Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning

Ramadhani, Fadhlullah; Pullanagari, Reddy; Keresztúri, Gábor; Procter, Jonathan

doi:10.1080/01431161.2020.1779378

Cited by 24 publications

(18 citation statements)

References 70 publications

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“…The results of this study show the integration of PROBA-V and Sentinel-1 can be one alternative to deliver rice growth stage maps in the near-real-time with high accuracy of each rice growth stage models with cloud-free data, compared with a previous study [57]. Figure 15 shows the fluctuation of the composition of the integration of two sensors.…”

Section: Discussionmentioning

confidence: 74%

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Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics

et al. 2021

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Monitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to produce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production.

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Section: Discussionmentioning

confidence: 74%

“…Additionally, Griffiths, et al [56] suggested that SVM have high accuracy when used with a small dataset. Moreover, the previous study shows that SVM has better performance than the neural network and random forest classifier [57]. It could be used to create the automatization of rice growth stages map [39].…”

Section: Building Classification Modelsmentioning

confidence: 99%

Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics

et al. 2021

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“…In the UAV-based data, water hyacinth was predicted in agricultural fields (all areas) and trees (areas 1 and 3). A potential explanation for this occurrence can be spectral similarity, as most agricultural fields in the Lower Mondego river basin are used for planting rice [39,40], which also contains a certain water presence level [41]. The same effects were also visible in the classification of the Sentinel-2 MSI data, with differences in the field located in the western part of study area 1, which was not misclassified, and in the fields of the third area, which seemed to have more misclassified pixels.…”

Section: Discussionmentioning

confidence: 99%

Water Hyacinth (Eichhornia crassipes) Detection Using Coarse and High Resolution Multispectral Data

Pádua

Antão-Geraldes

Sousa

et al. 2022

Drones

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Efficient detection and monitoring procedures of invasive plant species are required. It is of crucial importance to deal with such plants in aquatic ecosystems, since they can affect biodiversity and, ultimately, ecosystem function and services. In this study, it is intended to detect water hyacinth (Eichhornia crassipes) using multispectral data with different spatial resolutions. For this purpose, high-resolution data (<0.1 m) acquired from an unmanned aerial vehicle (UAV) and coarse-resolution data (10 m) from Sentinel-2 MSI were used. Three areas with a high incidence of water hyacinth located in the Lower Mondego region (Portugal) were surveyed. Different classifiers were used to perform a pixel-based detection of this invasive species in both datasets. From the different classifiers used, the results were achieved by the random forest classifiers stand-out (overall accuracy (OA): 0.94). On the other hand, support vector machine performed worst (OA: 0.87), followed by Gaussian naive Bayes (OA: 0.88), k-nearest neighbours (OA: 0.90), and artificial neural networks (OA: 0.91). The higher spatial resolution from UAV-based data enabled us to detect small amounts of water hyacinth, which could not be detected in Sentinel-2 data. However, and despite the coarser resolution, satellite data analysis enabled us to identify water hyacinth coverage, compared well with a UAV-based survey. Combining both datasets and even considering the different resolutions, it was possible to observe the temporal and spatial evolution of water hyacinth. This approach proved to be an effective way to assess the effects of the mitigation/control measures taken in the study areas. Thus, this approach can be applied to detect invasive species in aquatic environments and to monitor their changes over time.

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“…Partitioning the VIs' signal in mixed pixels is a crucial step for improving the estimation of ecosystem processes by using freely available optical satellite imageries [18,27,28]. Although the spatial resolution of most remote sensing platforms could be adequate for crop monitoring growth and production at full canopy cover [29][30][31], it does not allow the VI contribution to be disentangled between woody and grass layers in olive groves, thus resulting in being less effective in monitoring the actual olive tree growth processes during the season.…”

Section: Discussionmentioning

confidence: 99%

Use of Sentinel-2 Derived Vegetation Indices for Estimating fPAR in Olive Groves

et al. 2022

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Olive tree cultivation is currently a dominant agriculture activity in the Mediterranean basin, where the increasing impact of climate change coupled with the inefficient management of olive groves is negatively affecting olive oil production and quality in some marginal areas. In this context, satellite imagery may help to monitor crop growth under different environmental conditions, thus providing useful information for optimizing olive grove management and final production. However, the spatial resolution of freely-available satellite products is not yet adequate to estimate plant biophysical parameters in complex agroecosystems such as olive groves, where both olive trees and grass cover contribute to the vegetation indices (VIs) signal at pixel scale. The aim of this study is therefore to test a disentangling procedure to partition the VIs signal among the different components of the agroecosystem to use this information for the monitoring of olive growth processes during the season. Specifically, five VIs (GEMI, MCARI2, NDVI, OSAVI, MCARI2/OSAVI) as recorded by Sentinel-2 at a spatial resolution of 10 m over five olive groves in the Montalbano area (Tuscany, Central Italy), were tested as a proxy for olive tree intercepted radiation. The olive tree volume per pixel was initially used to linearly rescale the VIs signal into the relevant value for the grass cover and olive trees. The models, describing the relationship between rescaled VIs and observed fraction of Photosynthetically Active Radiation (fPAR), were fitted and then validated against independent datasets. While in the calibration phase, a greater robustness at predicting fPAR was obtained using NDVI (r = 0.96 and RRMSE = 9.86), the validation results demonstrating that GEMI and MCARI2/OSAVI provided the highest performances (GEMI: r = 0.89 and RRMSE = 21.71; MCARI2/OSAVI: r = 0.87 and RRMSE = 25.50), in contrast to MCARI2 that provided the lowest (r = 0.67 and RRMSE = 36.78). These results may be related to the VIs’ intrinsic features (e.g., lower sensitivity to atmosphere and background effects), which make some of these indices, compared to others, less sensitive to saturation effects by improving fPAR estimation (e.g., GEMI vs. NDVI). On this basis, this study evidenced the need to improve the current methodologies to reduce inter-row effects and select appropriate VIs for fPAR estimation, especially in complex agroecosystems where inter-row grass growth may affect remote sensed-derived VIs signal at an inadequate pixel resolution.

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Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning

Cited by 24 publications

References 70 publications

Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics

Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics

Water Hyacinth (Eichhornia crassipes) Detection Using Coarse and High Resolution Multispectral Data

Use of Sentinel-2 Derived Vegetation Indices for Estimating fPAR in Olive Groves

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