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Special Issue "Satellite Image Processing and Object Recognition for Agriculture and Food Security Applications"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 August 2023 | Viewed by 4095

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Special Issue Editors

Dr. Beril Kallfelz Sirmacek

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Guest Editor

Independent Scientist, Overijssel, The Netherlands
Interests: remote sensing; earth observation; machine learning; artificial intelligence; computer vision; feature engineering; big data visualization
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ning Li

E-Mail Website
Guest Editor

College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
Interests: radar systems; SAR; image processing; remote sensing; earth observation; satellite image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant agriculture is facing immense challenges due to climate change. By 2050, it is expected that more than nine billion people will live on our planet. To feed this number of people, the amount of food that is produced must increase by approximately 70%. At the same time, there is an increasing demand for sustainable agriculture which has a far smaller ecological footprint than the current agricultural processes. Therefore, it is important to find new ways to increase productivity while reducing harmful chemical use.

In this Special Issue, we would like researchers to propose new approaches to process remote sensing satellite images with object detection, machine learning, and artificial intelligence methods in order to provide opportunities for the use of sustainable plant agriculture and food security applications. We welcome researchers to use novel methods on real-life use cases and conduct experiments on specific test scenarios. We are looking forward to receiving journal manuscripts that are dedicated to helping our planet and extending the state-of-the-art research in this field. The topics include, but are not limited to, the following:

The identification of agricultural infrastructures;
The mapping of crop plantation and distribution;
The monitoring of crop growth;
The monitoring of crop diseases and insect pests;
The inversion of farmland soil moisture and other key parameters;
The models and methods for predicting crop yield;
The protection and monitoring of farmland biodiversity;
Food security and sustainable agriculture;
The novel image processing methods for agricultural and food security applications.

Dr. Beril Kallfelz Sirmacek
Prof. Dr. Ning Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing
geoscience and earth observation
artificial intelligence
machine learning
food security
water security
biodiversity protection
big data
visualization and mapping
automation and robotics
soil quality
water quality
yield protection
yield prediction

Published Papers (5 papers)

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Research

Open AccessArticle

Synergy of Sentinel-1 and Sentinel-2 Imagery for Crop Classification Based on DC-CNN

and

Remote Sens. 2023, 15(11), 2727; https://doi.org/10.3390/rs15112727 - 24 May 2023

Viewed by 389

Abstract

Over the years, remote sensing technology has become an important means to obtain accurate agricultural production information, such as crop type distribution, due to its advantages of large coverage and a short observation period. Nowadays, the cooperative use of multi-source remote sensing imagery has become a new development trend in the field of crop classification. In this paper, the polarimetric components of Sentinel-1 (S-1) decomposed by a new model-based decomposition method adapted to dual-polarized SAR data were introduced into crop classification for the first time. Furthermore, a Dual-Channel Convolutional Neural Network (DC-CNN) with feature extraction, feature fusion, and encoder-decoder modules for crop classification based on S-1 and Sentinel-2 (S-2) was constructed. The two branches can learn from each other by sharing parameters so as to effectively integrate the features extracted from multi-source data and obtain a high-precision crop classification map. In the proposed method, firstly, the backscattering components (VV, VH) and polarimetric components (volume scattering, remaining scattering) were obtained from S-1, and the multispectral feature was extracted from S-2. Four candidate combinations of multi-source features were formed with the above features. Following that, the optimal one was found on a trial. Next, the characteristics of optimal combinations were input into the corresponding network branches. In the feature extraction module, the features with strong collaboration ability in multi-source data were learned by parameter sharing, and they were deeply fused in the feature fusion module and encoder-decoder module to obtain more accurate classification results. The experimental results showed that the polarimetric components, which increased the difference between crop categories and reduced the misclassification rate, played an important role in crop classification. Among the four candidate feature combinations, the combination of S-1 and S-2 features had a higher classification accuracy than using a single data source, and the classification accuracy was the highest when two polarimetric components were utilized simultaneously. On the basis of the optimal combination of features, the effectiveness of the proposed method was verified. The classification accuracy of DC-CNN reached 98.40%, with Kappa scoring 0.98 and Macro-F1 scoring 0.98, compared to 2D-CNN (OA reached 94.87%, Kappa scored 0.92, and Macro-F1 scored 0.95), FCN (OA reached 96.27%, Kappa scored 0.94, and Macro-F1 scored 0.96), and SegNet (OA reached 96.90%, Kappa scored 0.95, and Macro-F1 scored 0.97). The results of this study demonstrated that the proposed method had significant potential for crop classification. Full article

(This article belongs to the Special Issue Satellite Image Processing and Object Recognition for Agriculture and Food Security Applications)

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Open AccessArticle

Inversion of Soil Moisture on Farmland Areas Based on SSA-CNN Using Multi-Source Remote Sensing Data

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Remote Sens. 2023, 15(10), 2515; https://doi.org/10.3390/rs15102515 - 10 May 2023

Viewed by 586

Abstract

Soil moisture is a crucial factor in the field of meteorology, hydrology, and agricultural sciences. In agricultural production, surface soil moisture (SSM) is crucial for crop yield estimation and drought monitoring. For SSM inversion, a synthetic aperture radar (SAR) offers a trustworthy data source. However, for agricultural fields, the use of SAR data alone to invert SSM is susceptible to the influence of vegetation cover. In this paper, based on Sentinel-1 microwave remote sensing data and Sentinel-2 optical remote sensing data, a convolution neural network optimized by sparrow search algorithm (SSA-CNN) was suggested to invert farmland SSM. The feature parameters were first extracted from pre-processed remote sensing data. Then, the correlation analysis between the extracted feature parameters and field measured SSM data was carried out, and the optimal combination of feature parameters for SSM inversion was selected as the input data of the subsequent models. To enhance the performance of the CNN, the hyper-parameters of CNN were optimized using SSA, and the SSA-CNN model was built for SSM inversion based on the obtained optimal hyper-parameter combination. Three typical machine learning approaches, including generalized regression neural network, random forest, and CNN, were used for comparison to show the efficacy of the suggested method. With an average coefficient of determination of 0.80, an average root mean square error of 2.17 vol.%, and an average mean absolute error of 1.68 vol.%, the findings demonstrated that the SSA-CNN model with the optimal feature combination had a better accuracy among the 4 models. In the end, the SSM of the study region was inverted throughout four phenological periods using the SSA-CNN model. The inversion results indicated that the suggested method performed well in local situations. Full article

(This article belongs to the Special Issue Satellite Image Processing and Object Recognition for Agriculture and Food Security Applications)

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Open AccessArticle

Classification of Land Cover in Complex Terrain Using Gaofen-3 SAR Ascending and Descending Orbit Data

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Remote Sens. 2023, 15(8), 2177; https://doi.org/10.3390/rs15082177 - 20 Apr 2023

Cited by 1 | Viewed by 483

Abstract

Synthetic aperture radar (SAR) image is an effective remote sensing data source for geographic surveys. However, accurate land cover mapping based on SAR image in areas of complex terrain has become a challenge due to serious geometric distortions and the inadequate separation ability of dual-polarization data. To address these issues, a new land cover mapping framework which is suitable for complex terrain is proposed based on Gaofen-3 data of ascending and descending orbits. Firstly, the geometric distortion area is determined according to the local incident angle, based on analysis of the SAR imaging mechanism, and the correct polarization information of the opposite track is used to compensate for the geometric distortion area, including layovers and shadows. Then, the dual orbital polarization characteristics (DOPC) and dual polarization radar vegetation index (DpRVI) of dual-pol SAR data are extracted, and the optimal feature combination is found by means of Jeffries–Matusita (J-M) distance analysis. Finally, the deep learning method 2D convolutional neural network (2D-CNN) is applied to classify the compensated images. The proposed method was applied to a mountainous region of the Danjiangkou ecological protection area in China. The accuracy and reliability of the method were experimentally compared using the uncompensated images and the images without DpRVI. Quantitative evaluation revealed that the proposed method achieved better performance in complex terrain areas, with an overall accuracy (OA) score of 0.93, and a Kappa coefficient score of 0.92. Compared with the uncompensated image, OA increased by 5% and Kappa increased by 6%. Compared with the images without DpRVI, OA increased by 4% and Kappa increased by 5%. In summary, the results demonstrate the importance of ascending and descending orbit data to compensate geometric distortion and reveal the effectiveness of optimal feature combination including DpRVI. Its simple and effective polarization information compensation capability can broaden the promising application prospects of SAR images. Full article

(This article belongs to the Special Issue Satellite Image Processing and Object Recognition for Agriculture and Food Security Applications)

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Open AccessArticle

Soil Moisture Inversion Based on Data Augmentation Method Using Multi-Source Remote Sensing Data

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Remote Sens. 2023, 15(7), 1899; https://doi.org/10.3390/rs15071899 - 31 Mar 2023

Viewed by 611

Abstract

Soil moisture is an important land environment characteristic that connects agriculture, ecology, and hydrology. Surface soil moisture (SSM) prediction can be used to plan irrigation, monitor water quality, manage water resources, and estimate agricultural production. Multi-source remote sensing is a crucial tool for assessing SSM in agricultural areas. The field-measured SSM sample data are required in model building and accuracy assessment of SSM inversion using remote sensing data. When the SSM samples are insufficient, the SSM inversion accuracy is severely affected. An SSM inversion method suitable for a small sample size was proposed. The alpha approximation method was employed to expand the measured SSM samples to offer more training data for SSM inversion models. Then, feature parameters were extracted from Sentinel-1 microwave and Sentinel-2 optical remote sensing data, and optimized using three methods, which were Pearson correlation analysis, random forest (RF), and principal component analysis. Then, three common machine learning models suitable for small sample training, which were RF, support vector regression, and genetic algorithm-back propagation neural network, were built to retrieve SSM. Comparison experiments were carried out between various feature optimization methods and machine learning models. The experimental results showed that after sample augmentation, SSM inversion accuracy was enhanced, and the combination of utilizing RF for feature screening and RF for SSM inversion had a higher accuracy, with a coefficient of determination of 0.7256, a root mean square error of 0.0539 cm³/cm³, and a mean absolute error of 0.0422 cm³/cm³, respectively. The proposed method was finally used to invert the regional SSM of the study area. The inversion results indicated that the proposed method had good performance in regional applications with a small sample size. Full article

(This article belongs to the Special Issue Satellite Image Processing and Object Recognition for Agriculture and Food Security Applications)

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Open AccessCommunication

Which Vegetation Index? Benchmarking Multispectral Metrics to Hyperspectral Mixture Models in Diverse Cropland

Daniel Sousa

and

Christopher Small

Remote Sens. 2023, 15(4), 971; https://doi.org/10.3390/rs15040971 - 10 Feb 2023

Cited by 1 | Viewed by 1424

Abstract

The monitoring of agronomic parameters like biomass, water stress, and plant health can benefit from synergistic use of all available remotely sensed information. Multispectral imagery has been used for this purpose for decades, largely with vegetation indices (VIs). Many multispectral VIs exist, typically relying on a single feature—the spectral red edge—for information. Where hyperspectral imagery is available, spectral mixture models can use the full VSWIR spectrum to yield further insight, simultaneously estimating area fractions of multiple materials within mixed pixels. Here we investigate the relationships between VIs and mixture models by comparing hyperspectral endmember fractions to six common multispectral VIs in California’s diverse crops and soils. In so doing, we isolate spectral effects from sensor- and acquisition-specific variability associated with atmosphere, illumination, and view geometry. Specifically, we compare: (1) fractional area of photosynthetic vegetation (F_v) from 64,000,000 3–5 m resolution AVIRIS-ng reflectance spectra; and (2) six popular VIs (NDVI, NIRv, EVI, EVI2, SR, DVI) computed from simulated Planet SuperDove reflectance spectra derived from the AVIRIS-ng spectra. Hyperspectral F_v and multispectral VIs are compared using both parametric (Pearson correlation, ρ) and nonparametric (Mutual Information, MI) metrics. Four VIs (NIRv, DVI, EVI, EVI2) showed strong linear relationships with F_v (ρ > 0.94; MI > 1.2). NIRv and DVI showed strong interrelation (ρ > 0.99, MI > 2.4), but deviated from a 1:1 correspondence with F_v. EVI and EVI2 were strongly interrelated (ρ > 0.99, MI > 2.3) and more closely approximated a 1:1 relationship with F_v. In contrast, NDVI and SR showed a weaker, nonlinear, heteroskedastic relation to F_v (ρ < 0.84, MI = 0.69). NDVI exhibited both especially severe sensitivity to unvegetated background (–0.05 < NDVI < +0.6) and saturation (0.2 < F_v < 0.8 for NDVI = 0.7). The self-consistent atmospheric correction, radiometry, and sun-sensor geometry allows this simulation approach to be further applied to indices, sensors, and landscapes worldwide. Full article

(This article belongs to the Special Issue Satellite Image Processing and Object Recognition for Agriculture and Food Security Applications)

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