Evaluation of Crop Type Classification with Different High Resolution Satellite Data Sources

Crop type classification with satellite imageries is widely applied in support of crop production management and food security strategy. The abundant supply of these satellite data is accelerating and blooming the application of crop classification as satellite data at 10 m to 30 m spatial resolution have been made accessible easily, widely and free of charge, including optical sensors, the wide field of viewer (WFV) onboard the GaoFen (GF, high resolution in English) series from China, the MultiSpectral Instrument (MSI) onboard Sentinel 2 (S2) from Europe and the Operational Land Imager (OLI) onboard Landsat 8 (L8) from USA, thanks to the implementation of the open data policy. There are more options in using the satellite data as these three data sources are available. This paper explored the different capability of these three data sources for the crop type mapping in the same area and within the same growing season. The study was executed in a flat and irrigated area in Northwest China. Nine types of crop were classified using these three kinds of time series of data sources in 2017 and 2018, respectively. The same suites of the training samples and validation samples were applied for each of the data sources. Random Forest (RF) was used as the classifier for the crop type classification. The confusion error matrix with the OA, Kappa and F1-score was used to evaluate the accuracy of the classifications. The result shows that GF-1 relatively has the lowest accuracy as a consequence of the limited spectral bands, but the accuracy is at 93–94%, which is still excellent and acceptable for crop type classification. S2 achieved the highest accuracy of 96–98%, with 10 available bands for the crop type classification at either 10 m or 20 m. The accuracy of 97–98% for L8 is in the middle but the difference is small in comparison with S2. Any of these satellite data may be used for the crop type classification within the growing season, with a very good accuracy if the training datasets were well tuned.

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In-season crop classification using optical remote sensing with random forest over irrigated agricultural fields in Australia

10.5194/egusphere-egu21-4987 ◽

2021 ◽

Author(s):

Zitian Gao ◽

Danlu Guo ◽

Dongryeol Ryu ◽

Andrew Western

Keyword(s):

Random Forest ◽

Water Use ◽

Landsat 8 ◽

Irrigation Season ◽

Yield Estimation ◽

Cropping Season ◽

Crop Type ◽

Eastern Australia ◽

Crop Classification ◽

Type Classification

Timely classification of crop types is critical for agronomic planning in water use and crop production. However, crop type mapping is typically undertaken only after the cropping season, which precludes its uses in later-season water use planning and yield estimation. This study aims 1) to understand how the accuracy of crop type classification changes within cropping season and 2) to suggest the earliest time that it is possible to achieve reliable crop classification. We focused on three main summer crops (corn/maize, cotton and rice) in the Coleambally Irrigation Area (CIA), a major irrigation district in south-eastern Australia consisting of over 4000 fields, for the period of 2013 to 2019. The summer irrigation season in the CIA is from mid-August to mid-May and most farms use surface irrigation to support the growth of summer crops. We developed models that combine satellite data and farmer-reported information for in-season crop type classification. Monthly-averaged Landsat spectral bands were used as input to Random Forest algorithm. We developed multiple models trained with data initially available at the start of the cropping season, then later using all the antecedent images up to different stages within the season. We evaluated the model performance and uncertainty using a two-fold cross validation by randomly choosing training vs. validation periods. Results show that the classification accuracy increases rapidly during the first three months followed by a marginal improvement afterwards. Crops can be classified with a User&#8217;s accuracy above 70% based on the first 2-3 months after the start of the season. Cotton and rice have higher in-season accuracy than corn/maize. The resulting crop maps can be used to support activities such as later-season system scale irrigation decision-making or yield estimation at a regional scale.Keywords: Landsat 8 OLI, in-season, multi-year, crop type, Random Forest

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Modelling of growing season methane fluxes in a high-Arctic wet tundra ecosystem 1997–2010 using in situ and high-resolution satellite data

Tellus B ◽

10.3402/tellusb.v65i0.19722 ◽

2013 ◽

Vol 65 (1) ◽

pp. 19722 ◽

Cited By ~ 19

Author(s):

Torbern Tagesson ◽

Mikhail Mastepanov ◽

Meelis Mölder ◽

Mikkel P. Tamstorf ◽

Lars Eklundh ◽

...

Keyword(s):

High Resolution ◽

Satellite Data ◽

Growing Season ◽

High Arctic ◽

Tundra Ecosystem ◽

Methane Fluxes

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High-resolution satellite data reveal an increase in peak growing season gross primary production in a high-Arctic wet tundra ecosystem 1992–2008

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2012.03.016 ◽

2012 ◽

Vol 18 ◽

pp. 407-416 ◽

Cited By ~ 26

Author(s):

Torbern Tagesson ◽

Mikhail Mastepanov ◽

Mikkel P. Tamstorf ◽

Lars Eklundh ◽

Per Schubert ◽

...

Keyword(s):

High Resolution ◽

Primary Production ◽

Satellite Data ◽

Gross Primary Production ◽

Growing Season ◽

High Arctic ◽

Tundra Ecosystem

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Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-1511-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 1511-1516 ◽

Cited By ~ 4

Author(s):

I. Theologou ◽

M. Patelaki ◽

K. Karantzalos

Keyword(s):

Water Quality ◽

High Resolution ◽

Satellite Data ◽

Landsat 8 ◽

Landsat 7 ◽

Multi Temporal ◽

Water Quality Status ◽

Quality Status ◽

Empirical Algorithms

Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn’t establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

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Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image

International Journal of Image Graphics and Signal Processing ◽

10.5815/ijigsp.2014.09.02 ◽

2014 ◽

Vol 6 (9) ◽

pp. 11-19 ◽

Cited By ~ 5

Author(s):

J. Senthilnath ◽

Nitin Karnwal ◽

D. Sai Teja

Keyword(s):

High Resolution ◽

Clonal Selection ◽

Satellite Image ◽

Clonal Selection Algorithm ◽

Selection Algorithm ◽

Crop Type ◽

High Resolution Satellite Image ◽

Type Classification

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Assessment and inventory of forest parameters using medium-high resolution satellite data

10.12681/eadd/48769 ◽

2021 ◽

Author(s):

Ειρήνη Χρυσάφη

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Satellite Data ◽

Earth Observation ◽

Landsat 8 ◽

Spectral Instrument ◽

Stacked Generalization ◽

Sentinel 2

Τα μεσογειακά δάση χαρακτηρίζονται από υψηλή χωροχρονική ετερογένεια και αποτελούν ένα από σημαντικότερα σημεία της βιοποικιλότητας στον πλανήτη. Η σημαντική αξία τους και το ευρύ φάσμα των οικοσυστημικών υπηρεσιών που παρέχουν, αναγνωρίζεται ευρέως από επιστήμονες, διεθνείς συμβάσεις και οργανισμούς. Ωστόσο, η ευπάθεια τους σε ανθρώπινες και φυσικές απειλές έχει ως αποτέλεσμα την διατάραξη τους. Συνεπώς, σχέδια βιώσιμης διαχείρισης και αειφορικής ανάπτυξης καθίστανται ως επιτακτική ανάγκη. Οι πρακτικές παρακολούθησης και απογραφής δασών απαιτούν την αξιόπιστη εκτίμηση δασικών παραμέτρων, όπως η κυκλική επιφάνεια, ο αριθμός δέντρων ανά μονάδα επιφάνειας και ξυλώδες όγκου. Η ετερογένεια των μεσογειακών δασών και η δύσκολη πρόσβασής τους, καθιστά την επιστήμη της τηλεπισκόπησης ως εξαιρετικά χρήσιμο μέσο για την αξιολόγηση των δασικών πόρων. Η τεχνολογία της τηλεπισκόπησης και τα ανοιχτά δεδομένα τηλεπισκόπησης παρέχουν μεγάλες δυνατότητες στον τομέα της δασολογίας και στην δασική απογραφή. Επιπλέον, η ταχεία πρόοδος στους αλγόριθμους τεχνητής νοημοσύνης διευκολύνει την ανάλυση ευρέος φάσματος δεδομένων. Σε αυτό το πλαίσιο, ο συνδυασμός αυτών των ισχυρών εργαλείων (δεδομένα τηλεπισκόπησης και προσεγγίσεις μηχανικής μάθησης) συνιστά μια πολλά υποσχόμενη, αλλά και ερευνητική πρόκληση, για την εκτίμηση δασικών παραμέτρων. Στη παρούσα διατριβή, εξετάζονται διάφορες προσεγγίσεις για την βελτιστοποίηση της εκτίμησης δασικών παραμέτρων με την χρήση δορυφορικών εικόνων και τεχνικών μηχανικής μάθησης.Η δομή της παρούσας διατριβής αποτελείται από τρία μέρη. Το πρώτο μέρος αποτελείται από τέσσερα κεφάλαια. Αρχικά, στο Κεφάλαιο 1, γίνεται μια εισαγωγή στην αξία των μεσογειακών δασών, στις υπηρεσίες που παρέχουν και στις απειλές που αντιμετωπίζουν. Το Κεφάλαιο 2 τονίζει την ανάγκη αειφορικής διαχείρισης των δασών και κατ 'επέκταση της απογραφής και αξιόπιστης εκτίμησης δασικών παραμέτρων. Στο Κεφάλαιο 3, παρουσιάζονται εν συντομία πηγές δεδομένων τηλεπισκόπησης και η συμβολή τους σε δασικές εφαρμογές και ιδιαίτερα στην εκτίμηση δασικών παραμέτρων, σε περιοχές της Μεσογείου. Το κεφάλαιο 4, αποτελεί μια εισαγωγή στους αλγόριθμους τεχνητής νοημοσύνης και μηχανικής μάθησης και πώς αυτές οι προσεγγίσεις εφαρμόζονται στον τομέα της τηλεπισκόπησης και της δασολογίας. Τέλος παρουσιάζονται τα ερευνητικά ερωτήματα και τα αντικείμενα της παρούσας διατριβής. Το δεύτερο μέρος αποτελείται από τέσσερα άρθρα, εκ των οποίων, το πρώτο (Κεφάλαιο 7) έχει δημοσιευτεί στο περιοδικό Remote Sensing of Environment (2017) και αφορά την εκτίμηση δασικών παραμέτρων χρησιμοποιώντας δια-εποχιακές εικόνες Landsat 8 Operational Land Imager. Το δεύτερο άρθρο (Κεφάλαιο 8) έχει δημοσιευτεί στο Remote Sensing Letters (2017) και αφορά τις σχέσεις μεταξύ ξυλώδες όγκου και εικόνων Sentinel-2 Multi Spectral Instrument. Το τρίτο άρθρο (Κεφάλαιο 9) έχει δημοσιευτεί στο περιοδικό International Journal of Applied Earth Observation and Geoinformation (2019) και αφορά την αξιολόγηση των δορυφορικών δεδομένων Sentinel-2 Multi Spectral Instrument για την εκτίμηση του ξυλώδες όγκου. Το τελευταίο άρθρο (Κεφάλαιο 10) που προορίζεται προς δημοσίευση, αποτελεί μια προκαταρκτική μελέτη για την εκτίμηση του ξυλώδες όγκου σε ένα μεσογειακό δασικό οικοσύστημα, με μία μετά-μαθησιακή προσέγγιση και την ανάπτυξη ενός μοντέλου συσσωρευμένης γενίκευσης (stacked generalization). Τέλος, στο τρίτο μέρος της παρούσας διατριβής παρουσιάζονται συνοπτικά οι απαντήσεις των ερωτημάτων που τέθηκαν στην παρούσα διατριβή και τα προβλήματα - περιορισμοί που αντιμετωπίστηκαν. Επίσης, προτείνονται δυνατότητες και προοπτικές εξέλιξης της παρούσας έρευνας, που θα μπορούσε να αποτελέσουν αντικείμενο για μελλοντική έρευνα.

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Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

Remote Sensing ◽

10.3390/rs8060513 ◽

2016 ◽

Vol 8 (6) ◽

pp. 513 ◽

Cited By ~ 36

Author(s):

Manuel Aguilar ◽

Abderrahim Nemmaoui ◽

Antonio Novelli ◽

Fernando Aguilar ◽

Andrés García Lorca

Keyword(s):

Time Series ◽

High Resolution ◽

Satellite Data ◽

Landsat 8 ◽

Object Based ◽

Very High

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Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series

Remote Sensing ◽

10.3390/rs70810400 ◽

2015 ◽

Vol 7 (8) ◽

pp. 10400-10424 ◽

Cited By ~ 35

Author(s):

François Waldner ◽

Marie-Julie Lambert ◽

Wenjuan Li ◽

Marie Weiss ◽

Valérie Demarez ◽

...

Keyword(s):

Time Series ◽

High Resolution ◽

Land Cover ◽

Resolution Time ◽

Crop Type ◽

Type Classification

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Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery

Precision Agriculture ◽

10.1007/s11119-019-09698-y ◽

2019 ◽

Vol 21 (4) ◽

pp. 856-880 ◽

Cited By ~ 3

Author(s):

Holly Croft ◽

Joyce Arabian ◽

Jing M. Chen ◽

Jiali Shang ◽

Jiangui Liu

Keyword(s):

Chlorophyll Content ◽

Satellite Data ◽

Nitrogen Fertilizer ◽

Vegetation Index ◽

Growing Season ◽

Fertilizer Application ◽

Landsat 8 ◽

Leaf Chlorophyll Content ◽

Leaf Chlorophyll ◽

Normalised Difference Vegetation Index

AbstractSpatial information on crop nutrient status is central for monitoring vegetation health, plant productivity and managing nutrient optimization programs in agricultural systems. This study maps the spatial variability of leaf chlorophyll content within fields with differing quantities of nitrogen fertilizer application, using multispectral Landsat-8 OLI data (30 m). Leaf chlorophyll content and leaf area index measurements were collected at 15 wheat (Triticum aestivum) sites and 13 corn (Zea mays) sites approximately every 10 days during the growing season between May and September 2013 near Stratford, Ontario. Of the 28 sites, 9 sites were within controlled areas of zero nitrogen fertilizer application. Hyperspectral leaf reflectance measurements were also sampled using an Analytical Spectral Devices FieldSpecPro spectroradiometer (400–2500 nm). A two-step inversion process was developed to estimate leaf chlorophyll content from Landsat-8 satellite data at the sub-field scale, using linked canopy and leaf radiative transfer models. Firstly, at the leaf-level, leaf chlorophyll content was modelled using the PROSPECT model, using both hyperspectral and simulated mulitspectral Landsat-8 bands from the same leaf sample. Hyperspectral and multispectral validation results were both strong (R2 = 0.79, RMSE = 13.62 μg/cm2 and R2 = 0.81, RMSE = 9.45 μg/cm2, respectively). Secondly, leaf chlorophyll content was estimated from Landsat-8 satellite imagery for 7 dates within the growing season, using PROSPECT linked to the 4-Scale canopy model. The Landsat-8 derived estimates of leaf chlorophyll content demonstrated a strong relationship with measured leaf chlorophyll values (R2 = 0.64, RMSE = 16.18 μg/cm2), and compared favourably to correlations between leaf chlorophyll and the best performing tested spectral vegetation index (Green Normalised Difference Vegetation Index, GNDVI; R2 = 0.59). This research provides an operational basis for modelling within-field variations in leaf chlorophyll content as an indicator of plant nitrogen stress, using a physically-based modelling approach, and opens up the possibility of exploiting a wealth of multispectral satellite data and UAV-mounted multispectral imaging systems.

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SENTINEL 2 AND LANDSAT-8 BANDS SENSITIVITY ANALYSIS FOR MAPPING OF ALKALINE SOIL IN NORTHERN DRY ZONE OF KARNATAKA, INDIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-307-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 307-313 ◽

Cited By ~ 1

Author(s):

S. Meti ◽

P. D. Lakshmi ◽

M. S. Nagaraja ◽

V. Shreepad ◽

Keyword(s):

High Resolution ◽

Soil Ph ◽

Satellite Data ◽

Soil Salinity ◽

Degradation Process ◽

Landsat 8 ◽

Alkaline Soil ◽

Policy Makers ◽

Dry Zone ◽

Sentinel 2

Abstract. Soil salinization is most common land degradation process occurring in deep vertisol of northern dry zone of Karnataka, India. Accurate and high resolution spatial information on salinization can assist policy makers to better target areas for interventions to avoid aggravation of soil degradation process. Digital soil mapping using satellite data has been identified as a potential means of obtaining soil information. This paper focuses on exploring possibility of using new generation medium resolution Landsat-8 and Sentinel-2 satellite data to map alkaline soils of Ramthal irrigation project area in north Karnataka. Surface soil salinity parameters of zone 20 were correlated with reflectance values of different band and band combination and traditional salinity indices and result has indicated that SWIR bands of both satellite showed significant negative correlation with soil pH, EC (r&thinsp;=&thinsp;&minus;0.39 to &minus;0.45) whereas visible and NIR bands did not show significant relation. However rationing of SWIR bands with visible blue band has significantly improved the correlation with soil pH and EC (r&thinsp;=&thinsp;+0.60 to +0.70). Traditional salinity index based on visible bands failed to show significant correlation with soil parameters. It is interesting to note that SWIR bands alone did not show significant correlation with soil sodicity parameters like exchangeable Na, SAR, RSC but band rationing with blue bands has significantly improved the correlation (r&thinsp;=&thinsp;0.45). High resolution soil salinity map was prepared using simple linear regression model and using this map will serve as base map for the policy makers.

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