Assessing in-season crop classification performance using satellite data: a test case in Northern Italy

Azar, Ramin; Villa, Paolo; Stroppiana, Daniela; Crema, Alberto; Boschetti, Mirco; Brivio, Pietro Alessandro

doi:10.5721/eujrs20164920

Cited by 58 publications

(35 citation statements)

References 36 publications

(55 reference statements)

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“…Multitemporal SIs have already demonstrated their efficacy in capturing cropland characteristics [54,65]. For our approach, three SIs were derived as proxies of crop conditions from optical data: Enhanced Vegetation Index (EVI, Equation (1)), Normalized Difference Flood Index (NDFI, Equation (2)), and Red Green Ratio Index (RGRI, Equation (3)).…”

Section: Satellite Data Pre-processingmentioning

confidence: 99%

“…The main objective is to define a classification tree approach for producing a crop map early in the summer season, i.e., around mid-July [54], to support agricultural management in Northern Italy. Spectral features for the winter and summer crop seasons (named synoptic seasonal features) are extracted from the temporal profiles of a set of proxies derived from optical and SAR data.…”

Section: Introductionmentioning

confidence: 99%

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In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

et al. 2015

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Abstract:The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features.

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Section: Satellite Data Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

et al. 2015

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“…Crop type separation is a crucial requirement for the planning [1], short-term monitoring [2], management [3], high-throughput phenotyping [4][5][6], and climate change modeling [7] of agricultural areas. Many of these tasks need up-to-date information, in particular before the end of the growing season.…”

Section: Introductionmentioning

confidence: 99%

Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features

2019

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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 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.

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“…Considerable researches have been conducted on early-season crop type mapping (Azar et al, 2016; Vaudour, Noirot-Cosson & Membrive, 2015; Villa et al, 2015), and there are two vital factors that contribute to early crop identification: the crop calendar and the remote sensing imagery characteristics. Previous early crop identification studies have shown that if crops are separable, high temporal frequency data such as Moderate Resolution Imaging Spectroradiometer (MODIS) can identify crops with a short image time series (Hao et al, 2015; Zhou, Zhang & Townley-Smith, 2013).…”

Section: Introductionmentioning

confidence: 99%

Estimation of different data compositions for early-season crop type classification

Hao

Niu

et al. 2018

PeerJ

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Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

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Assessing in-season crop classification performance using satellite data: a test case in Northern Italy

Cited by 58 publications

References 36 publications

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features

Estimation of different data compositions for early-season crop type classification

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