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High Resolution Image Time Series for Novel Agricultural Applications

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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 (30 November 2018) | Viewed by 112930

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Special Issue Editors

Dr. Agnes Begue

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Guest Editor

CIRAD, UMR TETIS, Maison de la Télédétection, Montpellier, France
Interests: advanced remote sensing techniques for vegetation monitoring and dynamics; remote sensing for agriculture; food security (crop acreage and crop conditions); early warning systems; NDVI trend analysis

Dr. Valentine Lebourgeois

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Guest Editor

Cirad, UMR TETIS (Land, Environment, Remote Sensing and Spatial Information), Maison de la Télédétection, 500 Rue J-F. Breton, 34000 Montpellier, France
Interests: remote sensing for agriculture; land use mapping; crop characterization; food security
Special Issues, Collections and Topics in MDPI journals

Dr. Raffaele Gaetano

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Guest Editor

CIRAD, UMR TETIS, Maison de la Télédétection, Montpellier, France
Interests: image processing; remote sensing; multi-sensor data fusion; machine and deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Gérard Dedieu

E-Mail Website
Guest Editor

CNES, UMR CESBIO, 18 av. Edouard Belin, Toulouse, France
Interests: remote sensing time series for agriculture, agro-ecology and environment monitoring; development of applications with actors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For many years, the importance of cropping practices, in terms of productivity, natural resources use and farmer income, has been largely recognized by the international community, and the needs of spatialized information about agricultural practices are expected to grow rapidly. Likewise, the need for accurate and timely information on crop production to supply food security programs, such as GEOGLAM, becomes increasingly important, especially in countries at risk.

In terms of vegetation monitoring, annual crops are certainly the most demanding vegetation type for frequent observations. A close monitoring of vegetation growth at the field scale can give access to precise crop phenology and crop conditions at various key development stages, improving the identification of the cropping practices, and the estimation of biomass and yield through data assimilation in crop models. Thanks to the recent availability of the freely accessible EU Sentinel 1 and 2 data, and the development of the UAV technologies, applications can now be developed for a large range of agricultural systems, from smallholders to agro-industry.

This Special Issue will focus on the new applications for agriculture that are made possible thanks to these high and very high resolution optical and radar dense time series. We expect papers that present: 1. novel methods, based on single or multi-sensor time series and/or multi-source (remote sensing data, ancillary data, and expertise) approaches to go beyond the state of the art in terms of crop type mapping (for example a finer nomenclature) and yield estimation, or 2. novel applications based on satellite time series focusing on agricultural land use (identification of crop management techniques, agro-ecology, landscape epidemiology, etc.). The list below provides a general (but not exhaustive) overview of these two topics that are solicited for this Special Issue:

Fusion of multi-sensor observations (optical-radar time series, very high resolution and high resolution time series, etc.)
Fusion of multi-source data (satellite, airborne, environmental data, surveys, and local knowledge)
New information extraction techniques for very large data sets (data mining, deep learning)
Value-added of time series for cropland, crop group or crop type characterization and mapping
Combining remote sensing data and empirical or process based models for the estimation of yield, carbon and water fluxes or water use efficiency, for instance
Detection and identification of cropping patterns or other crop management practices
Mapping and characterization of agricultural systems/landscapes at larger scales

Dr. Agnès Bégué
Dr. Valentine Lebourgeois
Dr. Raffaele Gaetano
Mr. Gérard Dedieu
Guest Editors

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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.

Published Papers (13 papers)

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Research

26 pages, 5722 KiB

Open AccessArticle

Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

by Cecília Lira Melo de Oliveira Santos, Rubens Augusto Camargo Lamparelli, Gleyce Kelly Dantas Araújo Figueiredo, Stéphane Dupuy, Julie Boury, Ana Cláudia dos Santos Luciano, Ricardo da Silva Torres and Guerric le Maire

Remote Sens. 2019, 11(3), 334; https://doi.org/10.3390/rs11030334 - 08 Feb 2019

Cited by 38 | Viewed by 10234

Abstract

Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km² in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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24 pages, 14388 KiB

Open AccessArticle

Cropland Mapping Using Fusion of Multi-Sensor Data in a Complex Urban/Peri-Urban Area

by Eunice Nduati, Yuki Sofue, Akbar Matniyaz, Jong Geol Park, Wei Yang and Akihiko Kondoh

Remote Sens. 2019, 11(2), 207; https://doi.org/10.3390/rs11020207 - 21 Jan 2019

Cited by 15 | Viewed by 5129

Abstract

Urban and Peri-urban Agriculture (UPA) has recently come into sharp focus as a valuable source of food for urban populations. High population density and competing land use demands lend a spatiotemporally dynamic and heterogeneous nature to urban and peri-urban croplands. For the provision of information to stakeholders in agriculture and urban planning and management, it is necessary to characterize UPA by means of regular mapping. In this study, partially cloudy, intermittent moderate resolution Landsat images were acquired for an area adjacent to the Tokyo Metropolis, and their Normalized Difference Vegetation Index (NDVI) was computed. Daily MODIS 250 m NDVI and intermittent Landsat NDVI images were then fused, to generate a high temporal frequency synthetic NDVI data set. The identification and distinction of upland croplands from other classes (including paddy rice fields), within the year, was evaluated on the temporally dense synthetic NDVI image time-series, using Random Forest classification. An overall classification accuracy of 91.7% was achieved, with user’s and producer’s accuracies of 86.4% and 79.8%, respectively, for the cropland class. Cropping patterns were also estimated, and classification of peanut cultivation based on post-harvest practices was assessed. Image spatiotemporal fusion provides a means for frequent mapping and continuous monitoring of complex UPA in a dynamic landscape. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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14 pages, 2981 KiB

Open AccessArticle

In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series

by Valérie Demarez, Florian Helen, Claire Marais-Sicre and Frédéric Baup

Remote Sens. 2019, 11(2), 118; https://doi.org/10.3390/rs11020118 - 10 Jan 2019

Cited by 70 | Viewed by 6934

Abstract

Numerous studies have reported the use of multi-spectral and multi-temporal remote sensing images to map irrigated crops. Such maps are useful for water management. The recent availability of optical and radar image time series such as the Sentinel data offers new opportunities to map land cover with high spatial and temporal resolutions. Early identification of irrigated crops is of major importance for irrigation scheduling, but the cloud coverage might significantly reduce the number of available optical images, making crop identification difficult. SAR image time series such as those provided by Sentinel-1 offer the possibility of improving early crop mapping. This paper studies the impact of the Sentinel-1 images when used jointly with optical imagery (Landsat8) and a digital elevation model of the Shuttle Radar Topography Mission (SRTM). The study site is located in a temperate zone (southwest France) with irrigated maize crops. The classifier used is the Random Forest. The combined use of the different data (radar, optical, and SRTM) improves the early classifications of the irrigated crops (k = 0.89) compared to classifications obtained using each type of data separately (k = 0.84). The use of the DEM is significant for the early stages but becomes useless once crops have reached their full development. In conclusion, compared to a “full optical” approach, the “combined” method is more robust over time as radar images permit cloudy conditions to be overcome. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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17 pages, 4135 KiB

Open AccessArticle

Decreasing Rice Cropping Intensity in Southern China from 1990 to 2015

by Min Jiang, Liangjie Xin, Xiubin Li, Minghong Tan and Renjing Wang

Remote Sens. 2019, 11(1), 35; https://doi.org/10.3390/rs11010035 - 26 Dec 2018

Cited by 49 | Viewed by 5865

Abstract

Assessing changes in rice cropping systems is essential for ensuring food security, greenhouse gas emissions, and sustainable water management. However, due to the insufficient availability of images with moderate to high spatial resolution, caused by frequent cloud cover and coarse temporal resolution, high-resolution maps of rice cropping systems at a large scale are relatively limited, especially in tropical and subtropical regions. This study combined the difference of Normalized Difference Vegetation Index (dNDVI) method and the Normalized Difference Vegetation Index (NDVI) threshold method to monitor changes in rice cropping systems of Southern China using Landsat images, based on the phenological differences between different rice cropping systems. From 1990–2015, the sown area of double cropping rice (DCR) in Southern China decreased by 61054.5 km², the sown area of single cropping rice (SCR) increased by 20,110.7 km², the index of multiple cropping decreased from 148.3% to 129.3%, and the proportion of DCR decreased by 20%. The rice cropping systems in Southern China showed a “double rice shrinking and single rice expanding” change pattern from north to south, and the most dramatic changes occurred in the Middle-Lower Yangtze Plain. This study provided an efficient strategy that can be applied to moderate to high resolution images with deficient data availability, and the resulting maps can be used as data support to adjust agricultural structures, formulate food security strategies, and compile a greenhouse gas emission inventory. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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18 pages, 6511 KiB

Open AccessArticle

The Use of Photogrammetry to Construct Time Series of Vegetation Permeability to Water and Seed Transport in Agricultural Waterways

by Fabrice Vinatier, Jeanne Dollinger, Gabrielle Rudi, Denis Feurer, Gilles Belaud and Jean-Stéphane Bailly

Remote Sens. 2018, 10(12), 2050; https://doi.org/10.3390/rs10122050 - 17 Dec 2018

Cited by 10 | Viewed by 3631

Abstract

Terrestrial vegetation has numerous positive effects on the main regulating services of agricultural channels, such as seed retention, pollutant mitigation, bank stabilization, and sedimentation, and this vegetation acts as a porous medium for the flow of matter through the channels. This vegetation also limits the water conveyance in channels, and consequently is frequently removed by farmers to increase its porosity. However, the temporal effects of these management practices remain poorly understood. Indeed, the vegetation porosity exhibits important temporal variations according to the maintenance schedule, and the water level also varies with time inside a given channel section according to rainfall events or irrigation practices. To maximise the impacts of vegetation on agricultural channels, it is now of primary importance to measure vegetation porosity according to water level over a long time period rather than at a particular time. Time series of such complex vegetation characteristics have never been studied using remote sensing methods. Here, we present a new approach using the Structure-from-Motion approach using a Multi-View Stereo algorithm (SfM-MVS) technique to construct time series of herbaceous vegetation porosity in a real agricultural channel managed by five different practices: control, dredging, mowing, burning, and chemical weeding. We post-processed the time series of point clouds to create an indicator of vegetation porosity for the whole section and of the surface of the channel. Mowing and chemical weeding are the practices presenting the most favorable temporal evolutions of the porosity indicators regarding flow events. Burning did not succeed in restoring the porosity of the channel due to quick recovery of the vegetation and dephasing of the maintenance calendar with the flow events. The high robustness of the technique and the automatization of the SfM-MVS calculation together with the post-processing of the point clouds should help in handling time series of SfM-MVS data for applications in ecohydrology or agroecology. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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22 pages, 8017 KiB

Open AccessArticle

Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

by Kristof Van Tricht, Anne Gobin, Sven Gilliams and Isabelle Piccard

Remote Sens. 2018, 10(10), 1642; https://doi.org/10.3390/rs10101642 - 16 Oct 2018

Cited by 223 | Viewed by 16942

Abstract

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security and allowing early crop monitoring practices. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here, we used joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical inputs across the country, Sentinel-1 12-day backscatter mosaics were created after incidence angle normalization, and Sentinel-2 normalized difference vegetation index (NDVI) images were smoothed to yield 10-daily cloud-free mosaics. An optimized random forest classifier predicted the eight crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types were largest. Furthermore, we showed that the concept of classification confidence derived from the random forest classifier provided insight into the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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22 pages, 8074 KiB

Open AccessArticle

Using Time Series of High-Resolution Planet Satellite Images to Monitor Grapevine Stem Water Potential in Commercial Vineyards

by David Helman, Idan Bahat, Yishai Netzer, Alon Ben-Gal, Victor Alchanatis, Aviva Peeters and Yafit Cohen

Remote Sens. 2018, 10(10), 1615; https://doi.org/10.3390/rs10101615 - 11 Oct 2018

Cited by 54 | Viewed by 9342

Abstract

Spectral-based vegetation indices (VI) have been shown to be good proxies of grapevine stem water potential (Ψ_stem), assisting in irrigation decision-making for commercial vineyards. However, VI-Ψ_stem correlations are mostly reported at the leaf or canopy scales, using proximal canopy-based sensors or very-high-spatial resolution images derived from sensors mounted on small airplanes or drones. Here, for the first time, we take advantage of high-spatial resolution (3-m) near-daily images acquired from Planet’s nano-satellite constellation to derive VI-Ψ_stem correlations at the vineyard scale. Weekly Ψ_stem was measured along the growing season of 2017 in six vines each in 81 commercial vineyards and in 60 pairs of grapevines in a 2.4 ha experimental vineyard in Israel. The Clip application programming interface (API), provided by Planet, and the Google Earth Engine platform were used to derive spatially continuous time series of four VIs—GNDVI, NDVI, EVI and SAVI—in the 82 vineyards. Results show that per-week multivariable linear models using variables extracted from VI time series successfully tracked spatial variations in Ψ_stem across the experimental vineyard (Pearson’s-r = 0.45–0.84; N = 60). A simple linear regression model enabled monitoring seasonal changes in Ψ_stem along the growing season in the vineyard (r = 0.80–0.82). Planet VIs and seasonal Ψ_stem data from the 82 vineyards were used to derive a ‘global’ model for in-season monitoring of Ψ_stem at the vineyard-level (r = 0.78; RMSE = 18.5%; N = 970). The ‘global’ model, which requires only a few VI variables extracted from Planet images, may be used for real-time weekly assessment of Ψ_stem in Mediterranean vineyards, substantially improving the efficiency of conventional in-field monitoring efforts. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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22 pages, 4427 KiB

Open AccessArticle

Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery

by Feng Gao, Martha Anderson, Craig Daughtry and David Johnson

Remote Sens. 2018, 10(9), 1489; https://doi.org/10.3390/rs10091489 - 18 Sep 2018

Cited by 72 | Viewed by 7801

Abstract

The utility of remote sensing data in crop yield modeling has typically been evaluated at the regional or state level using coarse resolution (>250 m) data. The use of medium resolution data (10–100 m) for yield estimation at field scales has been limited due to the low temporal sampling frequency characteristics of these sensors. Temporal sampling at a medium resolution can be significantly improved, however, when multiple remote sensing data sources are used in combination. Furthermore, data fusion approaches have been developed to blend data from different spatial and temporal resolutions. This paper investigates the impacts of improved temporal sampling afforded by multi-source datasets on our ability to explain spatial and temporal variability in crop yields in central Iowa (part of the U.S. Corn Belt). Several metrics derived from vegetation index (VI) time-series were evaluated using Landsat-MODIS fused data from 2001 to 2015 and Landsat-Sentinel2-MODIS fused data from 2016 and 2017. The fused data explained the yield variability better, with a higher coefficient of determination (R²) and a smaller relative mean absolute error than using a single data source alone. In this study area, the best period for the yield prediction for corn and soybean was during the middle of the growing season from day 192 to 236 (early July to late August, 1–3 months before harvest). These findings emphasize the importance of high temporal and spatial resolution remote sensing data in agricultural applications. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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16 pages, 3723 KiB

Open AccessArticle

Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France

by Emile Ndikumana, Dinh Ho Tong Minh, Nicolas Baghdadi, Dominique Courault and Laure Hossard

Remote Sens. 2018, 10(8), 1217; https://doi.org/10.3390/rs10081217 - 03 Aug 2018

Cited by 214 | Viewed by 13633

Abstract

The development and improvement of methods to map agricultural land cover are currently major challenges, especially for radar images. This is due to the speckle noise nature of radar, leading to a less intensive use of radar rather than optical images. The European Space Agency Sentinel-1 constellation, which recently became operational, is a satellite system providing global coverage of Synthetic Aperture Radar (SAR) with a 6-days revisit period at a high spatial resolution of about 20 m. These data are valuable, as they provide spatial information on agricultural crops. The aim of this paper is to provide a better understanding of the capabilities of Sentinel-1 radar images for agricultural land cover mapping through the use of deep learning techniques. The analysis is carried out on multitemporal Sentinel-1 data over an area in Camargue, France. The data set was processed in order to produce an intensity radar data stack from May 2017 to September 2017. We improved this radar time series dataset by exploiting temporal filtering to reduce noise, while retaining as much as possible the fine structures present in the images. We revealed that even with classical machine learning approaches (K nearest neighbors, random forest, and support vector machines), good performance classification could be achieved with F-measure/Accuracy greater than 86% and Kappa coefficient better than 0.82. We found that the results of the two deep recurrent neural network (RNN)-based classifiers clearly outperformed the classical approaches. Finally, our analyses of the Camargue area results show that the same performance was obtained with two different RNN-based classifiers on the Rice class, which is the most dominant crop of this region, with a F-measure metric of 96%. These results thus highlight that in the near future these RNN-based techniques will play an important role in the analysis of remote sensing time series. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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16 pages, 6323 KiB

Open AccessArticle

Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping

by Nathan Torbick, Xiaodong Huang, Beth Ziniti, David Johnson, Jeff Masek and Michele Reba

Remote Sens. 2018, 10(7), 1058; https://doi.org/10.3390/rs10071058 - 04 Jul 2018

Cited by 43 | Viewed by 8128

Abstract

Crop type inventory and within season estimates at moderate (<30 m) resolution have been elusive in many regions due to the lack of temporal frequency, clouds, and restrictive data policies. New opportunities exist from the operational fusion of Landsat-8 Operational Land Imager (OLI), Sentinel-2 (A & B), and Sentinel-1 (A & B) which provide more frequent open access observations now that these satellites are fully operating. The overarching goal of this research application was to compare Harmonized Landsat-8 Sentinel-2 (HLS), Sentinel-1 (S1), and combined radar and optical data in an operational, near-real-time (within 24 h) context. We evaluated the ability of these Earth observations (EO) across major crops in four case study regions in United States (US) production hot spots. Hindcast time series combinations of these EO were fed into random forest classifiers trained with crop cover type information from the Cropland Data Layer (CDL) and ancillary ground truth. The outcomes show HLS achieved high (>85%) accuracies and the ability to provide insight on crop location and extent within the crop season. HLS fused with S1 had, at times, a higher accuracy (5–10% relative overall accuracy and kappa increases) within season although the combination of fused data was minimal at times, crop dependent, and the accuracies tended to converge by harvest. In cloud prone regions and certain temporal periods, S1 performed well overall. The growth in the availability of time dense moderate resolution data streams and different sensitivities of optical and radar data provide a mechanism for within season crop mapping and area estimates that can help improve food security. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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21 pages, 5667 KiB

Open AccessArticle

Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy

by Vasileios Sitokonstantinou, Ioannis Papoutsis, Charalampos Kontoes, Alberto Lafarga Arnal, Ana Pilar Armesto Andrés and José Angel Garraza Zurbano

Remote Sens. 2018, 10(6), 911; https://doi.org/10.3390/rs10060911 - 08 Jun 2018

Cited by 82 | Viewed by 8821

Abstract

This work investigates a Sentinel-2 based crop identification methodology for the monitoring of the Common Agricultural Policy’s (CAP) Cross Compliance (CC) and Greening obligations. In this regard, we implemented and evaluated a parcel-based supervised classification scheme to produce accurate crop type mapping in a smallholder agricultural zone in Navarra, Spain. The scheme makes use of supervised classifiers Support Vector Machines (SVMs) and Random Forest (RF) to discriminate among the various crop types, based on a large variable space of Sentinel-2 imagery and Vegetation Index (VI) time-series. The classifiers are separately applied at three different levels of crop nomenclature hierarchy, comparing their performance with respect to accuracy and execution time. SVM provides optimal performance and proves significantly superior to RF for the lowest level of the nomenclature, resulting in 0.87 Cohen’s kappa coefficient. Experiments were carried out to assess the importance of input variables, where top contributors are the Near Infrared (NIR), vegetation red-edge, and Short-Wave Infrared (SWIR) multispectral bands, and the Normalized Difference Vegetation (NDVI) and Plant Senescence Reflectance (PSRI) indices, sensed during advanced crop phenology stages. The scheme is finally applied to a Lansat-8 OLI based equivalent variable space, offering 0.70 Cohen’s kappa coefficient for the SVM classification, highlighting the superior performance of Sentinel-2 for this type of application. This is credited to Sentinel-2’s spatial, spectral, and temporal characteristics. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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22 pages, 8070 KiB

Open AccessArticle

Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data

by Seonyoung Park, Jungho Im, Seohui Park, Cheolhee Yoo, Hyangsun Han and Jinyoung Rhee

Remote Sens. 2018, 10(3), 447; https://doi.org/10.3390/rs10030447 - 12 Mar 2018

Cited by 115 | Viewed by 10783

Abstract

Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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24 pages, 9112 KiB

Open AccessArticle

Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data

by Wangfei Zhang, Erxue Chen, Zengyuan Li, Lei Zhao, Yongjie Ji, Yahong Zhang and Zhiqin Liu

Remote Sens. 2018, 10(2), 206; https://doi.org/10.3390/rs10020206 - 30 Jan 2018

Cited by 16 | Viewed by 4458

Abstract

In this study, 27 polarimetric parameters were extracted from Radarsat-2 polarimetric synthetic aperture radar (SAR) at each growth stage of the rape crop. The sensitivity to growth parameters such as stem height, leaf area index (LAI), and biomass were investigated as a function of days after sowing. Based on the sensitivity analysis, five empirical regression models were compared to determine the best model for stem height, LAI, and biomass inversion. Of these five models, quadratic models had higher R² values than other models in most cases of growth parameter inversions, but when these results were related to physical scattering mechanisms, the inversion results produced overestimation in the performance of some parameters. By contrast, linear and logarithmic models, which had lower R² values than the quadratic models, had stable performance for growth parameter inversions, particularly in terms of their performance at each growth stage. The best biomass inversion performance was acquired by the volume component of a quadratic model, with an R² value of 0.854 and root mean square error (RMSE) of 109.93 g m⁻². The best LAI inversion was also acquired by a quadratic model, but used the radar vegetation index (Cloude), with an R² value of 0.8706 and RMSE of 0.56 m² m⁻². Stem height was acquired by scattering angle alpha (

α

) using a logarithmic model, with an R² of 0.926 value and RMSE of 11.09 cm. The performances of these models were also analysed for biomass estimation at the second growth stage (P2), third growth stage (P3), and fourth growth stage (P4). The results showed that the models built at the P3 stage had better substitutability with the models built during all of the growth stages. From the mapping results, we conclude that a model built at the P3 stage can be used for rape biomass inversion, with 90% of estimation errors being less than 100 g m⁻². Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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