Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves

Automatic detection and monitoring of freezing injury in crops is of vital importance for assessing plant physiological status and yield losses. This study investigates the potential of hyperspectral techniques for detecting leaves at the stages of freezing and post-thawing injury, and for quantifying the impacts of freezing injury on leaf water and pigment contents. Four experiments were carried out to acquire hyperspectral reflectance and biochemical parameters for oilseed rape plants subjected to freezing treatment. Principal component analysis and support vector machines were applied to raw reflectance, first and second derivatives (SDR), and inverse logarithmic reflectance to differentiate freezing and the different stages of post-thawing from the normal leaf state. The impacts on biochemical retrieval using particular spectral domains were also assessed using a multivariate analysis. Results showed that SDR generated the highest classification accuracy (> 95.6%) in the detection of post-thawed leaves. The optimal ratio vegetation index (RVI) generated the highest predictive accuracy for changes in leaf water content, with a cross validated coefficient of determination (R2cv) of 0.85 and a cross validated root mean square error (RMSEcv) of 2.4161 mg/cm2. Derivative spectral indices outperformed multivariate statistical methods for the estimation of changes in pigment contents. The highest accuracy was found between the optimal RVI and the change in carotenoids content (R2CV = 0.70 and RMSECV = 0.0015 mg/cm2). The spectral domain 400–900 nm outperformed the full spectrum in the estimation of individual pigment contents, and hence this domain can be used to reduce redundancy and increase computational efficiency in future operational scenarios. Our findings indicate that hyperspectral remote sensing has considerable potential for characterizing freezing injury in oilseed rape, and this could form a basis for developing satellite remote sensing products for crop monitoring

Safety and immunogenicity of the two-dose heterologous Ad26.ZEBOV and MVA-BN-Filo Ebola vaccine regimen in children in Sierra Leone: a randomised, double-blind, controlled trial

Background—Children account for a substantial proportion of cases and deaths from Ebola virus disease. We aimed to assess the safety and immunogenicity of a two-dose heterologous vaccine regimen, comprising the adenovirus type 26 vector-based vaccine encoding the Ebola virus glycoprotein (Ad26.ZEBOV) and the modified vaccinia Ankara vectorbased vaccine, encoding glycoproteins from the Ebola virus, Sudan virus, and Marburg virus, and the nucleoprotein from the Tai Forest virus (MVA-BN-Filo), in a paediatric population in Sierra Leone. Methods—This randomised, double-blind, controlled trial was done at three clinics in Kambia district, Sierra Leone. Healthy children and adolescents aged 1–17 years were enrolled in three age cohorts (12–17 years, 4–11 years, and 1–3 years) and randomly assigned (3:1), via computer-generated block randomisation (block size of eight), to receive an intramuscular injection of either Ad26.ZEBOV (5 × 1010 viral particles; first dose) followed by MVA-BN-Filo (1 × 108 infectious units; second dose) on day 57 (Ebola vaccine group), or a single dose of meningococcal quadrivalent (serogroups A, C, W135, and Y) conjugate vaccine (MenACWY; first dose) followed by placebo (second dose) on day 57 (control group). Study team personnel (except for those with primary responsibility for study vaccine preparation), participants, and their parents or guardians were masked to study vaccine allocation. The primary outcome was safety, measured as the occurrence of solicited local and systemic adverse symptoms during 7 days after each vaccination, unsolicited systemic adverse events during 28 days after each vaccination, abnormal laboratory results during the study period, and serious adverse events or immediate reportable events throughout the study period. The secondary outcome was immunogenicity (humoral immune response), measured as the concentration of Ebola virus glycoprotein-specific binding antibodies at 21 days after the second dose. The primary outcome was assessed in all participants who had received at least one dose of study vaccine and had available reactogenicity data, and immunogenicity was assessed in all participants who had received both vaccinations within the protocol-defined time window, had at least one evaluable post-vaccination sample, and had no major protocol deviations that could have influenced the immune response. This study is registered at ClinicalTrials.gov, NCT02509494. Findings—From April 4, 2017, to July 5, 2018, 576 eligible children or adolescents (192 in each of the three age cohorts) were enrolled and randomly assigned. The most common solicited local adverse event during the 7 days after the first and second dose was injection-site pain in all age groups, with frequencies ranging from 0% (none of 48) of children aged 1–3 years after placebo injection to 21% (30 of 144) of children aged 4–11 years after Ad26.ZEBOV vaccination. The most frequently observed solicited systemic adverse event during the 7 days was headache in the 12–17 years and 4–11 years age cohorts after the first and second dose, and pyrexia in the 1–3 years age cohort after the first and second dose. The most frequent unsolicited adverse event after the first and second dose vaccinations was malaria in all age cohorts, irrespective of the vaccine types. Following vaccination with MenACWY, severe thrombocytopaenia was observed in one participant aged 3 years. No other clinically significant laboratory abnormalities were observed in other study participants, and no serious adverse events related to the Ebola vaccine regimen were reported. There were no treatment-related deaths. Ebola virus glycoprotein-specific binding antibody responses at 21 days after the second dose of the Ebola virus vaccine regimen were observed in 131 (98%) of 134 children aged 12–17 years (9929 ELISA units [EU]/mL [95% CI 8172–12 064]), in 119 (99%) of 120 aged 4–11 years (10 212 EU/mL [8419–12 388]), and in 118 (98%) of 121 aged 1–3 years (22 568 EU/mL [18 426–27 642]). Interpretation—The Ad26.ZEBOV and MVA-BN-Filo Ebola vaccine regimen was well tolerated with no safety concerns in children aged 1–17 years, and induced robust humoral immune responses, suggesting suitability of this regimen for Ebola virus disease prophylaxis in children

Rice biophysical parameter retrieval with optical satellite imagery: a comparative assessment of parametric and non-parametric models

This article presents a comparison of parametric and non-parametric models in rice biomass and leaf area index (LAI) retrieval using optical satellite imagery. Four parametric models including the linear, quadratic, logarithmic and exponential models, and four non-parametric models that include RF, SVM, kNN, and GBDT were applied, respectively, on the optical satellite dataset. GBDT produced the most accurate biomass estimates (RMSE of 191.8 g/m2) before heading and the quadratic model produced the most accurate biomass estimates (RMSE of 364.7 g/m2) after heading. RF registered the most accurate LAI estimates (RMSE of 0.79 m2/m2) before heading, whereas the quadratic model recorded the most accurate LAI estimates (RMSE of 1.04 m2/m2) after heading. Non-parametric models outperformed their parametric counterparts at before heading, whereas the reverse is the case after heading. These findings provide a guide to the optimal choice of empirical models for rice biomass and LAI retrieval with optical imagery

Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery

Geographic object-based image analysis (GEOBIA) has been widely used in the remote sensing of agricultural crops. However, issues related to image segmentation, data redundancy and performance of different classification algorithms with GEOBIA have not been properly addressed in previous studies, thereby compromising the accuracy of subsequent thematic products. It is in this regard that the current study investigates the optimal scale parameter (SP) in multi-resolution segmentation, feature subset, and classification algorithm for use in GEOBIA based on multisource satellite imagery. For this purpose, a novel supervised optimal SP selection method was proposed based on information gain ratio, and was then compared with a preexisting unsupervised optimal SP selection method. Additionally, the recursive feature elimination (RFE) and enhanced RFE (EnRFE) algorithms were modified to generate an improved EnRFE (iEnRFE) algorithm, which was then compared with its precursors in the selection of optimal classification features. Based on the above, random forest (RF), gradient boosting decision tree (GBDT) and support vector machine (SVM) were applied to segmented objects for crop classification. The results indicated that the supervised optimal SP selection method is more suitable for application in heterogeneous land cover, whereas the unsupervised method proved more efficient as it does not require reference segmentation objects. The proposed iEnRFE method outperformed the preexisting EnRFE and RFE methods in optimal feature subset selection as it recorded the highest accuracy and less processing time. The RF, GBDT, and SVM algorithms achieved overall classification accuracies of 91.8%, 92.4%, and 90.5%, respectively. GBDT and RF recorded higher classification accuracies and utilized much less computational time than SVM and are, therefore, considered more suitable for crop classification requiring large numbers of image features. These results have shown that the proposed object-based crop classification scheme could provide a valuable reference for relevant applications of GEOBIA in crop recognition using multisource satellite imagery

Drivers of land-use and land-cover change in rural landscapes; a case in Eastern Sierra Leone

This study provides the first attempt at assessing the drivers of LULC change in eastern Sierra Leone where settlement expansion is as eminent as the proliferation of the primary industrial sector, notably agriculture and artisanal mining. For this purpose, using Landsat images at 1986, 2000 and 2020, LULC maps of the study area were obtained. The principal drivers of LULC change were identified based on field observations and interviews with local residents. Additionally, population and climate data were obtained to better understand LULC changes. Vast changes were observed in Shrub/Fallow, Grass/Cropland, Wetland and Built/Barren. Moderate changes were observed in Trees/Forest and Open Water. The drivers of LULC change are attributable to population growth as the main underlying driver and agricultural expansion and infrastructural development as the main proximate or direct drivers. Moreover, the declining rainfall in tandem with rising temperatures can be linked to the shrinking Wetland and Open Water

Correction: Wang, J., et al. Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data. Remote Sens. 2016, 8, 931

The authors wish to make the following corrections to their paper [1].[...

Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models

Three Sentinel-1A datasets in vertical transmitted and horizontal received (VH) and vertical transmitted and vertical received (VV) polarisations, and the linear combination of VH and VV (VHVV) are evaluated for rice green leaf area index (LAI) estimation using four machine learning regression models [Support Vector Machine (SVM), k-Nearest Neighbour (k-NN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT)]. Results showed that for the entire growing season, VV outperformed VH, recording an R2 of 0.68 and an RMSE of 0.98 m2/m2 with the k-NN model. However, VHVV produced the most accurate estimates with GBDT (R2 of 0.82 and RMSE of 0.68 m2/m2), followed by that of VHVV with RF (R2 of 0.78 and RMSE of 0.90 m2/m2). Our findings have further confirmed that combining VH and VV data can achieve improved rice growth modelling, and that tree-based algorithms can better handle data dimensionality

Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models

Optical satellite imagery has been widely used to monitor leaf area index (LAI). However, most studies have focussed on single- or dual-source data, thus making little use of a growing repository of freely available optical imagery. Hence this study has evaluated the feasibility of quad-source optical satellite imagery involving Landsat-8, Sentinel-2A, China’s environment satellite constellation (HJ-1 A and B) and Gaofen-1 (GF-1) in modelling rice green LAI over a test site located in southeast China at two growing seasons. With the application of machine learning regression models including Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbour (k-NN) and Gradient Boosting Decision Tree (GBDT), results indicated that regression models based on an ensemble of decision trees (RF and GBDT) were more suitable for modelling rice green LAI. The current study has demonstrated the feasibility of quad-source optical imagery in modelling rice green LAI and this is relevant for cloudy areas

Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R2 of 0.82 and RMSE of 191.8 g/m2) followed by the RF model (R2 of 0.79 and RMSE of 197.8 g/m2). After heading, the k-NN model performed best (R2 of 0.43 and RMSE of 452.1 g/m2) followed by the RF model (R2 of 0.42 and RMSE of 464.7 g/m2). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended

Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets

SVM and RF are widely used in rice mapping. However, their performance with single and different combinations of satellite datasets is rarely reported. Hence we report their rice mapping accuracies for two seasons using Sentinel-1A, Landsat-8 and Sentinel-2A images. The VH and VV polarizations of Sentinel-1A, and two spectral indices (SIs) of Landsat-8 and Sentine1-2A were used to obtain seven datasets (VH, VV, SI, VHVV, VHSI, VVSI and VHVVSI), and on which SVM and RF were applied and accuracies were assessed. VHSI showed the highest overall accuracy for both algorithms in both years. SVM with VHSI had a slightly higher accuracy (90.8%) than RF with VHSI (89.2%) in 2015 while in 2016 RF with VHSI showed a slightly higher accuracy (95.2%) than SVM with VHSI (93.4%). Although they produced equivalent accuracies within years, RF is more sensitive to additional data, given a 6.0% increase from 2015 to 2016 with VHSI