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Application of Remote Sensing in Efficient Utilization and Protection of Cultivated Land

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 12866

Special Issue Editors

Prof. Dr. Huanjun Liu

E-Mail Website
Guest Editor
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: remote sensing and smart agriculture; soil and farmland monitoring
Dr. Chong Luo

E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888, Shengbei Street, Changchun 130102, China
Interests: crop mapping; soil mapping; google earth engine; remote sensing; agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Qiangzi Li

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Interests: agricultural remote sensing; precision agriculture

Special Issue Information

Dear Colleagues,

Cultivated land is an extremely important component of land resources, influencing the national food security, agricultural product quality security and ecological security, and is a guarantee of social and economic sustainability. However, about 75 billion tons of fertile soil are lost every year, and desertification and drought alone lead to 12 million hectares of arable land degradation, which means that the world is facing a serious problem of farmland degradation. Features associated with it include, but are not limited to, cultivated land abandoning, topsoil thinning, decreased fertility, increased runoff erosion, soil salinization, and aquifer depletion. A very important condition for the life and growth of human beings and civilization, as well as a cornerstone of ensuring national food security and enhancing people's livelihood, is the efficient utilization and protection of cultivated land as a limited resource.

Remote sensing technology can provide various information about cultivated land, in an objective, accurate and timely manner. It is an important source of accurate field data. Monitoring the status and function of agricultural production in cultivated land with remote sensing technology is an important means to ensure the sustainable development of agriculture. With the wide application of more and more multi-spectral satellites, hyper-spectral satellites and synthetic aperture radar (SAR), unmanned aerial vehicles and Internet of Things (IoT) sensors, a richer data source is provided for monitoring in cultivated land. At the same time, with the development of computer technology, there are more and more methods of remote sensing, from pixel-based to object-oriented methods, from manual to machine learning methods. In addition, the emergence of a cloud computing platform makes remote sensing monitoring expand from a small scale to a large area. However, due to the complexity and diversity of cultivated land, the application of remote sensing in efficient utilization and the protection of cultivated land still faces many challenges.

This issue aims to explore the use of the most advanced remote sensing technology for innovative research. Articles help to understand the application of remote sensing in the efficient utilization and protection of cultivated land. Potential topics include, but are not limited to:

  • Soil physical and chemical properties and remote sensing mapping
  • Remote sensing retrieval of crop physiological parameters
  • Remote sensing monitoring of crop planting structure
  • Crop growth and yield estimation
  • Crop growth model and remote sensing data assimilation
  • Remote sensing technology and intelligent agriculture
  • Remote sensing technology and sustainable development of agriculture

Dr. Huanjun Liu
Dr. Chong Luo
Dr. Qiangzi 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 2700 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.

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Published Papers (3 papers)

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Research

23 pages, 5677 KiB  
Article
Combinations of Feature Selection and Machine Learning Algorithms for Object-Oriented Betel Palms and Mango Plantations Classification Based on Gaofen-2 Imagery
by Hongxia Luo, Maofen Li, Shengpei Dai, Hailiang Li, Yuping Li, Yingying Hu, Qian Zheng, Xuan Yu and Jihua Fang
Remote Sens. 2022, 14(7), 1757; https://doi.org/10.3390/rs14071757 - 6 Apr 2022
Cited by 21 | Viewed by 4586
Abstract
Betel palms and mango plantations are two crucial commercial crops in tropical agricultural areas. Accurate spatial distributions of these two crops are essential in tropical agricultural regional planning and management. However, the characteristics of small patches, scattering, and perennation make it challenging to [...] Read more.
Betel palms and mango plantations are two crucial commercial crops in tropical agricultural areas. Accurate spatial distributions of these two crops are essential in tropical agricultural regional planning and management. However, the characteristics of small patches, scattering, and perennation make it challenging to map betel palms and mango plantations in complex tropical agricultural regions. Furthermore, the excessive features of very-high-resolution (VHR) imaging might lead to a reduction in classification accuracy and an increase in computation times. To address these challenges, we selected five feature selection (FS) methods (random forest means a decrease in accuracy (RFMDA), ReliefF, random forest-recursive feature elimination (RFE), aggregated boosted tree (ABT), and logistic regression (LR)) and four machine learning algorithms (random forest (RF), support vector machine (SVM), classification and regression tree (CART), and adaptive boosting (AdaBoost)). Then, the optimal combinations of FS and machine learning algorithms suited for object-oriented classification of betel palms and mango plantations were explored using VHR Gaofen-2 imagery. In terms of overall accuracy, all optimal classification schemes exceeded 80%, and the classifiers using selected features increased the overall accuracy between 1% and 4% compared with classification without FS methods. Specifically, LR was appropriate to RF and SVM classifiers, which produced the highest classification accuracy (89.1% and 89.88% for RF and SVM, respectively). In contrast, ABT and ReliefF were found to be suitable FS methods for CART and AdaBoost classifiers, respectively. Overall, all four optimal combinations of FS methods and classifiers could precisely recognize mango plantations, whereas betel palms were best depicted by using the RF-LR method with 26 features. The results indicated that combination of feature selection and machine learning algorithms contributed to the object-oriented classification of complex tropical crops using Gaofen-2 imagery, which provide a useful methodological reference for precisely recognizing small tropical agricultural patterns. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Efficient Utilization and Protection of Cultivated Land)
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24 pages, 7779 KiB  
Article
A Remotely Sensed Framework for Spatially-Detailed Dryland Soil Organic Matter Mapping: Coupled Cross-Wavelet Transform with Fractional Vegetation and Soil-Related Endmember Time Series
by Qiangqiang Sun, Ping Zhang, Xin Jiao, Fei Lun, Shiwei Dong, Xin Lin, Xiangyu Li and Danfeng Sun
Remote Sens. 2022, 14(7), 1701; https://doi.org/10.3390/rs14071701 - 1 Apr 2022
Cited by 3 | Viewed by 2591
Abstract
Soil organic matter (SOM) plays pivotal roles in characterizing dryland structure and function; however, remotely sensed spatially-detailed SOM mapping in these regions remains a challenge. Various digital soil mapping approaches based on either single-period remote sensing or spectral indices in other ecosystems usually [...] Read more.
Soil organic matter (SOM) plays pivotal roles in characterizing dryland structure and function; however, remotely sensed spatially-detailed SOM mapping in these regions remains a challenge. Various digital soil mapping approaches based on either single-period remote sensing or spectral indices in other ecosystems usually produce inaccurate, poorly constrained estimates of dryland SOM. Here, a framework for spatially-detailed SOM mapping was proposed based on cross-wavelet transform (XWT) that exploits ecologically meaningful features from intra-annual fractional vegetation and soil-related endmember records. In this framework, paired green vegetation (GV) and soil-related endmembers (i.e., dark surface (DA), saline land (SA), sand land (SL)) sequences were adopted to extract 30 XWT features in temporally and spatially continuous domains of cross-wavelet spectrum. We then selected representative features as exploratory covariates for SOM mapping, integrated with four state-of-the-art machine learning approaches, i.e., ridge regression (RR), least squares-support vector machines (LS-SVM), random forests (RF), and gradient boosted regression trees (GBRT). The results reported that SOM maps from 13 coupled filtered XWT features and four machine learning approaches were consistent with soil-landscape knowledge, as evidenced by a spatially-detailed gradient from oasis to barren. This framework also presented more accurate and reliable results than arithmetically averaged features of intra-annual endmembers and existing datasets. Among the four approaches, both RF and GBRT were more appropriate in the XWT-based framework, showing superior accuracy, robustness, and lower uncertainty. The XWT synthetically characterized soil fertility from the consecutive structure of intra-annual vegetation and soil-related endmember sequences. Therefore, the proposed framework improved the understanding of SOM and land degradation neutrality, potentially leading to more sustainable management of dryland systems. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Efficient Utilization and Protection of Cultivated Land)
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20 pages, 5316 KiB  
Article
Cotton Cultivated Area Extraction Based on Multi-Feature Combination and CSSDI under Spatial Constraint
by Yong Hong, Deren Li, Mi Wang, Haonan Jiang, Lengkun Luo, Yanping Wu, Chen Liu, Tianjin Xie, Qing Zhang and Zahid Jahangir
Remote Sens. 2022, 14(6), 1392; https://doi.org/10.3390/rs14061392 - 13 Mar 2022
Cited by 3 | Viewed by 2909
Abstract
Cotton is an important economic crop, but large-scale field extraction and estimation can be difficult, particularly in areas where cotton fields are small and discretely distributed. Moreover, cotton and soybean are cultivated together in some areas, further increasing the difficulty of cotton extraction. [...] Read more.
Cotton is an important economic crop, but large-scale field extraction and estimation can be difficult, particularly in areas where cotton fields are small and discretely distributed. Moreover, cotton and soybean are cultivated together in some areas, further increasing the difficulty of cotton extraction. In this paper, an innovative method for cotton area estimation using Sentinel-2 images, land use status data (LUSD), and field survey data is proposed. Three areas in Hubei province (i.e., Jingzhou, Xiaogan, and Huanggang) were used as research sites to test the performance of the proposed extraction method. First, the Sentinel-2 images were spatially constrained using LUSD categories of irrigated land and dry land. Seven classification schemes were created based on spectral features, vegetation index (VI) features, and texture features, which were then used to generate the SVM classifier. To minimize misclassification between cotton and soybean fields, the cotton and soybean separation index (CSSDI) was introduced based on the red band and red-edge band of Sentinel-2. The configuration combining VI and spectral features yielded the best cotton extraction results, with F1 scores of 86.93%, 80.11%, and 71.58% for Jingzhou, Xiaogan, and Huanggang. When CSSDI was incorporated, the F1 score for Huanggang increased to 79.33%. An alternative approach using LUSD for non-target sample augmentation was also introduced. The method was used for Huangmei county, resulting in an F1 score of 78.69% and an area error of 7.01%. These results demonstrate the potential of the proposed method to extract cotton cultivated areas, particularly in regions with smaller and scattered plots. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Efficient Utilization and Protection of Cultivated Land)
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