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The accurate mapping of Spartina alterniflora (S. alterniflora) invasion is crucial for controlling its spread and reducing severe ecological problems. Satellite images have been extensively employed for S. alterniflora invasion monitoring; however, there are still several issues that need to be addressed. The spectral similarities between S. alterniflora and surrounding ground objects make it challenging for traditional classifiers to achieve satisfactory extraction accuracy. Since the phenological information and red-edge spectral differences have been considered as informative features for identifying S. alterniflora, current studies mainly used them separately as classification features and seldom considered the differences of red-edge information at different phenological periods. Therefore, we proposed a pixel-based phenological and red-edge feature composite method (PpRef-CM) for S. alterniflora extraction considering both phenological information and red-edge bands derived from Sentinel-2 time series based on the existing pixel-based phenological feature composite method (Ppf-CM). The proposed PpRef-CM and machine-learning algorithms were employed for S. alterniflora extraction in two typical mangrove forests along coastal China. Results indicated that red-edge information at different phenological periods is essential for detecting S. alterniflora. S. alterniflora extraction achieved the highest accuracy of 96.57% by using the eXtreme gradient boost algorithm when compared with other machine-learning algorithms. The PpRef-CM gave 2.72% and 2.61% more extraction accuracies of S. alterniflora than the Ppf-CM in two study sites, separately. These findings provide insights for selecting suitable classification features for S. alterniflora extraction studies and serve as an effective control and management of S. alterniflora.

Nitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) satellite data and available soil N (Nav) at different vegetative growth stages for corn crop. Eleven field studies were conducted in the Pampas region (Argentina), applying five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1), all at sowing time. Plant samples were collected at sixth-leaf (V6), tenth-leaf (V10), fourteen-leaf (V14), and flowering (R1). Using linear regression models, NNI was best predicted using only optical satellite data from V6 to V14, and integrating optical with C-SAR plus Nav at R1. The best monitoring model integrated vegetation spectral indices, C-SAR and Nav data at V10 with an adjusted R2 of 0.75 achieved during calibration in the northern Pampa. During validation, it predicted NNI with an RMSE of 0.14 and a MAPE of 12% in the southeastern Pampa. The red-edge spectrum and Local Incidence Angle of C-SAR were necessary to monitor the corn N status via prediction of NNI. Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The pressure of natural resource management and ecological environment monitoring is increasingly prominent. It is urgent to give full play to the advantages of remote sensing data to assist the natural resources management. The application capability of domestic satellites needs further excavation. GF-6 is a newly launched satellite belonging to China High Resolution Earth Observation System, which has the advantages of large angle, high frequency and new spectrum. CF-6 is one of the few satellites with eight bands in the visible and near-infrared spectrum. A control experiment was designed for the test of new bands of GF-6 based on methods of linear spectral mixing analysis (LSMA) model, decision tree and correlation analysis. The complete spectral space was reconstructed into four scenarios: the original spectral space (S1), the lack of red-edge band scenario (S2), the lack of yellow-band scenario (S3) and the lack of purple-band scenario (S4). All the research work was based on endmember (EM) fraction maps, which were generated from LSMA. In order to obtain the endmember fraction maps accurately, we employed the principal component analysis (PCA) to reduce the data dimensions, and determined four endmembers (Green vegetation, GV; Substrate, SU; Dark material, DA and Water, WA) though the result of PCA and the status of local Land use/cover. After that, the contribution of new bands to endmember fraction maps was judged by correlation analysis between the add-bands and each endmember fraction maps. Finally, the decision tree classification was used to observe the classification results in scenarios and draw the final conclusion. In addition, we also compared the application ability of GF-6 with OLI and Sentinel-2 with LSMA model. Through the four situation’s experiments, we came to conclusions as follow. The results of all three methods show that the red-edge band is sensitive to vegetation, which can effectively improve the recognition accuracy of vegetation. Besides, the result of LSMA model indicates that the red-edge band also contributes to the applicability and stability of the LSMA model. The result of correlation analysis shows that violet band and yellow band have strong correlation with substrate, therefore contributing to the classification of urban interior facilities; but they have an inverse correlation with vegetation. The yellow band and red-edge1 band may cause classification errors in mapping of large area. The significance of this study lies in it founds a variety of response characteristics of new bands in GF-6 to land use/cover. And the conclusion of this study will not only provide a robust support for natural resources supervision and ecological protection in our country, but also witness the great progress made by Chinese satellites. [ABSTRACT FROM AUTHOR]

• Hyperspectral drone imagery allowed early detection of pine wilt disease. • The 1st derivative reflectance at 714 nm had the highest separability. • Yellow and blue-edge inflection points can improve the classification. • Using machine learning with more variables hardly improved the classification. • Using similar wavelengths to the Sentinel-2 images achieved similar accuracy. Pine wilt disease (PWD) is a destructive disease of pine trees caused by the pine wood nematode and early identification is crucial for disease control. Hyperspectral drone imagery has the potential to achieve early detection, although specific methods have not been sufficiently explored, including the spectral characteristics of early infections and the most efficient identification methods. This study aimed to examine the spectral responses to early infection, quantify the separability of healthy and early infections, and compare the accuracy and efficiency of different methods including single bands, vegetation indices (VIs) and 1st derivative reflectance and indices. We collected hyperspectral drone data in southeast China and used linear discriminant analysis (LDA) to determine the separability of healthy trees and trees at an early stage of infection. We also used bands with the same wavelengths as Sentienl-2 images (denoted S2 bands) to propose a standard for band selection. We found that it was possible to separate healthy trees and those at an early stage of infection around 0.71–0.78 using different methods. Among individual bands, the red-edge bands had the highest separability of 0.74. Using standard vegetation indices resulted in separability between 0.67 and 0.71; in addition, we proposed three new indices that achieved separability between 0.73 and 0.75. The 1st derivative reflectance at 714 nm had the highest separability of 0.78 in this study, while using the 1st derivative reflectance indices was slightly less accurate. The classification accuracy was also slightly lower when using Random Forest (RF) with all bands, sensitive bands, and S2 bands. We conclude that, of the methods tested, the red-edge bands are most sensitive to early infection, and using the 1st derivative reflectance at 714 nm or using the 1st derivative reflectance at the red-edge and blue-edge inflection point (REIP and BEIP) was sufficient for early identification. Acquiring and using additional wavelengths hardly improved the classification. Using wavelengths similar to those in the Sentienl-2 images achieved similar results, and thus can be used as a standard for dimensionality reduction of hyperspectral data pertaining to forest disease. The newly proposed VIs and 1st derivative reflectances at the yellow-edge inflection point (YEIP) and BEIP delivered better performance than the other tested indices, and could be alternatives for early identification. This study proposes simple and practical methods for early identification of wilt and provides insights for efficient data acquisition and data reduction. [ABSTRACT FROM AUTHOR]

Plastic greenhouses (PGs) are widely built near cities in China to produce vegetables and fruits. In order to promote sustainable agriculture, rural landscape construction, and better manage water resources, numerous remote sensing methods have been developed to identify and monitor the distribution of PGs, of which many map PGs based on spectral responses and geometric shapes. In this study, we proposed a new fine- and coarse-scale mapping approach using two-temporal Sentinel-2 images with various seasonal characteristics and a one-dimensional convolutional neural network (1D-CNN). Having applied this approach in a pilot area study, the results were summarized as follows: (1) A time-series analysis of Sentinel-2 images showed that the reflectance of greenhouses changes during crop growth and development. In particular, the red-edge and near-infrared bands undergo a significant increase and then decrease during the whole crop growth period. Thus, two critical period images, containing a substantial difference in greenhouse reflectance, were sufficient to carry out an accurate and efficient mapping result. (2) The 1D-CNN classifier was used to map greenhouses by capturing subtle details and the overall trend of the spectrum curve. Overall, our approach showed higher classification accuracy than other approaches using support vector machines (SVM) or random forests (RF). In addition, the greenhouse area identified was highly consistent with the existing surfaces observed in very high-resolution images, with a kappa co-efficient of 0.81. (3) The narrow band feature differences (red-edge and near infrared narrow bands) in two-temporal Sentinel-2 images played a significant role in high-precision greenhouse mapping. The classification accuracy with narrow band features was much better than the maps produced without narrow band features. This scheme provided a method to digitize greenhouse precisely and publish its statistics for free, which enable advanced decision support for agriculture management.

Abstract Remote sensing data is widely used in numerous ecological applications. The Sentinel-2 satellites (S2 A and B), recently launched by the European Spatial Agency's (ESA), provide at present the best revisit time, spatial and spectral resolution among the freely available remote sensing optical data. In this study, we explored the potential of S2 enhanced spectral and spatial resolution to explain and predict mice abundances and distribution in border habitats of agroecosystems. We compared the predictive ability of different vegetation and water indices derived from S2 and Landsat 8 (L8) imagery. Our analyses revealed that the best predictor of mice abundance was L8-derived Enhanced Vegetation Index (EVI). S2-based indices, however, outperformed those computed from L8 bands for indices estimated simultaneously to mice trappings and for mice distribution models. Furthermore, indices including S2 red-edge bands were the best predictors of the distribution of the two most common rodent species in the ensemble. The findings of this study can be used as guidelines when selecting the sensors and vegetation variables to be included in more complex models aimed at predicting the distribution and risk of various vector-borne diseases, and especially rodents in other agricultural landscapes. Highlights • Mice abundance in border habitats was better explained by a Landsat 8 index. • Mice distribution and abundance in t0 was better explained by Sentinel 2. • Vegetation indices including red-edge bands were among the best predictors. • These findings are guidelines to select sensors and vegetation indices to use in monitoring systems for early warning. [ABSTRACT FROM AUTHOR]

One of the most common approaches to reducing the environmental impact of nitrogen (N) fertilisation in intensive agrosystems is to adjust the N input of the crop requirement. This adjustment is frequently related to the nitrogen nutrition index (NNI) based on the concepts of the critical and actual N absorbed (kg/ha) in the crop canopy (respectively, N C and CNC). Accurate estimation of the N C and CNC at the field scale over large areas based on freely available satellite imagery is thus a key issue to address. Relying on a large dataset of farmers' fields, this study highlights the high correlation (R 2  = 0.90) between the wheat CNC and canopy chlorophyll content ( CCC ) retrieved from Sentinel-2 (S2) with an Artificial Neural Network (ANN). The estimation is related to errors of 4 and 21 kg/ha (depending on the growing stage), which is a promising result for evaluating the NNI. There are four major outcomes from this result: (i) the importance of working at the canopy level; (ii) the independence of the relationship to the considered cultivars; (iii) the dependence of the relationship on the growing stage; and (iv) the potential to use only the 10 m S2 bands, opening the way for precision agriculture. In parallel, estimation accuracies were investigated for the three biophysical variables (BV) related to the CNC and N C , i.e., the green area index ( GAI ), leaf chlorophyll content ( Cab ) and CCC . From this analysis, the added value of the red-edge bands for improving the estimation of the 3 BVs of interest was quantified as was the performance reduction related to the field heterogeneity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • Estimating sugarcane leaf nitrogen content on at field scale using of Sentinel-2 satellite. • GEMI index as the most effective variable to estimate sugarcane nitrogen content. • The performance of the RF algorithm was better than of SVR algorithm for estimating nitrogen. Nitrogen is an essential factor for assessing the quality of sugarcane during the growing season, as deficiency of this component significantly reduces crop yield. The Kjeldahl method is the most common approach to measuring sugarcane nitrogen, but it is a laborious, costly, and time-consuming process. Conversely, multispectral satellite imagery can provide timely, cost-effective, and large-scale information on the nitrogen content in sugarcane fields. The current study applied random forest (RF) and support vector regression (SVR) models to estimate sugarcane leaf nitrogen using vegetation indices and spectral bands of Sentinel-2. In-situ data was taken from 45 farms (1125 ha) in a sugarcane production agro-industrial complex in southwest Iran. The global environmental monitoring index (GEMI), chlorophyll index green (Clgreen), and Sentinel-2 red-edge position index (S2REP) were found to be the most important variables related to sugarcane nitrogen. The coefficient of determination (R2) for RF and SVR was 0.59 and 0.58, respectively, and the corresponding root mean square error (RMSE) was 0.08 and 0.09, respectively. Despite the similar performances of the two models, RF showed higher accuracy; however, to improve the results, the use of multi-temporal data for model calibration is recommended. [ABSTRACT FROM AUTHOR]

Plastic greenhouses (PGs) are widely built near cities in China to produce vegetables and fruits. In order to promote sustainable agriculture, rural landscape construction, and better manage water resources, numerous remote sensing methods have been developed to identify and monitor the distribution of PGs, of which many map PGs based on spectral responses and geometric shapes. In this study, we proposed a new fine- and coarse-scale mapping approach using two-temporal Sentinel-2 images with various seasonal characteristics and a one-dimensional convolutional neural network (1D-CNN). Having applied this approach in a pilot area study, the results were summarized as follows: (1) A time-series analysis of Sentinel-2 images showed that the reflectance of greenhouses changes during crop growth and development. In particular, the red-edge and near-infrared bands undergo a significant increase and then decrease during the whole crop growth period. Thus, two critical period images, containing a substantial difference in greenhouse reflectance, were sufficient to carry out an accurate and efficient mapping result. (2) The 1D-CNN classifier was used to map greenhouses by capturing subtle details and the overall trend of the spectrum curve. Overall, our approach showed higher classification accuracy than other approaches using support vector machines (SVM) or random forests (RF). In addition, the greenhouse area identified was highly consistent with the existing surfaces observed in very high-resolution images, with a kappa co-efficient of 0.81. (3) The narrow band feature differences (red-edge and near infrared narrow bands) in two-temporal Sentinel-2 images played a significant role in high-precision greenhouse mapping. The classification accuracy with narrow band features was much better than the maps produced without narrow band features. This scheme provided a method to digitize greenhouse precisely and publish its statistics for free, which enable advanced decision support for agriculture management. [ABSTRACT FROM AUTHOR]