Special Issue "Near Real-Time (NRT) Agriculture Monitoring"

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: 1 May 2022.

Special Issue Editors

Dr. Liang Sun
E-Mail Website
Chief Guest Editor
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: multi-satellite data fusion; agriculture monitoring; yield prediction; evapotranspiration
Dr. Feng Gao
E-Mail Website
Guest Editor
Hydrology and Remote Sensing Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA
Interests: crop phenology; yield mapping; crop monitoring; data fusion; land surface modeling; land cover and land use change
Special Issues, Collections and Topics in MDPI journals
Dr. Wenbin Wu
E-Mail Website
Guest Editor
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: smart agriculture; agricultural system; crop mapping; climate change

Special Issue Information

Dear Colleagues,

Near-real-time (NRT) agriculture monitoring can provide immediate crop information, which is vital for agriculture management and decision support. Capturing signal of crop stress at early stages will  help the farmers and decision makers to mitigate agricultural loss. An increasing availability of data acquired from satellites, unmanned aerial vehicles, and proximal sensors in the farmland has given us great opportunities to accomplish agricultural monitoring in near real-time. However, the requirements of NRT monitoring vary with application and with scale, from continental and regional scale to farm and field scale. In addition, a cloudy cover can limit the frequency of clear sky observations during the critical growing period, thus adding latency to the imagery used in NRT monitoring. Due to the diverse and complex set agricultural remote sensing monitoring indicators available, and coupled with rapid changes during the crop growth season, there are great demands for the effective use of remote sensing satellite observations, advanced multi-source data processing methods, and convenient joint data inversion. Recent advancements in remotely sensed data collection enable and inspire us to develop new algorithms for agricultural applications using data mining and machine learning techniques. This Special Issue focuses on novel methods and applications for agricultural monitoring in near real-time (within the season) using remote sensing. The contributions may include (1) crop type early mapping; (2) crop growing condition and crop phenology detection; (3) crop stress (water, nutrient, etc.) identification; (4) crop yield prediction; (5) soil water, fertility monitoring; and (6) data processing methods to achieve timely and high-quality monitoring within the season.

Dr. Liang Sun
Dr. Feng Gao
Dr. Wenbin Wu
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 papers will be 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.

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Keywords

  • near real-time (NRT)
  • early crop mapping
  • crop stress
  • crop phenology
  • yield prediction
  • soil monitoring
  • data fusion
  • time-series analysis

Published Papers (3 papers)

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Research

Article
An Object- and Topology-Based Analysis (OTBA) Method for Mapping Rice-Crayfish Fields in South China
Remote Sens. 2021, 13(22), 4666; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224666 - 19 Nov 2021
Viewed by 264
Abstract
The rice-crayfish field (i.e., RCF), a newly emerging rice cultivation pattern, has greatly expanded in China in the last decade due to its significant ecological and economic benefits. The spatial distribution of RCFs is an important dataset for crop planting pattern adjustment, water [...] Read more.
The rice-crayfish field (i.e., RCF), a newly emerging rice cultivation pattern, has greatly expanded in China in the last decade due to its significant ecological and economic benefits. The spatial distribution of RCFs is an important dataset for crop planting pattern adjustment, water resource management and yield estimation. Here, an object- and topology-based analysis (OTBA) method, which considers spectral-spatial features and the topological relationship between paddy fields and their enclosed ditches, was proposed to identify RCFs. First, we employed an object-based method to extract crayfish breeding ditches using very high-resolution images. Subsequently, the paddy fields that provide fodder for crayfish were identified according to the topological relationship between the paddy field and circumjacent crayfish ditch. The extracted ditch objects together with those paddy fields were merged to derive the final RCFs. The performance of the OTBA method was carefully evaluated using the RCF and non-RCF samples. Moreover, the effects of different spatial resolutions, spectral bands and temporal information on RCF identification were comprehensively investigated. Our results suggest the OTBA method performed well in extracting RCFs, with an overall accuracy of 91.77%. Although the mapping accuracies decreased as the image spatial resolution decreased, satisfactory RCF mapping results (>80%) can be achieved at spatial resolutions greater than 2 m. Additionally, we demonstrated that the mapping accuracy can be improved by more than 10% when near-infrared (NIR) band information was involved, indicating the necessity of the NIR band when selecting images to derive reliable RCF maps. Furthermore, the images acquired in the rice growth phase are recommended to maximize the differences of spectral characteristics between paddy fields and ditches. These promising findings suggest that the OTBA approach performs well for mapping RCFs in areas with fragmented agricultural landscapes, which provides fundamental information for further agricultural land use and water resources management. Full article
(This article belongs to the Special Issue Near Real-Time (NRT) Agriculture Monitoring)
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Article
Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information
Remote Sens. 2021, 13(14), 2790; https://doi.org/10.3390/rs13142790 - 15 Jul 2021
Viewed by 844
Abstract
Accurate crop type maps play an important role in food security due to their widespread applicability. Optical time series data (TSD) have proven to be significant for crop type mapping. However, filling in missing information due to clouds in optical imagery is always [...] Read more.
Accurate crop type maps play an important role in food security due to their widespread applicability. Optical time series data (TSD) have proven to be significant for crop type mapping. However, filling in missing information due to clouds in optical imagery is always needed, which will increase the workload and the risk of error transmission, especially for imagery with high spatial resolution. The development of optical imagery with high temporal and spatial resolution and the emergence of deep learning algorithms provide solutions to this problem. Although the one-dimensional convolutional neural network (1D CNN), long short-term memory (LSTM), and gate recurrent unit (GRU) models have been used to classify crop types in previous studies, their ability to identify crop types using optical TSD with missing information needs to be further explored due to their different mechanisms for handling invalid values in TSD. In this research, we designed two groups of experiments to explore the performances and characteristics of the 1D CNN, LSTM, GRU, LSTM-CNN, and GRU-CNN models for crop type mapping using unfilled Sentinel-2 (Sentinel-2) TSD and to discover the differences between unfilled and filled Sentinel-2 TSD based on the same algorithm. A case study was conducted in Hengshui City, China, of which 70.3% is farmland. The results showed that the 1D CNN, LSTM-CNN, and GRU-CNN models achieved acceptable classification accuracies (above 85%) using unfilled TSD, even though the total missing rate of the sample values was 43.5%; these accuracies were higher and more stable than those obtained using filled TSD. Furthermore, the models recalled more samples on crop types with small parcels when using unfilled TSD. Although LSTM and GRU models did not attain accuracies as high as the other three models using unfilled TSD, their results were almost close to those with filled TSD. This research showed that crop types could be identified by deep learning features in Sentinel-2 dense time series images with missing information due to clouds or cloud shadows randomly, which avoided spending a lot of time on missing information reconstruction. Full article
(This article belongs to the Special Issue Near Real-Time (NRT) Agriculture Monitoring)
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Article
Mapping Threats of Spring Frost Damage to Tea Plants Using Satellite-Based Minimum Temperature Estimation in China
Remote Sens. 2021, 13(14), 2713; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142713 - 09 Jul 2021
Viewed by 525
Abstract
Spring frost damage (SFD), defined as the disaster during the period of newly formed tea buds in spring caused by lower temperature and frost damage, is a particular challenge for tea plants (Camellia sinensis), whose capacity to adapt to extreme weather [...] Read more.
Spring frost damage (SFD), defined as the disaster during the period of newly formed tea buds in spring caused by lower temperature and frost damage, is a particular challenge for tea plants (Camellia sinensis), whose capacity to adapt to extreme weather and climate impacts is limited. In this paper, the region of the Middle and Lower Reaches of the Yangtze River (MLRYR) in China was selected as the major tea plantation study area, and the study period was focused on the concentrated occurrence of SFD, i.e., from March to April. By employing the standard lapse rate of air temperature with elevation, a minimum temperature (Tmin) estimation model that had been previously established was used based on reconstructed MYD11A1 nighttime LST values for 3 × 3 pixel windows and digital elevation model data. Combined with satellite-based Tmin estimates and ground-based Tmin observations, the spatiotemporal characteristics of SFD for tea plants were systematically analyzed from 2003 to 2020 in the MLRYR. The SFD risks at three scales (temporal, spatial, and terrain) were then evaluated for tea plants over the MLRYR. The results show that both SFD days at the annual scale and SFD areas at the daily scale exhibited a decreasing trend at a rate of 2.7 days/decade and 2.45 × 104 ha/day, respectively (significant rates at the 0.05 and 0.01 levels, respectively). The period with the highest SFD risk appeared mainly in the first twenty days of March. However, more attention should be given to the mid-to-late April time period due to the occurrence of late SFD from time to time. Spatially, areas with relatively higher SFD days and SFD risks were predominantly concentrated in the higher altitude areas of northwestern parts of MLRYR for both multi-year averages and individual years. Fortunately, in regions with a higher risk of SFD, the distribution of tea plants was relatively scattered and the area was small. These findings will provide helpful guidance for all kinds of people, including government agencies, agricultural insurance agencies, and tea farmers, in order that reasonable and effective strategies to reduce losses caused by spring frost damage to tea plants may be recommended and implemented. Full article
(This article belongs to the Special Issue Near Real-Time (NRT) Agriculture Monitoring)
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