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Remote Sensing for Mapping Farmland and Agricultural Infrastructure

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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: closed (31 July 2023) | Viewed by 16759

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Special Issue Editors

Dr. Qiong Hu

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Guest Editor

College of Urban and Environmental Sciences, Central China Normal University, No.152 Luoyu Road, Wuhan 430079, China
Interests: crop mapping; land use change; feature selection; cropping system; agricultural intensification
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wenbin Wu

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Guest Editor

Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Haidian District, Beijing 100081, China
Interests: smart agriculture; agricultural system; crop mapping; climate change
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hao Wu

E-Mail Website
Guest Editor

College of Urban and Environment Sciences, Huazhong Normal University, Wuhan 430079, China
Interests: land cover classification; land use modelling; agricultural system

Dr. Tuya Hasi

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Guest Editor

College of Grassland, Resources and Environment Inner Mongolia Agricultural University, No. 29, Ordos East Street, Saihan District, Hohhot 010011, China
Interests: agricultural infrastructure mapping; precision agriculture; agricultural ecosystem

Dr. Qian Song

E-Mail Website
Guest Editor

Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Haidian District, Beijing, 100081, China
Interests: crop mapping; land use and land cover; agricultural system

Special Issue Information

Dear Colleagues,

Attempting to feed a growing population with limited farmland presents a major challenge. Addressing this challenge requires the sustainable management of farmland. In addition, agricultural infrastructure (e.g., greenhouse, irrigation facilities, road networks, and shelterbelts) associated with farmland plays crucial roles in improving the input–output efficiency of agricultural production. This suggests that there is an urgent need for precise information about the spatial distribution, type identification and size estimation of farmland and agricultural infrastructure at different scales. With the advent of modern remote sensing techniques (multi-source satellite observations and advanced data processing methods), the accuracies and efficiency of agricultural monitoring have been increasingly improved. This proposed Special Issue aims to share the latest research progress related to novel techniques and applications for mapping farmland and agricultural infrastructure. The topics include, but are not limited to, the following:

Crop type/cropland mapping;
Crop field size mapping/ field boundary detection;
Mapping tree crops or specialty crops (citrus, coffee, cocoa, etc.);
Mapping and characterization of management practices in various cropping systems (crop rotation, intensity, tillage, etc.);
Identification of agricultural building infrastructures (greenhouse, road networks, etc.);
Identification of agricultural irrigation infrastructures (irrigation well, hydropower station, etc.);
Identification of agricultural ecological protection infrastructures (farmland shelterbelts, check dam, etc.);
Novel image processing methods for agricultural applications.

Dr. Qiong Hu
Prof. Dr. Wenbin Wu
Prof. Dr. Hao Wu
Dr. Tuya Hasi
Dr. Qian Song
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.

Keywords

mapping
crop types
cropland parcels
specialty crops
agricultural infrastructure
farming system
cropping patterns
smallholder agriculture

Published Papers (5 papers)

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Research

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19 pages, 3750 KiB

Open AccessArticle

Cultivated Land Quality Evaluated Using the RNN Algorithm Based on Multisource Data

by Wu Zhou, Li Zhao, Yueming Hu, Zhenhua Liu, Lu Wang, Changdong Ye, Xiaoyun Mao and Xia Xie

Remote Sens. 2022, 14(23), 6014; https://doi.org/10.3390/rs14236014 - 27 Nov 2022

Cited by 1 | Viewed by 1424

Abstract

Cultivated land quality (CLQ) is associated with national food security, benign economic development, social harmony, and stability. The scientific evaluation of CLQ provides the basis for achieving the “trinity” protection of cultivated land quantity, and quality, as well as ecology. However, the current research on CLQ evaluation has some limitations, mainly the poor consideration of evaluation indicators, time-consuming and labor-intensive data acquisition, and low precision of evaluation at the regional scale. Therefore, this study introduced multisource data to evaluate CLQ and proposed a new method for CLQ evaluation (natural grade evaluation, utilization grade evaluation, and economic grade evaluation), combining multisource data and the recurrent neural network (RNN) algorithm. Initially, optimal indicators were determined by correlation analysis and generalized linear regression coefficient methods based on factors related to CLQ acquired from multisource data. Then, CLQ evaluation models were constructed with the RNN algorithm on the basis of the aforementioned optimal indicators. Finally, the models were adopted to map CLQ. The present study was carried out in Guangzhou City, Guangdong Province, China. According to the results: (1) CLQ showed close relationship to pH, effective soil layer thickness (EST), chemical fertilizer application rate (CHFE), organic matter content (OMC), annual accumulated temperature (TEMA), 5–15 cm soil depth soil cation exchange capacity (CEC515), 0–5 cm soil depth soil cation exchange capacity (CEC05), 5–15 cm soil depth soil organic carbon content (SOC515), 0–5 cm soil depth soil organic carbon content (SOC05), field slope (FS), groundwater level (GWL), and terrain slope (TS). (2) All modeling accuracies (

R^{2}

) were greater than 0.80 for the CLQ evaluation models constructed based on the RNN algorithm. The area and spatial distribution of each grade of CLQ evaluation were consistent with the actual situation. The best and the worst quality cultivated land occupied a small area, and the area without a gap with the actual CLQ was as high as 76%, indicating that the model results were reliable. The study shows the suitability of the method for evaluating CLQ at the regional scale, offering a scientific foundation for the rational utilization and management of cultivated land resources, as well as a reference for evaluating CLQ in the future. Full article

(This article belongs to the Special Issue Remote Sensing for Mapping Farmland and Agricultural Infrastructure)

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18 pages, 6143 KiB

Open AccessArticle

Genetic Programming for High-Level Feature Learning in Crop Classification

by Miao Lu, Ying Bi, Bing Xue, Qiong Hu, Mengjie Zhang, Yanbing Wei, Peng Yang and Wenbin Wu

Remote Sens. 2022, 14(16), 3982; https://doi.org/10.3390/rs14163982 - 16 Aug 2022

Cited by 4 | Viewed by 1832

Abstract

Information on crop spatial distribution is essential for agricultural monitoring and food security. Classification with remote-sensing time series images is an effective way to obtain crop distribution maps across time and space. Optimal features are the precondition for crop classification and are critical to the accuracy of crop maps. Although several approaches are available for extracting spectral, temporal, and phenological features for crop identification, these methods depend heavily on domain knowledge and human experiences, adding uncertainty to the final crop classification. This study proposed a novel Genetic Programming (GP) approach to learning high-level features from time series images for crop classification to address this issue. We developed a new representation of GP to extend the GP tree’s width and depth to dynamically generate either fixed or flexible informative features without requiring domain knowledge. This new GP approach was wrapped with four classifiers, i.e., K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM), and was then used for crop classification based on MODIS time series data in Heilongjiang Province, China. The performance of the GP features was compared with the traditional features of vegetation indices (VIs) and the advanced feature learning method Multilayer Perceptron (MLP) to show GP effectiveness. The experiments indicated that high-level features learned by GP improved the classification accuracies, and the accuracies were higher than those using VIs and MLP. GP was more robust and stable for diverse classifiers, different feature numbers, and various training sample sets compared with classification using VI features and the classifier MLP. The proposed GP approach automatically selects valuable features from the original data and uses them to construct high-level features simultaneously. The learned features are explainable, unlike those of a black-box deep learning model. This study demonstrated the outstanding performance of GP for feature learning in crop classification. GP has the potential of becoming a mainstream method to solve complex remote sensing tasks, such as feature transfer learning, image classification, and change detection. Full article

(This article belongs to the Special Issue Remote Sensing for Mapping Farmland and Agricultural Infrastructure)

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24 pages, 15207 KiB

Open AccessArticle

Improved U-Net Remote Sensing Classification Algorithm Fusing Attention and Multiscale Features

by Xiangsuo Fan, Chuan Yan, Jinlong Fan and Nayi Wang

Remote Sens. 2022, 14(15), 3591; https://doi.org/10.3390/rs14153591 - 27 Jul 2022

Cited by 12 | Viewed by 7959

Abstract

The selection and representation of classification features in remote sensing image play crucial roles in image classification accuracy. To effectively improve the features classification accuracy, an improved U-Net remote sensing classification algorithm fusing attention and multiscale features is proposed in this paper, called spatial attention-atrous spatial pyramid pooling U-Net (SA-UNet). This framework connects atrous spatial pyramid pooling (ASPP) with the convolutional units of the encoder of the original U-Net in the form of residuals. The ASPP module expands the receptive field, integrates multiscale features in the network, and enhances the ability to express shallow features. Through the fusion residual module, shallow and deep features are deeply fused, and the characteristics of shallow and deep features are further used. The spatial attention mechanism is used to combine spatial with semantic information so that the decoder can recover more spatial information. In this study, the crop distribution in central Guangxi province was analyzed, and experiments were conducted based on Landsat 8 multispectral remote sensing images. The experimental results showed that the improved algorithm increases the classification accuracy, with the accuracy increasing from 93.33% to 96.25%, The segmentation accuracy of sugarcane, rice, and other land increased from 96.42%, 63.37%, and 88.43% to 98.01%, 83.21%, and 95.71%, respectively. The agricultural planting area results obtained by the proposed algorithm can be used as input data for regional ecological models, which is conducive to the development of accurate and real-time crop growth change models. Full article

(This article belongs to the Special Issue Remote Sensing for Mapping Farmland and Agricultural Infrastructure)

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21 pages, 5991 KiB

Open AccessArticle

An Adaptive Image Segmentation Method with Automatic Selection of Optimal Scale for Extracting Cropland Parcels in Smallholder Farming Systems

by Zhiwen Cai, Qiong Hu, Xinyu Zhang, Jingya Yang, Haodong Wei, Zhen He, Qian Song, Cong Wang, Gaofei Yin and Baodong Xu

Remote Sens. 2022, 14(13), 3067; https://doi.org/10.3390/rs14133067 - 26 Jun 2022

Cited by 15 | Viewed by 2376

Abstract

Reliable cropland parcel data are vital for agricultural monitoring, yield estimation, and agricultural intensification assessments. However, the inherently high landscape fragmentation and irregularly shaped cropland associated with smallholder farming systems restrict the accuracy of cropland parcels extraction. In this study, we proposed an adaptive image segmentation method with the automated selection of optimal scale (MSAOS) to extract cropland parcels in heterogeneous agricultural landscapes. The MSAOS method includes three major components: (1) coarse segmentation to divide the whole images into homogenous and heterogeneous regions, (2) fine segmentation to determine the optimal segmentation scale based on average local variance function, and (3) region merging to merge and dissolve the over-segmented objects with small area. The potential cropland objects derived from MSAOS were combined with random forest to generate the final cropland parcels. The MSAOS method was evaluated over different agricultural regions in China, and derived results were assessed by benchmark cropland parcels interpreted from high-spatial resolution images. Results showed the texture features of Homogeneity and Entropy are the most important features for MSAOS to extract potential cropland parcels, with the highest separability index of 0.28 and 0.26, respectively. MSAOS-derived cropland parcels had high agreement with the reference dataset over eight tiles in Qichun county, with average F1 scores of 0.839 and 0.779 for the area-based classification evaluation (F_ab) and object-based segmentation evaluation (F_ob), respectively. The further evaluation of MSAOS on different tiles of four provinces exhibited the similar results (F_ab = 0.857 and F_ob = 0.775) with that on eight test tiles, suggesting the good transferability of the MSAOS over different agricultural regions. Furthermore, MSAOS outperformed other widely-used approaches in terms of the accuracy and integrity of the extracted cropland parcels. These results indicate the great potential of using MSAOS for image segmentation in conjunction with random forest classification to effectively extract cropland parcels in smallholder farming systems. Full article

(This article belongs to the Special Issue Remote Sensing for Mapping Farmland and Agricultural Infrastructure)

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19 pages, 5404 KiB

Open AccessTechnical Note

Mapping Diverse Paddy Rice Cropping Patterns in South China Using Harmonized Landsat and Sentinel-2 Data

by Jie Hu, Yunping Chen, Zhiwen Cai, Haodong Wei, Xinyu Zhang, Wei Zhou, Cong Wang, Liangzhi You and Baodong Xu

Remote Sens. 2023, 15(4), 1034; https://doi.org/10.3390/rs15041034 - 14 Feb 2023

Cited by 4 | Viewed by 2137

Abstract

Paddy rice cropping patterns (PRCPs) play important roles in both agroecosystem modeling and food security. Although paddy rice maps have been generated over several regions using satellite observations, few studies have focused on mapping diverse smallholder PRCPs, which include crop rotation and are dominant cropping structures in South China. Here, an approach called the feature selection and hierarchical classification (FSHC) method was proposed to effectively identify paddy rice and its rotation types. Considering the cloudy and rainy weather in South China, a harmonized Landsat and Sentinel-2 (HLS) surface reflectance product was employed to increase high-quality observations. The FSHC method consists of three processes: cropping intensity mapping, feature selection, and decision tree (DT) model development. The FSHC performance was carefully evaluated using crop field samples obtained in 2018 and 2019. Results suggested that the derived cropping intensity map based on the Savitzky–Golay (S-G) filtered normalized difference vegetation index (NDVI) time series was reliable, with an overall accuracy greater than 93%. Additionally, the optimal spectral (i.e., normalized difference water index (NDWI) and land surface water index (LSWI)) and temporal (start-of-season (SOS) date) features for distinguishing different PRCPs were successfully identified, and these features are highly related to the critical growth stage of paddy rice. The developed DT model with three hierarchical levels based on optimal features performed satisfactorily, and the identification accuracy of each PRCP can be achieved approximately 85%. Furthermore, the FSHC method exhibited similar performances when mapping PRCPs in adjacent years. These results demonstrate that the proposed FSHC approach with HLS data can accurately extract diverse PRCPs over fragmented croplands; thus, this approach represents a promising opportunity for generating refined crop type maps. Full article

(This article belongs to the Special Issue Remote Sensing for Mapping Farmland and Agricultural Infrastructure)

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