Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop

Dhau, Inos; Adam, Elhadi; Mutanga, Onisimo; Ayisi, Kwabena Kingsley; Abdel‐Rahman, Elfatih M.; Odindi, John; Masocha, Mhosisi

doi:10.1080/10106049.2017.1343391

Cited by 30 publications

(14 citation statements)

References 59 publications

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“…Bellemans, et al [181] also reported on a global project (Sentinel-2 for Agriculture) involving African countries, such as Burkina Faso, South Africa, Morocco and Madagascar. The specific applications of Sentinel-2 in agriculture, include crop production monitoring [77,135,182], crop type mapping [183,184], irrigation agriculture monitoring [137], nitrogen content assessment [185] and assessment of crop health [186]. These studies range from small scale monitoring (i.e., field-level) [182,183] to continental level [76].…”

Section: Sentinel-2 For Agricultural Monitoringmentioning

confidence: 99%

“…The results from these studies have high accuracies of over 85%, indicating that Sentinel-2 data has the potential for crop health monitoring. Dhau, et al [186] assessed the abilities of Sentinel-2 multispectral images to detect maize grey leaf spot disease in Durban, South Africa. The results showed that including all the 13 bands for Sentinel-2 and using the RF classifier produced a high accuracy of 83%.…”

Section: Sentinel-2 For Agricultural Monitoringmentioning

confidence: 99%

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Sentinel-2 Data for Land Cover/Use Mapping: A Review

et al. 2020

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The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

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Section: Sentinel-2 For Agricultural Monitoringmentioning

confidence: 99%

Section: Sentinel-2 For Agricultural Monitoringmentioning

confidence: 99%

Sentinel-2 Data for Land Cover/Use Mapping: A Review

et al. 2020

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“…Remote sensing has become a feasible technology for disease detection and assessment over the last several decades. Diseases that have been detected using remote sensing include rust infection [6][7][8], Fusarium head blight [9,10], and powdery mildew [9][10][11][12] in wheat, bacterial leaf blight in rice [13,14], grey leaf spot in maize [15], and late blight disease and bacterial spot in tomato [16,17]. When plants are infected with diseases, the leaf water, pigment content and internal structure undergo changes, and these biochemical and biophysical changes are also reflected in the spectral characteristics of plants [18].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

Huang

et al. 2020

Remote Sensing

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Fusarium wilt (Panama disease) of banana currently threatens banana production areas worldwide. Timely monitoring of Fusarium wilt disease is important for the disease treatment and adjustment of banana planting methods. The objective of this study was to establish a method for identifying the banana regions infested or not infested with Fusarium wilt disease using unmanned aerial vehicle (UAV)-based multispectral imagery. Two experiments were conducted in this study. In experiment 1, 120 sample plots were surveyed, of which 75% were used as modeling dataset for model fitting and the remaining were used as validation dataset 1 (VD1) for validation. In experiment 2, 35 sample plots were surveyed, which were used as validation dataset 2 (VD2) for model validation. An UAV equipped with a five band multispectral camera was used to capture the multispectral imagery. Eight vegetation indices (VIs) related to pigment absorption and plant growth changes were chosen for determining the biophysical and biochemical characteristics of the plants. The binary logistic regression (BLR) method was used to assess the spatial relationships between the VIs and the plants infested or not infested with Fusarium wilt. The results showed that the banana Fusarium wilt disease can be easily identified using the VIs including the green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), normalized difference vegetation index (NDVI), and normalized difference red-edge index (NDRE). The fitting overall accuracies of the models were greater than 80%. Among the investigated VIs, the CIRE exhibited the best performance both for the VD1 (OA = 91.7%, Kappa = 0.83) and VD2 (OA = 80.0%, Kappa = 0.59). For the same type of VI, the VIs including a red-edge band obtained a better performance than that excluding a red-edge band. A simulation of imagery with different spatial resolutions (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m resolutions) showed that good identification accuracy of Fusarium wilt was obtained when the resolution was higher than 2 m. As the resolution decreased, the identification accuracy of Fusarium wilt showed a decreasing trend. The findings indicate that UAV-based remote sensing with a red-edge band is suitable for identifying banana Fusarium wilt disease. The results of this study provide guidance for detecting the disease and crop planting adjustment.

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“…By classifying the resampled spectra using random forest algorithm, the three categories of identified disease severity of grey leaf spot can be represented. The kappa value (0.76) and overall accuracy (84%) of Sentinel-2 data analysis were the largest [18]. Liu et al have proposed a spatio-temporal anomaly detection method, which could detect heavy metal-induced stress in rice crops using multi-temporal Sentinel-2 satellite images; their proposed method successfully detected rice under Cd stress, and the coefficients of spatio-temporal variation in rice vegetation indices were stable regardless of whether they were applied to consecutive growth stages or to two different crop years [19].…”

Section: Graminearum (Gibberella Zeae)mentioning

confidence: 97%

A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery

et al. 2020

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Rapid, non-destructive detection of wheat Fusarium head blight (FHB) is an important tool for disease control. Red-edge (RE) is a prominent spectral feature for determining crop conditions with the potential to enhance the accuracy of monitoring FHB regionally. This study explored the potential of RE for FHB monitoring based on Sentinel-2 Multispectral Instrument (MSI) data. The novel red-edge head blight index (REHBI) was developed to detect FHB at a regional scale. Hyperspectral data at the canopy scale was integrated to simulate Sentinel-2 multispectral reflectance using the relative spectral response (RSR) function of the sensor. Then, many differential and ratio combinations of Sentinel-2 bands that were sensitive to FHB severity were selected. REHBI was established based on these basic vegetation indexes (VIs), and the model developed from REHBI performed best in monitoring FHB severity (R 2 = 0.82, RMSE = 10.1). Additionally, the infected canopies with disease index (DI) values between 10 and 50 were classified as slightly diseased canopies. Ordinary least square (OLS) was used to test the performance of REHBI and two conventional VIs, i.e., OSAVI and RDVI, in monitoring slightly diseased canopies; REHBI outperformed these alternatives (R 2 = 0.69, RMSE = 3.6). To approximate real agricultural conditions, Poisson noise was added to the simulated Sentinel-2 multispectral data and generalized performance of VIs was evaluated again; REHBI still had the highest R 2 and lowest RMSE values (0.74 and 12.6, respectively). Finally, to validate REHBI's ability to detect FHB infection in agricultural production, it was applied to monitoring FHB in the wheat planting areas of Changfeng and Dingyuan counties from Sentinel-2 imagery. Generally, REHBI performed better in disease monitoring than OSAVI and RDVI. The overall accuracy was up to 78.6%, and the kappa coefficient was 0.51. Experimental results demonstrate that REHBI can be used to monitor FHB. INDEX TERMS Red-edge, wheat fusarium head blight, Sentinel-2, spectral analysis, disease index.

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Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop

Cited by 30 publications

References 59 publications

Sentinel-2 Data for Land Cover/Use Mapping: A Review

Sentinel-2 Data for Land Cover/Use Mapping: A Review

Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery

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