Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion

The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a new method to quantify and specify restrictions of the Sentinel-2 imagery in the context of checks by monitoring, a newly introduced control approach within the European Common Agriculture Policy framework. The method consists of a comparison of normalized difference vegetation index (NDVI) time series constructed from data of different spatial resolution to estimate the performance and limits of the coarser one. Using similarity assessment of Sentinel-2 (10 m pixel size) and PlanetScope (3 m pixel size) NDVI time series, it was estimated that for 10% out of 867 fields less than 0.5 ha in size, Sentinel-2 data did not provide reliable evidence of the activity or state of the agriculture field over a given timeframe. Statistical analysis revealed that the number of clean or full pixels and the proportion of pixels lost after an application of a 5-m (1/2 pixel) negative buffer are the geospatial parameters of the field that have the highest influence on the ability of the Sentinel-2 data to qualify the field’s state in time. We specified the following limiting criteria: at least 8 full pixels inside a border and less than 60% of pixels lost. It was concluded that compliance with the criteria still assures a high level of extracted information reliability. Our research proved the promising potential, which was higher than anticipated, of Sentinel-2 data for the continuous state assessment of small fields. The method could be applied to other sensors and indicators.

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Experimental Evaluation of Sentinel-2 Spectral Response Functions for NDVI Time-Series Continuity

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2012.2235447 ◽

2013 ◽

Vol 51 (3) ◽

pp. 1336-1348 ◽

Cited By ~ 50

Author(s):

Petra D'Odorico ◽

Alemu Gonsamo ◽

Alexander Damm ◽

Michael E. Schaepman

Keyword(s):

Time Series ◽

Experimental Evaluation ◽

Spectral Response ◽

Response Functions ◽

Ndvi Time Series ◽

Sentinel 2

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Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest

Remote Sensing ◽

10.3390/rs12213504 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3504

Author(s):

Xingrong Li ◽

Chenghai Yang ◽

Wenjiang Huang ◽

Jia Tang ◽

Yanqin Tian ◽

...

Keyword(s):

Random Forest ◽

Root Rot ◽

Vegetation Index ◽

Texture Features ◽

Spectral Model ◽

Spectral Indices ◽

Texture Model ◽

Cotton Root ◽

Cotton Fields ◽

Sentinel 2

Cotton root rot is a destructive cotton disease and significantly affects cotton quality and yield, and accurate identification of its distribution within fields is critical for cotton growers to control the disease effectively. In this study, Sentinel-2 images were used to explore the feasibility of creating classification maps and prescription maps for site-specific fungicide application. Eight cotton fields with different levels of root rot were selected and random forest (RF) was used to identify the optimal spectral indices and texture features of the Sentinel-2 images. Five optimal spectral indices (plant senescence reflectance index (PSRI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI1), moisture stressed index (MSI), and renormalized difference vegetation index (RDVI)) and seven optimal texture features (Contrast 1, Dissimilarity 1, Entory 2, Mean 1, Variance 1, Homogeneity 1, and Second moment 2) were identified. Three binary logistic regression (BLR) models, including a spectral model, a texture model, and a spectral-texture model, were constructed for cotton root rot classification and prescription map creation. The results were compared with classification maps and prescription maps based on airborne imagery. Accuracy assessment showed that the accuracies of the classification maps for the spectral, texture, and spectral-texture models were 92.95%, 84.81%, and 91.87%, respectively, and the accuracies of the prescription maps for the three respective models were 90.83%, 87.14%, and 91.40%. These results confirmed that it was feasible to identify cotton root rot and create prescription maps using different features of Sentinel-2 imagery. The addition of texture features had little effect on the overall accuracy, but it could improve the ability to identify root rot areas. The producer’s accuracy (PA) for infested cotton in the classification maps for the texture model and the spectral-texture model was 2.82% and 1.07% higher, respectively, than that of the spectral model, and the PA for treatment zones in the prescription maps for the two respective models was 8.6% and 8.22% higher than that of the spectral model. Results based on the eight cotton fields showed that the spectral model was appropriate for the cotton fields with relatively severe infestation and the spectral-texture model was more appropriate for the cotton fields with low or moderate infestation.

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A New Spatiotemporal Data Fusion Method to Reconstruct High-Quality Landsat Ndvi Time-Series Data

10.1109/igarss47720.2021.9554810 ◽

2021 ◽

Author(s):

Xiaofang Ling ◽

Ruyin Cao

Keyword(s):

Time Series ◽

Data Fusion ◽

Time Series Data ◽

Spatiotemporal Data ◽

Series Data ◽

Fusion Method ◽

High Quality ◽

Ndvi Time Series

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Automatic Derivation of Cropland Phenological Parameters by Adaptive Non-Parametric Regression of Sentinel-2 Ndvi Time Series

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8519264 ◽

2018 ◽

Cited By ~ 2

Author(s):

Yady Tatiana Solano-Correa ◽

Francesca Bovolo ◽

Lorenzo Bruzzone ◽

Diego Fernindez-Prieto

Keyword(s):

Time Series ◽

Parametric Regression ◽

Ndvi Time Series ◽

Sentinel 2 ◽

Non Parametric

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Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform

Remote Sensing ◽

10.3390/rs11212479 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2479 ◽

Cited By ~ 5

Author(s):

Huiying Li ◽

Mingming Jia ◽

Rong Zhang ◽

Yongxing Ren ◽

Xin Wen

Keyword(s):

Time Series ◽

Vegetation Index ◽

Google Earth ◽

Late Winter ◽

Mangrove Forests ◽

Mangrove Species ◽

Ndvi Time Series ◽

Google Earth Engine ◽

Species Mapping ◽

Sentinel 2

Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations.

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A new MODIS-Landsat fusion method to reconstruct Landsat NDVI time-series data

10.5194/egusphere-egu21-14869 ◽

2021 ◽

Author(s):

Xiaofang Ling ◽

Ruyin Cao

Keyword(s):

Time Series ◽

Data Fusion ◽

Vegetation Index ◽

Time Series Data ◽

Similarity Index ◽

Google Earth ◽

Series Data ◽

Fusion Method ◽

Landsat Data ◽

Ndvi Time Series

The Normalized Difference Vegetation Index (NDVI) data provided by the satellite Landsat have rich historical archive data with a spatial resolution of 30 m. However, the Landsat NDVI time-series data are quite discontinuous due to its 16-day revisit cycle, cloud contamination and some other factors. The spatiotemporal data fusion technology has been proposed to reconstruct continuous Landsat NDVI time-series data by blending the MODIS data with the Landsat data. Although a number of spatiotemporal fusion algorithms have been developed during the past decade, most of the existing algorithms usually ignore the effective use of partially cloud-contaminated images. In this study, we presented a new spatiotemporal fusion method, which employed the cloud-free pixels in the partially cloud-contaminated images to improve the performance of MODIS-Landsat data fusion by Correcting the inconsistency between MODIS and Landsat data in Spatiotemporal DAta Fusion (called CSDAF). We tested the new method at three sites covered by different vegetation types, including deciduous forests in the Shennongjia Forestry District of China (SNJ), evergreen forests in Southeast Asia (SEA), and the irrigated farmland in the Coleambally irrigated area of Australia (CIA). Two experiments were designed. In experiment I, we first simulated different cloud coverages in cloud-free Landsat images and then used both CSDAF and the recently developed IFSDAF method to restore these &#8220;missing&#8221; pixels for quantitative assessments. Results showed that CSDAF performed better than IFSDAF by achieving the smaller average Root Mean Square Error (RMSE) values (0.0767 vs. 0.1116) and the larger average Structural SIMilarity index (SSIM) values (0.8169 vs. 0.7180). In experiment II, we simulated the scenario of &#8220;inconsistence&#8221; between MODIS and Landsat by simulating different levels of noise on MODIS and Landsat data. Results showed that CSDAF was able to reduce the influence of the inconsistence between MODIS and Landsat data on MODIS-Landsat data fusion to some extent. Moreover, CSDAF is simple and can be implemented on the Google Earth Engine. We expect that CSDAF is potentially to be used to reconstruct Landsat NDVI time-series data at the regional and continental scales.

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