at a regional scale is crucial for national food policy and security assessments. Near-infrared reflectance is not sensitive to the leaf area index (LAI) and biomass of winter wheat at medium to high canopy cover (CC), and most of the vegetation indices displayed saturation phenomenon. However, LAI and biomass at medium to high CC can be efficiently estimated using imaging data from radar with stronger penetration, such as RADARSAT-2. This study had the following three objectives: (i) to combine vegetation indices based on our previous studies for estimating CC and biomass for winter wheat using HJ-1A/B and RADARSAT-2 imaging data; (ii) to combine HJ-1A/B and RADARSAT-2 imaging data with the AquaCrop model using the particle swarm optimization (PSO) algorithm to estimate winter wheat yield; and (iii) to compare the results from the assimilation of HJ-1A/B + RADARSAT-2 imaging data, HJ-1A/B imaging data, and RADARSAT-2 imaging data into the AquaCrop model using the PSO algorithm. Remote sensing data and concurrent LAI, biomass, and yield of sample fields were acquired in Yangling District, Shaanxi, China, during the 2014 winter wheat growing season. The PSO optimization algorithm was used to integrate the AquaCrop model and remote sensing data for yield estimation. The modified triangular vegetation index 2 (MTVI2) × radar vegetation index (RVI) and the enhanced vegetation index (EVI) × RVI had good relationships with CC and biomass, respectively. The results indicated that the predicted and measured yield ( R 2 = 0.31 and RMSE = 0.94 ton/ha) had agreement when the estimated CC from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model. When the estimated biomass from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model, the predicted yield showed agreement with the measured yield ( R 2 = 0.42 and RMSE = 0.81 ton/ha). These results show that using the biomass as the dynamic input variable provides a better yield estimation than using the CC as the dynamic variable. The predicted biomass and yield were more accurately estimated by combining the HJ-1A/B and RADARSAT-2 data with the AquaCrop model than by combining the only HJ-1A/B or RADARSAT-2 data with the AquaCrop model using the PSO algorithm. The results indicated that the PSO-based assimilation method could be used to estimate the winter wheat yield from the spot to the regional scale. [ABSTRACT FROM AUTHOR]

Crop failure detection using UAV images is helpful for precision agriculture, enabling the precision management of failure areas to reduce crop loss. For wheat failure area detection at the seedling stage using UAV images, the commonly used methods are not sufficiently accurate. Thus, herein, a new tool for precision wheat management at the seedling stage is designed. For this purpose, field experiments with two wheat cultivars and four nitrogen (N) treatments were conducted to create different scenarios for the failure area, and multispectral UAV images were acquired at the seedling growth stage. Based on the above data, a new failure detection method was designed by assimilating prior knowledge and a filter analysis strategy and compared with classical filter-based methods and Hough transform-based methods for wheat failure area detection. The results showed that the newly proposed assimilation method had a detection accuracy between 83.86% and 97.67% for different N levels and cultivars. In contrast, the filter-based methods and Hough transform-based methods had detection accuracies between 53.73% and 83.95% and between 20.71% and 75.79%, respectively. Thus, the assimilation method demonstrated the best failure detection performance. • An assimilation method was proposed for wheat failure area detection. • Different methods were compared for wheat failure area detection. • Wheat failure area can be detected using unmanned aerial vehicles imagery. [ABSTRACT FROM AUTHOR]

The fundamental basis for applying remote sensing methods to crop assessments is the interrelationships between crop features and canopy spectral characteristics. Thus, a thorough understanding of the differences in the distributions of crop variables among different vertical layers and modules, as well as the effects of these layers and modules on canopy spectral characteristics and derived vegetation indices, is vital to improving the performance of crop monitoring by remote sensing. The main goal of this work was to provide insight into the above issues based on field measurements of winter wheat under different treatments during anthesis. The results demonstrated that the leaf area index (LAI), leaf chlorophyll a and b content (Chl a + b ), nitrogen (N) content per unit leaf area (N L ), N concentration on a mass basis (N M ), dry matter accumulation (DM), N accumulation (NA), and water content (W c ) in leaves and stems generally exhibited great vertical differences within wheat canopies during anthesis. There were also significant differences in N M , DM, NA, and W c among the different plant modules (i.e., leaves, stems, and spikes) and the entire canopy. Moreover, the removal of wheat spikes and different vertical leaf layers had evident effects on the canopy spectral reflectance, and there were differences in the types and degrees of these effects at different wavelengths and treatments. In most cases, the vegetation indices decreased after removing wheat spikes and different leaf layers, although a few indices increased. There were also some vegetation indices that responded weakly to removal events. In comparison, the vegetation indices mostly showed stronger responses to the removal of the top 1st leaves than the spikes. To a certain extent, the responses to the removal of the top 2nd leaves were comparable to those of the top 1st leaves. The responses of most vegetation indices to the top 3rd leaves' removal were less than the responses to the top 2nd leaves' removal. In the case of the removal of the top 4th leaves and below, many of the R V (i.e., relative variation rate) absolute values of vegetation indices were greater than those of the top 3rd leaves' removal but smaller than or close to those of the top 2nd leaves' removal; this may partly be because all the withered and dead leaves covering the soil surface were also removed in this case. The above findings indicated that crop features may not be well characterized when they are based solely on leaves, especially only the upper leaves, as the lower leaves, stems, and spikes also have important effects. Moreover, wheat spikes, not just leaves, and even bottom-layer leaves, could have evident effects on canopy reflectance characteristics and derived vegetation indices. More effort is needed in future research to obtain a thorough understanding of the effects of canopy heterogeneity on canopy spectral characteristics and the relationships between crop variables and spectral vegetation indices. [ABSTRACT FROM AUTHOR]

• Meteorological data was introduced to build the HLM. • An AGB model for different regions was proposed and validated. • HLM models provided better AGB estimation at different regions. Above-ground biomass (AGB) is an important agronomic indicator that reflects crop growth and estimates yield. The AGB estimation using remote sensing becomes a non-destructive, rapid, and alternative method to post-harvest laboratory measurements. However, most of the AGB estimation models constructed based on remote sensing data are difficult to expand regionally, which limits the applicability of the models. This study combined ground-based hyperspectral and meteorological data by using a hierarchical linear modeling (HLM) to construct an AGB estimation model that was generalized across different regions. Experimental data from both regions were acquired and validated, namely from Xiaotangshan Experimental Base, Beijing, 2019 (North China) and Keshan Farm, Qiqihar Branch, Heilongjiang General Bureau of Reclamation, 2022 (Northeast China). Compared to OLS, RFR and GRU, the HLM method was better for estimating potato AGB in different regions with R2 = 0.54, RMSE = 429.62 kg/hm2, NRMSE = 28.20 %. The results of this study demonstrated that HLM could be used as a powerful method to improve the transferability of AGB estimation at different regions. [ABSTRACT FROM AUTHOR]

• The optimized vegetation indices (Opt-VIs) could resist spectral saturation. • Sensitive wavelengths in response to potato AGB were 734 and 742 nm. • Opt-VIs combined with texture features improved potato AGB estimation. Above-ground biomass (AGB) estimation is critical for monitoring crop growth and assessing yields. Unmanned aerial vehicle (UAV) optical remote sensing technology offers robust support for crop AGB estimation through vegetation indices (VIs). However, under conditions of high nitrogen or high AGB, most VIs lose their response to the presence of a dense plant canopy. To address the inaccuracy of estimating crop multistage AGB based on VIs formed in two bands, a UAV imaging hyperspectral experiment was conducted on potato test plots with different varieties, planting densities, and fertilizer application gradients. The ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) were initially modified using a lambda-by-lambda optimization algorithm, which yielded the optimized RVI (Opt-RVI) and NDVI (Opt-NDVI). Their performances were compared with published VIs. Then, the corresponding bands from optimized VIs were used to extract textural features and their estimation performances were evaluated. Finally, based on published VIs, Opt-VIs, textures of sensitive bands, and Opt-VIs combined with the textures, the potato multistage AGB was estimated by optimized random forest (RF) regression. The RF estimation model was simplified according to variable importance scores. Results showed the spectral regions sensitive to AGB were mainly located in the red-edge range. The wavelengths of Opt-RVI and Opt-NDVI that exhibited the strongest correlation with AGB were 734 and 742 nm. Except for the normalized red-edge index formed by two red-edge bands, the published VIs (R2 = 0.07–0.28) demonstrated saturation in the context of high AGB. The optimized Opt-RVI and Opt-NDVI suppressed this phenomenon, such that R2 values for both reached 0.44 for estimating AGB. The R2 values of textural features relative to AGB for both bands ranged from 0.06 and 0.31, and CON 734 , COR 734 , DIS 742 , ENT 742 , SEC 742 , and COR 742 exhibited the strongest correlations with AGB (R2 > 0.2). The performance of estimating potato AGB by a single indicator was sensitive texture features, Opt-VIs and published VIs from high to low and the estimation capacities of these models were limited. The combination of Opt-VIs and textural features of sensitive bands demonstrated the greatest estimation accuracy (R2 = 0.62 and RMSE = 293.08 kg/hm2). Based on variable importance scores, the accuracy of the simplified RF estimation model (four variables: Opt-RVI, CON 734 , ENT 742 , COR 742) slightly decreased (R2 = 0.59 and RMSE = 301.01 kg/hm2), but model complexity was reduced and computational efficiency was improved. The simplified model accuracy was mainly affected by potato variety, fertilizer gradient, and growth stage, but not by planting density. The results of this study can be used as a reference for potato growth monitoring in the field. [ABSTRACT FROM AUTHOR]

• Findings indicate a direct binding between PPARγ and miR-130b-3p. • exogenous overexpression of miR-130 b-3 p resulted in the decrease of ATG5, Becline and LC3B II/I. • MiR-130b-3p/PPARγ contributed to the premature senescence of these H 2 O 2 -induced HEI-OC1 cells. The study focuses on the underlying regulatory mechanism of age-related hearing loss (ARHL), which results from autophagy dysregulation mediated by miR-130b-3p targeting PPARγ. We constructed miR-130b-3p knockout (antagomir) and PPARγ over-expression (OE-PPARγ) mice model by injecting mmu-miR-130b-3p antagomir and HBAAV2/Anc80-m-Pparg-T2A-mCHerry into the right ear' round window of each mouse, respectively. In vitro, we introduced oxidative stress within HEI-OC1 cells by H 2 O 2 and exogenously changed the miR-130b-3p and PPARγ levels. MiRNA level was detected by RT-qPCR, proteins by western blotting and immunohistochemistry. Morphology of autophagosomes was observed by electron microscopy. In vivo, the cochlea of aged mice showed higher miR-130b-3p expression and lower PPARγ expression, while exogenous inhibition of miR-130b-3p up-regulated PPARγ expression. Autophagy-related biomarkers expression (ATG5, Beclin-1 and LC3B II/I) decreased in aged mice, which reversely increased after the inhibition of miR-130b-3p. The elevation of PPARγ demonstrated similar effects. Contrarily, exogenous overexpression of miR-130b-3p resulted in the decrease of ATG5, Beclin-1 and LC3B II/I. We created oxidative stress within HEI-OC1 by H 2 O 2 , subsequently observed the formation of autophagosomes under electron microscope, so as the elevated cell apoptosis rate and weakened cell viability. MiR-130b-3p/PPARγ contributed to the premature senescence of these H 2 O 2 -induced HEI-OC1 cells. MiR-130b-3p regulated HEI-OC1 cell growth by targeting PPARγ, thus leading to ARHL. [ABSTRACT FROM AUTHOR]

• Elucidated the possibility of using above-ground parameter to monitor yield of potato. • HLM was used to establish connections among VIs, EVs, and potato yield. • EVs successfully corrected inter-annual and regional variability in VIs-yield relationship. Timely monitoring of potato yield status is crucial for guiding field management and ensuring the sustainability of food trade. In recent years, non-destructive remote sensing techniques have emerged as one of the most promising methods for crop yield monitoring. However, most existing potato yield estimation models have primarily been tested at specific growth stages within a single ecosystem, highlighting the urgent need for a model that can be applied across different ecological environments. In this study, we developed a semi-physical model based on hierarchical linear modeling (HLM) to automatically correct yield estimation based on remotely sensed vegetation indices using environmentally relevant variables such as net solar radiation at the surface (ssr), air temperature at 2 m above the surface (t2m), and soil water content in the root zone (swvl). The accuracy and transferability of the model were further evaluated by comparing it with aboveground destructive sampling methods, conventional empirical models, and machine learning approaches. The results demonstrated that site-specific relationships exist between aboveground agronomic traits parameters like biomass and leaf area index (LAI) with respect to yield estimation. Conventional linear statistical and machine learning methods faced challenges in transferring their constructed yield estimation models across regions and years. However, the HLM approach exhibited excellent generalization ability across all trial samples. In the middle and later stages of potato growth, the standardized LAI determining index proved to be the most effective vegetation for predicting yield, while ssr, skt max , and shallow swvl were identified as key environmental variables affecting yield. By integrating these variables using HLM modeling techniques, we achieved optimal generalization performance (R2 = 0.57, RMSE = 7.65 t/ha, NRMSE = 25.02 % during tuber growth stage; R2 = 0.60, RMSE = 7.37 t/ha, NRMSE = 24.13 % during starch accumulation stage). This study underscores the importance of considering environmental factors when constructing a reliable model for estimating potato yield at different spatial and temporal scales using remote sensing technology. [ABSTRACT FROM AUTHOR]

Abstract: Leaf area index (LAI) is a major indicator for crop growth monitoring and yield estimation. Data assimilation as an effective tool for crop LAI estimation fully considers the properties of actual observations and physical model simulations. In this work, we present a new data assimilation scheme, introducing a very fast simulated annealing (VFSA) optimization algorithm into the process of crop LAI assimilation with a four dimensional variational data assimilation (4DVAR) algorithm. Firstly, calibrating the input parameters of a crop growth simulation model based on history observations. Secondly, quantitatively describing the relationship between fields observed data and model simulated data by the cost function of 4DVAR algorithm. Finally, the optimization process of the cost function is accomplished by the VFSA optimization algorithm, and further the optimal solution is taken as the best combination of input parameters of physical model for LAI estimation. Winter wheat in Beijing is taken as an experimental object. The numerical results show not only the improved time efficiency of this proposed assimilation scheme, but also enhanced assimilation accuracy of all LAI assimilations, especially for LAI 3.00. Theoretical analysis and practical experiments confirm the application prospect of VFSA optimization algorithm in LAI variational assimilation. [Copyright &y& Elsevier]

• A hyperspectral data-processing framework was developed using Boruta and DNN. • Hyperspectral bands that can estimate the LNC of apple trees were explicitly identified. • Investigated the potential of phenological data to enhance apple LNC estimation. Rapid and accurate assessment of apple-tree leaf nitrogen content (LNC) plays an important role in smart orchards for accurate fertilization. Hyperspectral remote sensing technology provides a reliable, economical, and fast method to estimate LNC. However, high-dimensional spectral data contain redundant information that is irrelevant to the target parameters, necessitating the development of robust hyperspectral analysis techniques to estimate apple-tree LNC. Additionally, the role of phenological information in improving LNC estimates across multiple growth stages remains uncertain. The present study thus proposes a novel hyperspectral data-processing framework (Boruta-Iteration-DNN) to optimize estimates of apple-tree LNC and investigates the potential of phenological information to improve LNC estimates of multi-fertility apple trees. The proposed framework incorporates the Boruta feature-selection method for identifying important spectral bands and assessing how they affect model performance through iterative feature analysis. A deep neural network (DNN) regression model is then developed for LNC estimation. In addition, a widely used Pearson-DNN method is used for comparison to demonstrate the superiority of the proposed approach. Furthermore, the potential of incorporating phenological information (Day After Anthesis, DAA) into the estimation models is examined to evaluate its ability to better estimate LNC. The results demonstrate that the proposed framework not only enhances the accuracy of apple-tree LNC estimates with respect to the full-spectrum model (without DAA: validation R2 improved from 0.69 to 0.75, NRMSE reduced from 11.92 % to 9.97 %; with DAA: validation R2 improved from 0.72 to 0.79, NRMSE reduced from 11.09 % to 9.06 %), but also reduces the complexity and dimensionality of the models by eliminating 96 % of the redundant spectral bands (2015 bands). Moreover, the inferior results of the Pearson-Iteration-DNN framework further underscore the effectiveness of the Boruta method in selecting relevant spectral bands. Additionally, including the DAA phenological information enhances the accuracy of all models (the total R2 of training and validation increased by 0.05 ∼ 0.12, and the total NRMSE reduced by 1.49 % ∼ 1.93 %). These findings highlight the ability of DNN models integrated with hyperspectral data and phenological information to accurately assess LNC in apple trees, thereby offering valuable guidance to orchard management for developing precise fertilization strategies. [ABSTRACT FROM AUTHOR]

• Deep features were extracted by combining SPA and LSTM. • The AGB estimation performance of spectral and deep features was compared. • The accuracy of the SPA-LSTM model across years and growth stages was evaluated. The accurate estimation of above ground biomass (AGB) is valuable in grasping the status of potato growth and assessing yield. Spectral analysis techniques play an important role in AGB estimation by virtue of its non-destructive and rapid advantages. However, the models constructed based on spectral features at the entire growth stages due to differences in phenological stages, such as vegetation indices (VIs) and locations at fixed wavelengths, can be not applicable to single growth stages. Therefore, the main objective of this study is to improve the applicability of the AGB estimation model to mitigate the impacts of phenological stages by mining depth features from hyperspectral data. The canopy hyperspectral and AGB data of potato tuber formation, tuber growth and starch accumulation stages were measured in 2017–2019. The acquired canopy hyperspectral reflectance was smoothed by Savitzky-Golay (SG) filter, and the green edge (GE), the red edge (RE), red edge chlorophyll index (CIred-edge), optimized soil adjusted vegetation index (OSAVI), green normalized difference vegetation index (GNDVI), and normalized difference vegetation index (NDVI) were extracted. The spectral changes were analyzed at different growth stages of each year. To obtain features in response to AGB changes, a new approach is proposed to analyze the hidden depth features between sensitive wavelengths by combining a successive projections algorithm (SPA) and a long-short-term memory network (LSTM). The AGB estimation models were constructed by partial least squares regression (PLSR) based on traditional VIs + GE + RE, full spectrum, sensitive wavelengths obtained by SPA, and depth features obtained by SPA-LSTM. The results showed that GE, RE and VIs alone explained only 24 %-36 % of the variation in AGB. The accuracy of estimating AGB at sensitive wavelengths obtained after SPA was higher (R2 = 0.70, RMSE = 406 kg/hm2, and NRMSE = 27.14 %) than that of conventional VIs + GE + RE (R2 = 0.51, RMSE = 536 kg/hm2, and NRMSE = 35.81 %) and full spectrum (R2 = 0.63, RMSE = 452 kg/hm2, and NRMSE = 30.19 %) in the three-year dataset. The depth features extracted by the SPA-LSTM method proposed in this study achieved the best AGB estimation (R2 = 0.82, RMSE = 318 kg/hm2, and NRMSE = 21.25 %). The applicability of the model was validated in different years and growth stages. The R2 of each year was in the range of 0.48–0.77, the RMSE was in the range of 290–367 kg/hm2, and the NRMSE was in the range of 16.69 %-25.81 %. The R2 of each growth stage within three year was in the range of 0.48–0.78, the RMSE was in the range of 184–386 kg/hm2, and the NRMSE was in the range of 14.91 %-29.45 %. The method proposed in this study mitigates the impacts of phenological stage and improves the robustness and accuracy of the AGB estimation model, which provides remote sensing technical support for realizing potato growth monitoring and yield assessment in the field. [ABSTRACT FROM AUTHOR]

• Coupling HLM method with remote sensing to estimate rice yield and quality. • Propose a modeling methed combining remote sensing with meteorological data. • A model was proposed with a better scalability at interannual and regional scales. Timely and accurate estimation of crop yield and quality can provide a practical and effective basis for the formulation of national food policies. Based on remote sensing data of rice multi-growth stages from 2021 to 2022 in Ninghe District of Tianjin, the methods coupling hierarchical linear model (HLM) with typical vegetation indices and meteorological data were used to estimate yield and quality in rice. The results showed that: (1) Compared with the multiple linear regression model, the methods of estimating rice yield and quality based on HLM had higher stability in interannual expansion; (2) For the three growth stages of rice, namely, jointing, booting and filling stage, the hierarchical linear model of yield and quality estimation at booting stage showed the highest performance. The R^2 of the GRVI rice yield estimation model, MSR rice amylose content estimation model, and RDVI rice protein content estimation model were 0.52, 0.70, and 0.88, and RMSE were 1.17 t/ha, 0.85 %, and 0.33 %, respectively. The nRMSE was 11.06 %, 4.46 %, and 3.37 %, respectively. The HLM rice yield and quality estimation models based on multi-growth period data were further established. The GCI rice yield estimation model, MSR amylose content estimation model, and DVI protein content estimation model R^2 were 0.67, 0.75, and 0.91, and RMSE were 1.01 t/ha, 0.78 %, and 0.30 %. The nRMSE was 9.48 %, 4.07 %, and 3.05 %. It indicates that the method using HLM has a good potential for accessing yield and quality in rice and shows better scalability at interannual and regional scales. [ABSTRACT FROM AUTHOR]

• The AGB estimation performance of ASD and UHD185 was compared. • HD was employed to improve spectral sensitivity to AGB. • The AGB spatial distribution map was created. Accurately estimating potato above-ground biomass (AGB), which is closely associated with the growth and yield of crops, carries significant importance for guiding field management practices. Hyperspectral techniques have emerged as a powerful and efficient tool for quickly and non-invasively acquiring information about AGB due to its capability to provide rich spectral data closely related to crop physiology and biochemistry. However, using spectral features obtained from hyperspectral data, such as spectral reflectance and vegetation indices (VIs), often leads to inaccurate estimations of crop AGB at multiple growth stages due to spectral saturation effects and dynamic changes in spectral responses. To enhance the robustness of AGB estimation models, this study proposed a harmonic decomposition (HD) method derived from Fourier series to extract energy features. The ground (referred to as ASD) and unmanned aerial vehicle hyperspectral (referred to as UHD185) remote sensing data from three growth stages of potatoes in 2018 (validation set) and 2019 (calibration set) were utilized in the study. Firstly, a comparison was made between the spectral reflectance of the potato canopy measured by the ASD and UHD185 sensors. Subsequently, the correlation between spectral reflectance, VIs, and harmonic components obtained from ASD and UHD185 sensors was analyzed in relation to AGB at both the individual and whole growth stage. Then, sensitive bands selected through CARS (competitive adaptive reweighted sampling), the entire spectral reflectance, VIs, and harmonic components, were utilized to construct AGB estimation models by partial least squares regression (PLSR). Finally, the optimal model performance was validated across different years, growth stages, and treatment conditions. The results showed there were differences in spectral reflectance acquired by ASD and UHD185 sensors across various wavelengths, but overall, there was a high level of consistency between the two. The correlation of spectral reflectance and VIs with potato AGB at individual growth stage was notably higher than that observed for entire growth stages. The accuracy of AGB estimation using VIs obtained from ASD (the R2, RMSE and NRMSE of validation sets were 0.52, 592 kg/hm2 and 26.91 %, respectively) and UHD185 (the R2, RMSE and NRMSE of validation sets were 0.46, 612 kg/hm2 and 27.82 %, respectively) sensors were low. Utilizing sensitive bands and full spectral reflectance separately improved the precision of models, although the enhancement was somewhat limited. The HD-PLSR models from ASD (the R2, RMSE and NRMSE of validation sets were 0.69, 477 kg/hm2 and 21.69 %, respectively) and UHD185 (the R2, RMSE and NRMSE of validation sets were 0.66, 481 kg/hm2 and 21.86 %, respectively) achieved the best AGB estimation results. Using the HD-PLSR model to estimate AGB for two years, the R2 values were 0.79 and 0.76 for ASD and UHD185, with RMSE values of 381 kg/hm2 and 386 kg/hm2 and NRMSE values of 22.35 % and 22.70 %, respectively. The capability of the HD-PLSR model was confirmed at various growth stages and treatments. This work offers valuable remote sensing technical support for implementing potato growth monitoring and yield assessment in the field. [ABSTRACT FROM AUTHOR]

• Late Blight in Potato, a prevalent potato disease, significantly impacts potato yield and stands as one of the principal afflictions affecting the potato crop. Timely and effective monitoring of late blight severity in potato and its spatial distribution has become an immediate priority. In the studies we know of, the research focus on late blight in potato monitoring still predominantly revolves around single-sensor approaches, such as multispectral UAV monitoring and hyperspectral UAV monitoring. However, as of now, there remains a lack of research utilizing the collaborative monitoring of potato late blight using multiple sensors on drones. By integrating the vegetation index derived from the multispectral UAV, the texture index from the RGB UAV, and the estimated crown coverage feature, we combined the Relief-mRmR technique with machine learning modeling algorithms to monitor late blight in potato. We compared and constructed severity monitoring models for late blight in potato using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) classification algorithms. The results indicate that the RF model exhibited the highest accuracy. For the training dataset, the overall accuracy (OA) and kappa coefficient were 92.50% and 0.90, respectively, while for the independent validation dataset, the OA and kappa coefficient reached 97.50% and 0.96, respectively. Late Blight in Potato, a prevalent potato disease, significantly impacts potato yield and stands as one of the principal afflictions affecting the potato crop. Timely and effective monitoring of late blight severity in potato and its spatial distribution has become an immediate priority. In this study, we employed a dual-drone collaborative approach, utilizing the multispectral drone from DJI as well as an ultra-high-resolution RGB drone. By integrating the vegetation index derived from the multispectral UAV, the texture index from the RGB drone, and the estimated crown coverage feature, we combined the relief-mRmR technique with machine learning modeling algorithms to monitor late blight in potato. We compared and constructed severity monitoring models for late blight in potato using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) classification algorithms. The results indicate that the RF model exhibited the highest accuracy. For the training dataset, the overall accuracy (OA) and kappa coefficient were 92.50% and 0.90, respectively, while for the independent validation dataset, the OA and kappa coefficient reached 97.50% and 0.96, respectively. The findings also demonstrate that augmenting the vegetation index and texture index with the estimated high-resolution plant crown coverage information significantly enhances the accuracy of the late blight in potato monitoring model. [ABSTRACT FROM AUTHOR]

• We developed an RRCNet that fuses the deep and shallow spectral features to map RRC. • RRCNet was trained based on an RTM-based "soil-crop-crop residue" spectra dataset. • Use transfer learning (TL) to recalibrate the fully connected layer of RRCNet. • RRCNet + TL can provide more accurate RRC estimates than SI + statistical regression. Accurate determination of rice residue cover (RRC) can improve the monitoring of tillage information. Currently, the accurate determination of RRC using optical remote sensing is hindered by variations in cropland moisture and cover of following crops. The fractional cover (FC) of the soil (f S), crop (f C), and crop residue (f CR) changes (f S + f C + f CR = 1) after the following crop is planted, which increases the difficulty of remote-sensing RRC estimation. Cropland soil moisture and crop residue moisture affect the values of cropland and crop residue spectral indices (CRSIs), thereby reducing the accuracy of remote-sensing RRC estimation. Deep learning techniques (e.g., convolutional neural networks [CNN] and transfer learning [TL]) have been proven to extract the deep features of input images with distortion invariance, such as displacement and scaling, which are similar to moisture and the following crop effects on remote-sensing CRSIs. This study aimed to evaluate the combined use of deep features of cropland spectra extracted by deep learning techniques to estimate the cropland RRC under the effects of variations in cropland moisture and cover of the following crops. This study proposes an RRCNet CNN that fuses deep and shallow features to improve RRC estimation. A PROSAIL radiative transfer model was employed to simulate a cropland "soil–crop–crop residue" mixed spectra dataset (n = 103,068), considering the variations in cropland moisture and the cover of the following crop. The RRCNet was first pre-trained using the simulated dataset, and then the knowledge from the pre-trained RRCNet was updated based on field experimental FCs, RRCs, and Sentinel-2 image spectra using the TL technique. Our study indicates that RRCNet can incorporate shallow and deep spectral features of cropland "soil–crop–crop residue" mixed spectra, providing high-performance FCs and RRC estimation. The FCs and RRC estimates from RRCNet + TL (FCs: R 2 = 0.939, root mean squared error (RMSE) = 0.071; RRC: R 2 = 0.891, RMSE = 0.083) were more accurate than those from CRSI + multiple linear regression, CRSI + random forest, and CRSI + support vector machine (FCs: R 2 = 0.877–907, RMSE = 0.086–0.101; RRC: R 2 = 0.378–0.714, RMSE = 0.133–0.229). We mapped the multistage RRC based on Sentinel-2 multispectral instrument (MSI) images and RRCNet. Tillage information can be inferred from RRC and RRC difference maps changes. [ABSTRACT FROM AUTHOR]

• AGB was estimated by combining UAV RGB and hyperspectral images. • The ability of different wavelet basis functions to extract features was compared. • The high-frequency information and wavelet coefficients were used to estimate AGB. • The problem of inaccurate estimation of potato AGB using VIs was solved. Crops' above-ground biomass (AGB) is a crucial indicator that reflects crop health and predicts crop yield. However, using only optical vegetation indices (VIs) can produce inaccurate AGB estimates due to differences in crop varieties, growth stages, and measurement environments. Given the advantages of unmanned aerial vehicle (UAV) RGB and hyperspectral image fusion, this study evaluated the performance of multi-source remote sensing data for estimating potato AGB at multiple growth stages. In 2019, this study conducted potato trials with different varieties, fertilization levels, and planting densities at the Xiaotangshan Experiment Base (Beijing). UAV image and AGB data of potato three main stages were obtained from ground survey work. High-frequency information of the potato canopy was extracted from RGB images using discrete wavelet transform (DWT). VIs and wavelet energy coefficients were extracted from hyperspectral images using continuous wavelet transform (CWT). The linear relationships between potato AGB with VIs, high-frequency information, and wavelet coefficients were analyzed. Potato AGB estimation models were constructed based on single and multiple types of variables using multiple stepwise regression (MSR) and random forest (RF) models, respectively. This work showed the following results: (i) High-frequency information and wavelet coefficients were more sensitive to potato multi-growth stage AGB than VIs, and the latter were the most sensitive. (ii) Using VIs, high-frequency information, or wavelet coefficients separately to estimate the potato multi-growth stage AGB resulted in higher error and lower model accuracy. (iii) Combining VIs with either high-frequency information or wavelet coefficients improved the accuracy of AGB estimation, which was further improved by combining high-frequency information with wavelet coefficients. (iv) Combining VIs with both high-frequency information and wavelet coefficients provided the highest estimation accuracy using the MSR method. This combined AGB estimation model reduced the RMSE by 27%, 21%, and 16%, respectively, relative to VIs, high-frequency information, or wavelet coefficients alone. This result shows that the complementary advantages of multi-source UAV data can solve the challenge of insufficient AGB estimation by optical remote sensing. The work in this study provides remote sensing technology support to achieve potato crop growth monitoring and improve yield predictions. [ABSTRACT FROM AUTHOR]

Lodging is a common phenomenon in maize production, which seriously affects its yield, quality, and mechanical harvesting capacity. With good penetrating power, satellite radar can monitor crop growth even under cloudy weather conditions. In this study, a method based on the change in plant height before and after lodging in maize is proposed to calculate the lodging angle and monitor the lodging degree by using dual-polarization Sentinel-1A data. The results show that the optimal sensitive polarization combinations of maize plant height before and after lodging are VH/VV and VV, respectively. The lodging angle is calculated using the plant height inversion results before and after lodging. The overall accuracy of classifying lodging grade of maize is 67%. The proposed model based on lodging angle could effectively mapped the maize lodging range on a regional scale and classify the lodging grades. [ABSTRACT FROM AUTHOR]

• The invariant shape spectral preprocessing method is effective for WYMV detection. • Machine learning has great potential for narrow to wide band migration applications. • The 3SV-SIW-SVM mode is most effective for WYMV detection. Wheat yellow mosaic disease (WYMD) is a low-temperature soil-borne disease that causes serious yield and economic losses. Reflectance spectroscopy has great potential for detecting crop diseases, but it has not been applied for detection of WYMD. Herein, we collected 2 years of wheat physiological and reflectance spectroscopy data at green-up and jointing stages, and conducted disease detection studies. We evaluated standard normal variate (SNV), multiplicative scatter correction (MSC), and spectral separation of soil and vegetation (3SV) as preprocessing methods for invariant spectral shapes, and 96 spectral indices. The spectral index wavelength (SIW), threshold method, and variance inflation factor (TVIF) were used as feature selection methods combined with machine learning to construct a systematic disease detection scheme. The results showed that the visible region centred at 750 nm was the most sensitive wavelength. Additionally, 900–––1000 nm was the sensitive wavelength in the middle and latter stages, during disease aggravation and fertility development. The preprocessing method was best with 3SV, which was highly consistent with the characteristic regions of the original spectra, followed by SNV and MSC. spectral indices OR-BG1 I, SNV-RPMI, MSC-RPMI and 3SV-CTRI 1 could be applied to detect WYMD, and the best index was 3SV-CTRI 1, with OA and Kappa values of 75.88% and 0.516, respectively. The effectiveness of the SIW feature selection method and coupling with machine learning was superior to TVIF. The best results among different machine learning models were obtained with SVM, followed by RF and KNN. The optimal 3SV-SIW-SVM disease detection mode achieved OA and Kappa values of 94.71% and 0.893, respectively, for field data validation, and 68.06% and 0.381, respectively, for simulated satellite data validation. The findings will facilitate migration of ground-based research to satellite platforms, and provide new ideas for quantitative identification of crop diseases. [ABSTRACT FROM AUTHOR]