Special Issue "Remote Sensing for Crop Mapping"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 1 May 2021.

Special Issue Editors

Dr. Steffen Fritz
Website
Guest Editor
International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Interests: remote sensing, cropland, crowdsourcing, mapping uncertainty, climate change, agricultural monitoring
Special Issues and Collections in MDPI journals
Dr. Qiong Hu
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Guest Editor
College of Urban and Environment Sciences, Huazhong Normal University, No.152 Luoyu Road, Wuhan 430079, China
Interests: crop mapping, land use change, feature selection, cropping system, agricultural intensification
Dr. Zhenong Jin
Website
Guest Editor
Department of Bioproducts and Biosystems Engineering, University of Minnesota, 1390 Eckles Ave., Room 200. St. Paul, MN 55108, USA
Interests: agricultural production system, climate change, crop yield mapping, crop model, precision nitrogen management, ecosystem service
Dr. Wenbin Wu
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: smart agriculture, agricultural system, crop mapping, climate change
Special Issues and Collections in MDPI journals
Dr. Liangzhi You
Website
Guest Editor
International Food Policy Research Institute, 1201 Eye Street NW, Washington, DC 20005, USA
Interests: farming system, remote sensing, land use change, spatial analysis, food policy, cropping system

Special Issue Information

Dear Colleagues,

In the last few decades, the advent of remote sensing (e.g., satellite and drones) has made it possible to assess and monitor the extent and status of cultivated land. Remote-sensing technologies are desirable because they have relatively low marginal cost; they provide higher levels of spatial resolution and sampling frequency compared to alternate approaches; are the only feasible data gathering mechanism in some locations with difficult or even no access; provide precise, automated repetition of data collection efforts; and can be combined with ground-based data collection to generate value-added products. Satellite imagery combined with ground-based data and spatial mapping tools can make an enormous difference to agricultural decision making at global, national, and local levels, by providing more timely and accurate information. Moreover, crop-type information is a critical input to cropping system models or integrated assessment models. To better understand our agroecosystem and guide policy making with these modeling tools, spatially explicit and detailed crop type maps are increasingly needed.

Furthermore, official datasets are becoming increasingly accessible. In particular, there have been many exciting activities in the last 5 years, and more and more national, regional, and global crop type datasets are being generated and shared. OneSoil, a startup in Minsk, Belarus, launches an interactive digital map of ag data including about 60 million fields across Europe and the United States, and data on more than 20 types of crops collected over the last three years (https://map.onesoil.ai/2018). Advances in remote sensing and the sen4agri project (http://www.esa-sen2agri.org/) have built a toolbox to map crop types using machine learnings (random forest) to classify Sentinel 2 images. More recent approaches also use radar data, e.g., from Sentinel 1, to classify crops. The proposed Special Issue will leverage these promising developments to share the latest methodological development of crop type mapping, particularly object/instance-based crop identification and agricultural productivity in complex, smallholder farming regions in the world. Articles covering but not limited to recent research on the following topics are invited to this Special issues:

  • Crop type/cropland mapping methods and algorithms;
  • Mapping intercropping in smallholder farming systems;
  • Mapping tree crops or specialty crops (avocado, coffee, and cocoa);
  • Crop field size mapping/crop field boundary detection;
  • Mapping and characterization of management practices in various cropping systems (crop rotation, intensity, tillage, etc.);
  • Crop yield mapping and monitoring;
  • Multi-source data fusion (satellite, census data, surveys, and local knowledge) for agricultural applications;
  • Agricultural land use change analysis.
Dr. Steffen Fritz
Dr. Qiong Hu
Dr. Zhenong Jin
Dr. Wenbin Wu
Dr. Liangzhi You
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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 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
  • crop mapping
  • crop types
  • field size
  • farming system
  • land use change
  • spatial analysis
  • smallholder agriculture

Published Papers (15 papers)

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Research

Open AccessArticle
Mapping Ratoon Rice Planting Area in Central China Using Sentinel-2 Time Stacks and the Phenology-Based Algorithm
Remote Sens. 2020, 12(20), 3400; https://doi.org/10.3390/rs12203400 - 16 Oct 2020
Abstract
Mapping rice cropping systems is important for grain yield prediction and food security assessments. Both single- and double-season rice are the dominant rice systems in central China. However, because of increasing labor shortages and high costs, there has been a gradual decline in [...] Read more.
Mapping rice cropping systems is important for grain yield prediction and food security assessments. Both single- and double-season rice are the dominant rice systems in central China. However, because of increasing labor shortages and high costs, there has been a gradual decline in double-season rice. Ratoon rice (RR) has been proposed as an alternative system that balances the productivity, cost, and labor requirements of rice cultivation. RR has been expanding in central China, encouraged by the improved cultivars, machinery, and favorable policies. However, to our knowledge, the distribution of RR has not been mapped with remote sensing techniques. This study developed a phenology-based algorithm to map RR at a 10 m resolution in Hubei Province, Central China, using dense time stacks of Sentinel-2 images (cloud cover <80%) in 2018. The key in differentiating RR from the other rice cropping systems is through the timing of maturity. We proposed to use two contrast vegetation indices to identify RR fields. The newly-developed yellowness index (YI) calculated with the reflectance of blue, green, and red bands was used to detect the ripening phase, and the enhanced vegetation index (EVI) was used to detect the green-up of the second-season crop to eliminate the misclassification caused by stubbles left in the field. The RR map demonstrated that RR was mainly distributed in the low alluvial plains of central and southern Hubei Province. The total planting area of RR in 2018 was 2225.4 km2, accounting for 10.03% of the total area of paddy rice fields. The overall accuracy of RR, non-RR rice fields, and non-rice land cover types was 0.76. The adjusted overall accuracy for RR and non-RR was 0.91, indicating that the proposed YI and the phenology-based algorithm could accurately identify RR fields from the paddy rice fields. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform
Remote Sens. 2020, 12(18), 2992; https://doi.org/10.3390/rs12182992 - 15 Sep 2020
Abstract
The knowledge of the area and spatial distribution of paddy rice fields is important for water resource management. However, accurate map of paddy rice is a long-term challenge because of its spatiotemporal discontinuity and short duration. To solve this problem, this study proposed [...] Read more.
The knowledge of the area and spatial distribution of paddy rice fields is important for water resource management. However, accurate map of paddy rice is a long-term challenge because of its spatiotemporal discontinuity and short duration. To solve this problem, this study proposed a paddy rice area extraction approach by using the combination of optical vegetation indices and synthetic aperture radar (SAR) data. This method is designed to overcome the data-missing problem due to cloud contamination and spatiotemporal discontinuities of the traditional optical remote sensing method. More specifically, the Sentinel-1A SAR and the Sentinel-2 multispectral imager (MSI) Level-2A imagery are used to identify paddy rice with a high temporal and spatial resolution. Three vegetation indices, namely normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface water index (LSWI), are estimated from optical bands. Two polarization bands (VH (vertical-horizontal) and VV (vertical-vertical)) are used to overcome the cloud contamination problem. This approach was applied with the random forest machine learning algorithm on the Google Earth Engine platform for the Jianghan Plain in China as an experimental area. The results of 39 experiments uncovered the effect of different factors. The results indicated that the combination of VV and VH band showed a better performance compared with other polarization bands; the average producer’s accuracy of paddy rice (PA) is 72.79%, 1.58% higher than the second one VH. Secondly, the combination of three indices also showed a better result than others, with average PA 73.82%, 1.42% higher than using NDVI alone. The classification result presented the best combination is EVI, VV, and VH polarization band. The producer’s accuracy of paddy rice was 76.67%, with the overall accuracy (OA) of 66.07%, and Kappa statistics of 0.45. However, NDVI, EVI, and VH showed better performance in mapping the morphology. The results demonstrated the method developed in this study can be successfully applied to the cloud-prone area for mapping paddy rice to overcome the data missing caused by cloud and rain during the paddy growing season. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Spatiotemporal Dynamics of the Northern Limit of Winter Wheat in China Using MODIS Time Series Images
Remote Sens. 2020, 12(15), 2382; https://doi.org/10.3390/rs12152382 - 24 Jul 2020
Abstract
Studying the spatiotemporal changes of the northern limit of winter wheat (NLWW) in China is important to ensure regional food security and deal with the effects of climate change. Previous studies mainly used climate indicators to analyze the variation of the potential NLWW [...] Read more.
Studying the spatiotemporal changes of the northern limit of winter wheat (NLWW) in China is important to ensure regional food security and deal with the effects of climate change. Previous studies mainly used climate indicators to analyze the variation of the potential NLWW in different historical periods, while little attention has been paid to the actual migrations and changes of the NLWW. The objectives of the present study were three-fold: (i) to map the spatial distribution of winter wheat in northern China in 2001, 2007, 2014 and 2019; (ii) to extract the actual NLWW; and (iii) to quantitatively explore the dynamics of the NLWW. First, we adopted the “combining variations before and after estimated heading dates” method to map the winter wheat in northern China based on time series MODIS EVI2 data. Second, we used the kernel density estimation algorithm to extract the actual NLWW in four historical periods. Finally, the fishnet method was utilized to quantitatively analyze the direction and distance of the spatiotemporal changes of the NLWW. The results demonstrated that the NLWW has exhibited a marked fluctuating trend of migration southward, with a 37-km shift in latitude over the past 20 years. The elevation limit of winter wheat planting was around 1600 m; however, the centroid of winter wheat planting has shifted slowly to lower elevations. There was a gap between the actual NLWW and the potential NLWW. The reason for this gap was that the actual NLWW moved southward under the interacting effects of human activities and climate change, while the potential NLWW moved northward due to climate change. The results of this study are of great scientific value in the formulation of winter wheat planting strategies in climate-sensitive areas to respond to climate change and ensure food security. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images
Remote Sens. 2020, 12(12), 1942; https://doi.org/10.3390/rs12121942 - 16 Jun 2020
Abstract
Climate change is increasing the frequency and intensity of heavy precipitation in the US Midwest, overwhelming existing tile drainage, and resulting in temporary soil ponding across the landscape. However, lack of direct observations of the dynamics of temporal soil ponding limits our understanding [...] Read more.
Climate change is increasing the frequency and intensity of heavy precipitation in the US Midwest, overwhelming existing tile drainage, and resulting in temporary soil ponding across the landscape. However, lack of direct observations of the dynamics of temporal soil ponding limits our understanding of its impacts on crop growth and biogeochemical cycling. Satellite remote sensing offers a unique opportunity to observe and analyze this dynamic phenomenon at the landscape scale. Here we analyzed a series of red–green–blue (RGB) and near infrared (NIR) remote sensing images from the Planet Labs CubeSat constellation following a period of heavy precipitation in May 2017 to determine the spatiotemporal characteristics of ponding events in the maize–soybean cropland of Champaign County, Illinois USA. We trained Random Forest algorithms for near-daily images to create binary classifications of surface water versus none, which achieved kappa values around 0.9. We then analyzed the morphology of classification results for connected pixels across space and time and found that 2.5% (5180 ha) of this cropland was classified as water surface at some point during this period. The frequency distribution of areal ponding extent exhibited a log–log relationship; the mean and median areas of ponds were 1231 m2 and 126 m2, respectively, with 26.1% of identified ponds being at the minimum threshold area of 45 m2, and 2.5% of the ponds having an area greater than 104 m2 (1 ha). Ponds lasted for a mean duration of 2.4 ± 1.7 days, and 2.3% of ponds lasted for more than a week. Our results suggest that transient ponding may be significant at the landscape scale and ought to be considered in assessments of crop risk, soil and water conservation, biogeochemistry, and sustainability. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours
Remote Sens. 2020, 12(7), 1205; https://doi.org/10.3390/rs12071205 - 08 Apr 2020
Cited by 1
Abstract
Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation [...] Read more.
Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Agronomic Traits Analysis of Ten Winter Wheat Cultivars Clustered by UAV-Derived Vegetation Indices
Remote Sens. 2020, 12(2), 249; https://doi.org/10.3390/rs12020249 - 10 Jan 2020
Cited by 4
Abstract
Timely and accurate estimation of crop yield variability before harvest is crucial in precision farming. This study is aimed to evaluate the ability of cluster analysis based on Vegetation Indices (VIs) that were obtained from UAVs to predict the spatial variability on agronomic [...] Read more.
Timely and accurate estimation of crop yield variability before harvest is crucial in precision farming. This study is aimed to evaluate the ability of cluster analysis based on Vegetation Indices (VIs) that were obtained from UAVs to predict the spatial variability on agronomic traits of ten winter wheat cultivars. Five VIs groups were identified and the ground truth yield-related data were analyzed for clusters validation. The yield data revealed a value of 6.91 t ha−1 for the first cluster with the highest VIs value and a decrease of −12%, −21%, and −27% for the 2nd, 3rd, and 4th clusters; respectively; the 5th cluster; with the lowest VIs value showed the lower yield values (4 t ha−1). Agronomic traits, such as dry biomass, spike numbers, and weight were grouped according to VIs clusters and analyzed and showed the same trends. The analysis of spatial distribution and agronomic data of the ten cultivars within the single clusters highlighted that the most productive varieties showing a greater value of spike weight and numbers and a greater presence of areas with high values of VIs and vice versa the less productive once, though two cultivars showed productions not linked to cluster classification and high data range variability were recorded. Cluster identified by high-resolution UAV vegetation indices can be a valid strategy although its effectiveness is closely linked to the cultivar component and, therefore, requires extensive verification. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Sight for Sorghums: Comparisons of Satellite- and Ground-Based Sorghum Yield Estimates in Mali
Remote Sens. 2020, 12(1), 100; https://doi.org/10.3390/rs12010100 - 27 Dec 2019
Cited by 5
Abstract
The advent of multiple satellite systems capable of resolving smallholder agricultural plots raises possibilities for significant advances in measuring and understanding agricultural productivity in smallholder systems. However, since only imperfect yield data are typically available for model training and validation, assessing the accuracy [...] Read more.
The advent of multiple satellite systems capable of resolving smallholder agricultural plots raises possibilities for significant advances in measuring and understanding agricultural productivity in smallholder systems. However, since only imperfect yield data are typically available for model training and validation, assessing the accuracy of satellite-based estimates remains a central challenge. Leveraging a survey experiment in Mali, this study uses plot-level sorghum yield estimates, based on farmer reporting and crop cutting, to construct and evaluate estimates from three satellite-based sensors. Consistent with prior work, the analysis indicates low correlation between the ground-based yield measures (r = 0.33). Satellite greenness, as measured by the growing season peak value of the green chlorophyll vegetation index from Sentinel-2, correlates much more strongly with crop cut (r = 0.48) than with self-reported (r = 0.22) yields. Given the inevitable limitations of ground-based measures, the paper reports the results from the regressions of self-reported, crop cut, and (crop cut-calibrated) satellite sorghum yields. The regression covariates explain more than twice as much variation in calibrated satellite yields (R2 = 0.25) compared to self-reported or crop cut yields, suggesting that a satellite-based approach anchored in crop cuts can be used to track sorghum yields as well or perhaps better than traditional measures. Finally, the paper gauges the sensitivity of yield predictions to the use of Sentinel-2 versus higher-resolution imagery from Planetscope and DigitalGlobe. All three sensors exhibit similar performance, suggesting little gains from finer resolutions in this system. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data
Remote Sens. 2019, 11(24), 3012; https://doi.org/10.3390/rs11243012 - 14 Dec 2019
Cited by 4
Abstract
Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an [...] Read more.
Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data
Remote Sens. 2019, 11(21), 2517; https://doi.org/10.3390/rs11212517 - 28 Oct 2019
Cited by 2
Abstract
As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The [...] Read more.
As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment
Remote Sens. 2019, 11(21), 2480; https://doi.org/10.3390/rs11212480 - 24 Oct 2019
Cited by 5
Abstract
Methods based on Sentinel-1 data were developed to monitor crops and fields to facilitate the distribution of subsidies. The objectives were to (1) develop a methodology to predict individual crop species or or management regimes; (2) investigate the earliest time point in the [...] Read more.
Methods based on Sentinel-1 data were developed to monitor crops and fields to facilitate the distribution of subsidies. The objectives were to (1) develop a methodology to predict individual crop species or or management regimes; (2) investigate the earliest time point in the growing season when the species predictions are satisfactory; and (3) to present a method to assess the uncertainty of the predictions at an individual field level. Seventeen Sentinel-1 synthetic aperture radar (SAR) scenes (VV and VH polarizations) acquired in interferometric wide swath mode from 14 May through to 30 August 2017 in the same geometry, and selected based on the weather conditions, were used in the study. The improved k nearest neighbour estimation, ik-NN, with a genetic algorithm feature optimization was tailored for classification with optional Sentinel-1 data sets, species groupings, and thresholds for the minimum parcel area. The number of species groups varied from 7 to as large as 41. Multinomial logistic regression was tested as an optional method. The Overall Accuracies (OA) varied depending on the number of species included in the classification, and whether all or not field parcels were included. OA with nine species groups was 72% when all parcels were included, 81% when the parcels area threshold (for incorporating parcels into classification) was 0.5 ha, and around 90% when the threshold was 4 ha. The OA gradually increased when adding extra Sentinel-1 scenes up until the early August, and the initial scenes were acquired in early June or mid-May. After that, only minor improvements in the crop recognition accuracy were noted. The ik-NN method gave greater overall accuracies than the logistic regression analysis with all data combinations tested. The width of the 95% confidence intervals with ik-NN for the estimate of the probability of the species with the largest probability on an individual parcel varied depending on the species, the area threshold of the parcel and the number of the Sentinel-1 scenes used. The results ranged between 0.06–0.08 units (6–8% points) for the most common species when the Sentinel-1 scenes were between 1 June and 12 August. The results were well-received by the authorities and encourage further research to continue the study towards an operational method in which the space-borne SAR data are a part of the information chain. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Crowd-Driven and Automated Mapping of Field Boundaries in Highly Fragmented Agricultural Landscapes of Ethiopia with Very High Spatial Resolution Imagery
Remote Sens. 2019, 11(18), 2082; https://doi.org/10.3390/rs11182082 - 05 Sep 2019
Abstract
Mapping the extent and location of field boundaries is critical to food security analysis but remains problematic in the Global South where such information is needed the most. The difficulty is due primarily to fragmentation in the landscape, small farm sizes, and irregular [...] Read more.
Mapping the extent and location of field boundaries is critical to food security analysis but remains problematic in the Global South where such information is needed the most. The difficulty is due primarily to fragmentation in the landscape, small farm sizes, and irregular farm boundaries. Very high-resolution satellite imagery affords an opportunity to delineate such fields, but the challenge remains of determining such boundaries in a systematic and accurate way. In this paper, we compare a new crowd-driven manual digitization tool (Crop Land Extent) with two semi-automated methods (contour detection and multi-resolution segmentation) to determine farm boundaries from WorldView imagery in highly fragmented agricultural landscapes of Ethiopia. More than 7000 one square-kilometer image tiles were used for the analysis. The three methods were assessed using quantitative completeness and spatial correctness. Contour detection tended to under-segment when compared to manual digitization, resulting in better performance for larger (approaching 1 ha) sized fields. Multi-resolution segmentation on the other hand, tended to over-segment, resulting in better performance for small fields. Neither semi-automated method in their current realizations however are suitable for field boundary mapping in highly fragmented landscapes. Crowd-driven manual digitization is promising, but requires more oversight, quality control, and training than the current workflow could allow. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Combining Deep Learning and Prior Knowledge for Crop Mapping in Tropical Regions from Multitemporal SAR Image Sequences
Remote Sens. 2019, 11(17), 2029; https://doi.org/10.3390/rs11172029 - 29 Aug 2019
Cited by 3
Abstract
Accurate crop type identification and crop area estimation from remote sensing data in tropical regions are still considered challenging tasks. The more favorable weather conditions, in comparison to the characteristic conditions of temperate regions, permit higher flexibility in land use, planning, and management, [...] Read more.
Accurate crop type identification and crop area estimation from remote sensing data in tropical regions are still considered challenging tasks. The more favorable weather conditions, in comparison to the characteristic conditions of temperate regions, permit higher flexibility in land use, planning, and management, which implies complex crop dynamics. Moreover, the frequent cloud cover prevents the use of optical data during large periods of the year, making SAR data an attractive alternative for crop mapping in tropical regions. This paper evaluates the effectiveness of Deep Learning (DL) techniques for crop recognition from multi-date SAR images from tropical regions. Three DL strategies are investigated: autoencoders, convolutional neural networks, and fully-convolutional networks. The paper further proposes a post-classification technique to enforce prior knowledge about crop dynamics in the target area. Experiments conducted on a Sentinel-1 multitemporal sequence of a tropical region in Brazil reveal the pros and cons of the tested methods. In our experiments, the proposed crop dynamics model was able to correct up to 16.5% of classification errors and managed to improve the performance up to 3.2% and 8.7% in terms of overall accuracy and average F1-score, respectively. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features
Remote Sens. 2019, 11(15), 1780; https://doi.org/10.3390/rs11151780 - 30 Jul 2019
Cited by 5
Abstract
The separation of crop types is essential for many agricultural applications, particularly when within-season information is required. Generally, remote sensing may provide timely information with varying accuracy over the growing season, but in small structured agricultural areas, a very high spatial resolution may [...] Read more.
The separation of crop types is essential for many agricultural applications, particularly when within-season information is required. Generally, remote sensing may provide timely information with varying accuracy over the growing season, but in small structured agricultural areas, a very high spatial resolution may be needed that exceeds current satellite capabilities. This paper presents an experiment using spectral and textural features of NIR-red-green-blue (NIR-RGB) bands data sets acquired with an unmanned aerial vehicle (UAV). The study area is located in the Swiss Plateau, which has highly fragmented and small structured agricultural fields. The observations took place between May 5 and September 29, 2015 over 11 days. The analyses are based on a random forest (RF) approach, predicting crop separation metrics of all analyzed crops. Three temporal windows of observations based on accumulated growing degree days (AGDD) were identified: an early temporal window (515–1232 AGDD, 5 May–17 June 2015) with an average accuracy (AA) of 70–75%; a mid-season window (1362–2016 AGDD, 25 June–22 July 2015) with an AA of around 80%; and a late window (2626–3238 AGDD, 21 August–29 September 2015) with an AA of <65%. Therefore, crop separation is most promising in the mid-season window, and an additional NIR band increases the accuracy significantly. However, discrimination of winter crops is most effective in the early window, adding further observational requirements to the first window. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology
Remote Sens. 2019, 11(14), 1677; https://doi.org/10.3390/rs11141677 - 15 Jul 2019
Cited by 8
Abstract
Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative [...] Read more.
Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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Open AccessArticle
DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data
Remote Sens. 2019, 11(13), 1619; https://doi.org/10.3390/rs11131619 - 08 Jul 2019
Cited by 6
Abstract
Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture [...] Read more.
Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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