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Cropland Phenology Monitoring Based on Cloud-Computing Platforms

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: 29 February 2024 | Viewed by 9482

Special Issue Editors

Laboratory for Earth Observation, Image Processing Laboratory - Scientific Park, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
Interests: imaging spectroscopy; vegetation properties retrieval; FLEX; vegetation fluorescence; optical remote sensing; radiative transfer models; retrieval methods
Special Issues, Collections and Topics in MDPI journals
1. Laboratory for Earth Observation, Image Processing Laboratory - Scientific Park, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
2. Mantle Labs GmbH, Vienna, Austria
Interests: agriculture; hybrid retrieval; hyperspectral remote sensing; machine learning methods; active learning
Special Issues, Collections and Topics in MDPI journals
Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, Enschede, The Netherlands
Interests: evapotranspiration; gross primary productivity; SCOPE model
Institute of Geomatics, University of Natural Resources and Life Sciences, 1090 Vienna, Austria
Interests: remote sensing of vegetation with focus on time series analysis and use of physically based radiative transfer models for mapping biochemical and biophysical traits
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring cropland phenological and seasonal dynamics is crucial to address sustainable agricultural production and hence global food security concerns. In addition, as climate change is one of the major pressures on Earth, globally assessing the phenology of cultivated lands is becoming of paramount relevance. Earth observation data helps to acquire phenology-related information frequently and on any place on Earth, enabling to trace crop development, which in itself is closely linked to crop production. Monitoring of cropland seasonal dynamics is thus a basic requirement to ensure food and nutritional security for a growing world population. Recently, significant progress has been achieved in mapping agricultural croplands using a multitude of satellite sensors at diverse spectral, spatial and temporal resolutions. To achieve this, cloud computing facilities have been introduced providing processing capabilities for deriving time-series of imagery over large areas from regional to global scales. Google Earth Engine (GEE) emerged hereby as an attractive high-performance platform, ingesting diverse machine learning algorithms to retrieve multidate cropland information, consistently acquired with sun-synchronous satellite data. Nowadays, GEE allows the cloud-based processing of petabytes of satellite data, thus providing powerful computational capacity for planetary-scale data processing. While multiple studies have recently been conducted using GEE for diverse applications in an agricultural context, we still see an untapped potential to integrate advanced image processing algorithms in cloud-computing environments, such as machine learning regression models and phenology detection methods. Introducing these algorithms in web-based platforms will open up capabilities for delivering unprecedented seasonal cropland monitoring services applicable anywhere and anytime in the world. 

Along with GEE, alternative cutting edge platforms, such as Amazon Web Services (AWS), Sentinel Hub, Open Data Cube (ODC) and OpenEO started to pave the way for moving from traditional image analysis using desktop PCs to cloud-based processing facilities.

This Special Issue encourages contributions aimed at estimating different phenology metrics, exploring time series data from optical and radar Earth Observation products in the context of agricultural disciplines and in particular exploring cloud computing platforms. We aim to cover diverse spatial scales, i.e. from local to global, using multiple sensors, such as MODIS, Copernicus Sentinel or Landsat families. The exploitation of advanced retrieval techniques is encouraged, ranging from empirical nonparametric (non)linear to physically-based (radiative transfer modelling) to hybrid methods, as well the usage of flexible smoothing and gap-filling methods. Apart from single-source time series processing, this Special Issue also encourages contributions where multiple EO time series sources are fused, e.g. for improved seasonality detection, such as harmonised Landsat and Sentinel products, as well fusion methods focusing on synergies between optical and radar data. Altogether, this Special Issue is expected to demonstrate recent progress of cloud based computing facilities and to discuss future perspectives in cropland traits sensing.

We welcome contributions that fall within the following themes (but are not limited to them): 

Themes: 

  • phenology metrics estimation using cloud computing platforms
  • analysis of time series dynamics  from optical and radar Earth Observation data
  • advanced gap-filling and smoothing  techniques in cloud computing platforms
  • time series fusion methods focusing on synergies between optical and radar data
  • cloud based computing facilities exploring cropland traits monitoring
  • Assessing and uncertainties in phenology metrics retrievals
  • Productivity and yield estimation based on time series processing

Article types: 

  • Research articles 
  • Review articles 
  • Short communications 
  • Technical notes

Dr. Jochem Verrelst
Dr. Katja Berger
Dr. Egor Prikaziuk
Dr. Clement Atzberger
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

  • Google Earth Engine
  • cropland phenology metrics
  • machine learning regression algorithms
  • cloud computing
  • hybrid methods
  • gap-filling

Published Papers (5 papers)

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Research

22 pages, 11066 KiB  
Article
Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine
Remote Sens. 2024, 16(4), 659; https://doi.org/10.3390/rs16040659 - 11 Feb 2024
Viewed by 425
Abstract
Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous [...] Read more.
Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous data collection. To address these challenges and fully leverage remote sensing big data and cloud computing platforms like Google Earth Engine (GEE), this paper developed an algorithm for Auto-Generating Winter Wheat Samples for mapping (AGWWS). The AGWWS utilizes historical samples to determine the optimal migration threshold by measuring Spectral Angle Distance (SAD), Euclidean Distance (ED), and Near-Infrared band Difference Index (NIRDI). This facilitates the auto-generation of winter wheat sample sets for the years 2000, 2005, 2010, 2015, and 2021. Approximately two-thirds of the samples were allocated for training, with the remaining one-third used for validating the mapping method, employing the One-Class Support Vector Machine (OCSVM). The Huang–Huai–Hai (HHH) Plain, a major winter wheat production region, was selected to perform the algorithm and subsequent analysis on. Different combinations of the hyper-parameters, gamma and nu, of the OCSVM based on the Gaussian Radial Basis Function Kernel were tested for each year. Following correlation analysis between the winter wheat area derived from the generated maps and the national statistical dataset at the city level, the map with the highest corresponding R2 was chosen as the AGWWS map for each year (0.77, 0.77, 0.80, 0.86, and 0.87 for 2000, 2005, 2010, 2015, and 2021, respectively). The AGWWS maps ultimately achieved an average Overall Accuracy of 81.65%. The study then explores the Non-Grain Production of Winter Wheat (NGPOWW) by analyzing winter wheat change maps from 2000–2005, 2005–2010, 2005–2010, and 2015–2021 in the HHH Plain. Despite an overall increase in the total planted area of winter wheat, the NGPOWW phenomena has led to concerning winter wheat planting marginalization. Compensatory winter wheat areas are notably situated in mountainous and suburban cultivated lands with low qualities. Consequently, despite the apparent expansion in planted areas, winter wheat production is anticipated to be adversely affected. The findings highlight the necessity for improved cultivated land protection policies monitoring the land quality of the compensation and setting strict quota limits on occupations. Full article
(This article belongs to the Special Issue Cropland Phenology Monitoring Based on Cloud-Computing Platforms)
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24 pages, 7092 KiB  
Article
A Robust Index Based on Phenological Features to Extract Sugarcane from Multisource Remote Sensing Data
Remote Sens. 2023, 15(24), 5783; https://doi.org/10.3390/rs15245783 - 18 Dec 2023
Viewed by 624
Abstract
Sugarcane is a major crop for sugar and biofuel production. Historically, mapping large sugarcane fields meticulously depended heavily on gathering comprehensive and representative training samples. This process was time-consuming and inefficient. Addressing this drawback, this study proposed a novel index, the Normalized Difference [...] Read more.
Sugarcane is a major crop for sugar and biofuel production. Historically, mapping large sugarcane fields meticulously depended heavily on gathering comprehensive and representative training samples. This process was time-consuming and inefficient. Addressing this drawback, this study proposed a novel index, the Normalized Difference Vegetation Index (NDVI)-Based Sugarcane Index (NBSI). NBSI analyzed the temporal variation of sugarcane’s NDVI over a year. Leveraging the distinct growth phases of sugarcane (transplantation, tillering, rapid growth and maturity) four measurement methodologies, f(W1), f(W2), f(V) and f(D), were developed to characterize the features of the sugarcane growth period. Utilizing imagery from Landsat-8, Sentinel-2, and MODIS, this study employed the enhanced gap-filling (EGF) method to reconstruct NDVI time-series data for seven counties in Chongzuo, Guangxi Zhuang Autonomous Region, during 2021, subsequently testing NBSI’s ability to extract sugarcane. The results demonstrate the efficiency of NBSI with simple threshold settings: it was able to map sugarcane cultivation areas, exhibiting higher accuracy when compared to traditional classifiers like support vector machines (SVM) and random forests (RF), with an overall accuracy (OA) of 95.24% and a Kappa coefficient of 0.93, significantly surpassing RF (OA = 85.31%, Kappa = 0.84) and SVM (OA = 85.87%, Kappa = 0.86). This confirms the outstanding generalizability and robustness of the proposed method in Chongzuo. Therefore, the NBSI methodology, recognized for its flexibility and practicality, shows potential in enabling the extensive mapping of sugarcane cultivation. This heralds a new paradigm of thought in this field. Full article
(This article belongs to the Special Issue Cropland Phenology Monitoring Based on Cloud-Computing Platforms)
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22 pages, 14239 KiB  
Article
Mapping Paddy Rice Planting Area in Dongting Lake Area Combining Time Series Sentinel-1 and Sentinel-2 Images
Remote Sens. 2023, 15(11), 2794; https://doi.org/10.3390/rs15112794 - 27 May 2023
Cited by 6 | Viewed by 1628
Abstract
Accurate and timely acquisition of cropping intensity and spatial distribution of paddy rice is not only an important basis for monitoring growth and predicting yields, but also for ensuring food security and optimizing the agricultural production management system of cropland. However, due to [...] Read more.
Accurate and timely acquisition of cropping intensity and spatial distribution of paddy rice is not only an important basis for monitoring growth and predicting yields, but also for ensuring food security and optimizing the agricultural production management system of cropland. However, due to the monsoon climate in southern China, it is cloudy and rainy throughout the year, which makes it difficult to obtain accurate information on rice cultivation based on optical time series images. Conventional image synthesis is prone to omission or redundancy of spectral and temporal features that are potentially important for rice-growth identification, making it difficult to determine the optimal features for high-precision mapping of paddy rice. To address these issues, we develop a method to granulate the effective use interval of classification features by extracting phenological signatures of rice to obtain cost-effective and highly accurate mapping results. Two steps are involved in this method: (1) analyzing the information on various features (spectra, polarization, and seasonal regularity) to identify three key phenological periods throughout the lifespan of paddy rice; (2) identifying the features with the highest class separation between paddy rice, non-paddy crops, and wetlands under different phenological stages; and (3) removing redundant features to retain the optimal feature combinations. Subsequently, the obtained feature sets are used as input data for the random forest classifier. The results showed that the overall accuracy of the identified rice results was 95.44% with F1 scores above 93% for both single- and double-cropping rice. Meanwhile, the correlation coefficient of our mapped rice area compared with the official statistics of rice area at county and district levels was 0.86. In addition, we found that combining Sentinel-1 and Sentinel-2 images for rice recognition was better than using Sentinel-1 or Sentinel-2 alone, and the classification accuracy was improved by 5.82% and 2.39%, which confirms that the synergistic Sentinel-1 and Sentinel-2 data can effectively overcome the problem of missing optical images caused by clouds and rain. Our study demonstrates the potential of distinguishing mixed rice-cropping systems in subtropical regions with fragmented rice-field distribution in a cloudy and rainy environment, and also provides a basis for the rational layout of rice production and improvement of cultivation systems. Full article
(This article belongs to the Special Issue Cropland Phenology Monitoring Based on Cloud-Computing Platforms)
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29 pages, 9780 KiB  
Article
Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
Remote Sens. 2023, 15(7), 1822; https://doi.org/10.3390/rs15071822 - 29 Mar 2023
Cited by 3 | Viewed by 2605
Abstract
Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to [...] Read more.
Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Sentinel-2 (S2) vegetation water content (VWC) time series over a dry agri-environment in southern Argentina. MOGP models not only exploit the auto-correlations of S1 and S2 data streams independently but also the inter-channel cross-correlations. The S1 RVI and S2 VWC time series at the selected study sites being the inputs of the MOGP models proved to be closely correlated. Regarding the set of assessed models, the Convolutional Gaussian model (CONV) delivered noteworthy accurate data fusion results over winter wheat croplands belonging to the 2020 and 2021 campaigns (NRMSEwheat2020 = 16.1%; NRMSEwheat2021 = 10.1%). Posteriorly, we removed S2 observations from the S1 & S2 dataset corresponding to the complete phenological cycles of winter wheat from September to the end of December to simulate the presence of clouds in the scenes and applied the CONV model at the pixel level to reconstruct spatiotemporally-latent VWC maps. After applying the fusion strategy, the phenology of winter wheat was successfully recovered in the absence of optical data. Strong correlations were obtained between S2 VWC and S1 & S2 MOGP VWC reconstructed maps for the assessment dates (R2¯wheat2020 = 0.95, R2¯wheat2021 = 0.96). Altogether, the fusion of S1 SAR and S2 optical EO data streams with MOGP offers a powerful innovative approach for cropland trait monitoring over cloudy high-latitude regions. Full article
(This article belongs to the Special Issue Cropland Phenology Monitoring Based on Cloud-Computing Platforms)
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31 pages, 38582 KiB  
Article
Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery
Remote Sens. 2022, 14(18), 4531; https://doi.org/10.3390/rs14184531 - 10 Sep 2022
Cited by 6 | Viewed by 2896
Abstract
Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop’s phenological cycle, which is particularly relevant for [...] Read more.
Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop’s phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R2 = 0.92, RMSE = 0.43 m2 m2, CCC: R2 = 0.80, RMSE = 0.27 g m2 and VWC: R2 = 0.75, RMSE = 416 g m2. The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions. Full article
(This article belongs to the Special Issue Cropland Phenology Monitoring Based on Cloud-Computing Platforms)
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