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Agronomy
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Mapping Croplands, Cropping Patterns, and Crop Types Using Satellite and...
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Mapping Croplands, Cropping Patterns, and Crop Types Using Satellite and UAV

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 3203

Special Issue Editor

Dr. Hesong Wang

E-Mail Website
Guest Editor
School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China
Interests: climate change; carbon and water flux; remote sensing of vegetation; vegetation dynamics; phenology and ecosystem productivity

Special Issue Information

Dear Colleagues,

The mapping of croplands, cropping patterns, and crop types is important in assessing food security. Satellite remote sensing provides an unprecedented solution to facilitating crop inventory and monitoring in order to map croplands at various spatial resolutions. Meanwhile, near-surface sensing technologies such as unmanned aerial sensing (UAV) are increasingly used as complementary platforms in agricultural mapping.

This Special Issue focuses on advanced remote sensing datasets, approaches to cropland classification, and further analysis related to cropland mapping. We welcome large-scale maps of croplands that include various kinds of cropping patterns or types, which may include single-crops, rotation, intercrops, fallow-crop rotation, and agroforestry. In addition, we are especially interested in uncertainties in the mapping of croplands, evaluations of vulnerability to disaster based on cropland maps, the application of UVA-based hyperspectral imaging, and 3D structure observation using SAR. We will accept submissions of original research, reviews, and case studies outlining recent progress in the above mentioned research areas.

Dr. Hesong Wang
Guest Editor

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. Agronomy is an international peer-reviewed open access monthly 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 2600 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 mapping
  • cropping patterns or types
  • UVA-based hyperspectral imaging
  • assessing food security
  • time-series analysis
  • agroforestry system
  • plant diseases and insect pests
  • evaluation of vulnerability to disturbance
  • uncertainties in cropland maps
  • crop yield estimation

Published Papers (2 papers)

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Research

15 pages, 3892 KiB  
Article
Regional Monitoring of Leaf ChlorophyII Content of Summer Maize by Integrating Multi-Source Remote Sensing Data
by Hongwei Tian, Lin Cheng, Dongli Wu, Qingwei Wei and Liming Zhu
Agronomy 2023, 13(8), 2040; https://doi.org/10.3390/agronomy13082040 - 31 Jul 2023
Viewed by 836
Abstract
This study addresses the problem of restricted ability for large-scale monitoring due to the limited cruising time of unmanned aerial vehicles (UAV) by identifying an optimal leaf ChlorophyII content (LCC) inversion machine learning model at different scales and under different parameterization schemes based [...] Read more.
This study addresses the problem of restricted ability for large-scale monitoring due to the limited cruising time of unmanned aerial vehicles (UAV) by identifying an optimal leaf ChlorophyII content (LCC) inversion machine learning model at different scales and under different parameterization schemes based on simultaneous observations of ground sampling, UAV flight, and satellite imagery. The following results emerged: (1) The correlation coefficient between most remote sensing features (RSFs) and LCC increased as the remote scale expanded; thus, the scale error caused by the random position difference between GPS and measuring equipment should be considered in field sampling observations. (2) The LCC simulation accuracy of the UAV multi-spectral camera using four machine learning algorithms was ExtraTree > GradientBoost > AdaBoost > RandomForest, and the 20- and 30-pixel scales had better accuracy than the 10-pixel scale, while the accuracy for three feature combination schemes ranked combination of extremely significantly correlated RSFs > combination of significantly correlated and above RSFs > combination of all features. ExtraTree was confirmed as the optimal model with the feature combination of scheme 2 at the 20-pixel scale. (3) Of the Sentinel-2 RSFs, 27 of 28 were extremely significantly correlated with LCC, while original band reflectance was negatively correlated, and VIs were positively correlated. (4) The LCC simulation accuracy of the four machine learning algorithms ranked as ExtraTree > GradientBoost > RandomForest > AdaBoost. In a comparison of two parameterization schemes, scheme 1 had better accuracy, while ExtraTree was the best algorithm, with 11 band reflectance as input RSFs; the RMSE values for the training and testing data sets of 0.7213 and 1.7198, respectively. Full article
(This article belongs to the Special Issue Mapping Croplands, Cropping Patterns, and Crop Types Using Satellite and UAV)
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19 pages, 4603 KiB  
Article
Monitoring of Cropland Abandonment Based on Long Time Series Remote Sensing Data: A Case Study of Fujian Province, China
by Jiayu Wu, Shaofei Jin, Gaolong Zhu and Jia Guo
Agronomy 2023, 13(6), 1585; https://doi.org/10.3390/agronomy13061585 - 12 Jun 2023
Cited by 3 | Viewed by 1737
Abstract
Farmland is the basis for human survival and development. The phenomenon of cropland abandonment has seriously affected national agricultural production and food security. In this study, remote sensing monitoring of abandoned cropland is carried out based on multisource time series remote sensing data [...] Read more.
Farmland is the basis for human survival and development. The phenomenon of cropland abandonment has seriously affected national agricultural production and food security. In this study, remote sensing monitoring of abandoned cropland is carried out based on multisource time series remote sensing data using the Google Earth Engine (GEE) cloud platform. Landsat and Sentinel-2 time series data from 2010–2021 were used to obtain monthly synthetic cloud-free image sets in combination with cropland plot data. The time series farmland probability dataset was generated using the random forest classification method. The LandTrendr algorithm was used to extract and analyse the time series cropland probability dataset. Finally, this study also explored the drivers of change in abandoned cropland in Fujian Province. The results show that (1) the LandTrendr algorithm can effectively extract abandoned farmland and avoid the impact of pseudovariation resulting from non-farmland categories. A total of 87.02% of the abandoned farmland was extracted in 2018; 87.50% of the abandoned farmland was extracted in 2020. (2) The abandoned area in Fujian Province fluctuated after a significant increase in 2012, with the abandoned area exceeding 30 thousand hectares. Since 2017, the abandoned area has decreased to slightly below 30 thousand hectares. (3) The regression results of the factors affecting abandoned cropland in Fujian Province show that the increase in the number of agricultural workers and the improvement in soil organic matter content will significantly reduce the area of abandoned cropland in Fujian Province, while the increase in the rate of urbanization, poor road accessibility, and insufficient irrigation conditions will increase the area of abandoned cropland. The results of this study are useful for conducting surveys of cropland abandonment and obtaining timely and accurate data on cropland abandonment. The results of this study are of great significance for the development of effective measures to stop the abandonment of cropland, and ensure the implementation of food security strategies. Full article
(This article belongs to the Special Issue Mapping Croplands, Cropping Patterns, and Crop Types Using Satellite and UAV)
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