Integrated method for rice cultivation monitoring using Sentinel-2 data and Leaf Area Index

Ali, Abdelraouf M.; Savin, I. Yu.; Poddubskiy, Anton; Abouelghar, Mohamed Amein; Saleh, N. S.; Abutaleb, Khaled; El-Shirbeny, Mohammed A.; Dokukin, Peter

doi:10.1016/j.ejrs.2020.06.007

Cited by 24 publications

(21 citation statements)

References 24 publications

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“…These outputs are consistent with [46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63]. Meanwhile, for the soils that were classified by low suitability for the rice crop, located at north of the study area, where land degradation factors are active such as salinity, alkalinity and high water table, among others, this was reflected by the NDVI values during the crop growth season, which is consistent with [63,64,[79][80][81]. Moreover, the results suggest that the soils that characterized by high suitability tend to have high rice yields.…”

Section: The Suitability Of the Soil Is Reflects In The Productivity Of The Cropsupporting

confidence: 85%

Modeling Land Suitability for Rice Crop Using Remote Sensing and Soil Quality Indicators: The Case Study of the Nile Delta

et al. 2020

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Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

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Section: The Suitability Of the Soil Is Reflects In The Productivity Of The Cropsupporting

confidence: 85%

Modeling Land Suitability for Rice Crop Using Remote Sensing and Soil Quality Indicators: The Case Study of the Nile Delta

et al. 2020

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“…Cloud occlusion reduces the temporal availability and, in turn, the usability of optical satellite data, imposing a significant obstacle to applications where frequent sampling of the ground surface is necessary. In the field of precision agriculture, for tasks such as monitoring crop growth [1], crop classification [2], or crop yield prediction [3], gaps in data result in challenges related to the development of accurate models, variability in prediction performance, and the need for the use of data imputation. Open data sources, such as those generated by the EU Copernicus Sentinel missions [4], have been instrumental to the accelerated development of applications, enabling easy and wide access to large sets of high-resolution data at relatively short revisit periods.…”

Section: Introductionmentioning

confidence: 99%

Deep Internal Learning for Inpainting of Cloud-Affected Regions in Satellite Imagery

et al. 2022

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Cloud cover remains a significant limitation to a broad range of applications relying on optical remote sensing imagery, including crop identification/yield prediction, climate monitoring, and land cover classification. A common approach to cloud removal treats the problem as an inpainting task and imputes optical data in the cloud-affected regions employing either mosaicing historical data or making use of sensing modalities not impacted by cloud obstructions, such as SAR. Recently, deep learning approaches have been explored in these applications; however, the majority of reported solutions rely on external learning practices, i.e., models trained on fixed datasets. Although these models perform well within the context of a particular dataset, a significant risk of spatial and temporal overfitting exists when applied in different locations or at different times. Here, cloud removal was implemented within an internal learning regime through an inpainting technique based on the deep image prior. The approach was evaluated on both a synthetic dataset with an exact ground truth, as well as real samples. The ability to inpaint the cloud-affected regions for varying weather conditions across a whole year with no prior training was demonstrated, and the performance of the approach was characterised.

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“…Fusion of Landsat and MODIS datasets were processed by using the Spatial-temporal adaptive reflectance fusion model (STARFM) [11,16]. STARFM was used for simulation between multitemporal MODIS images (250 m spatial and 8-day temporal resolutions) and a Landsat image (30 m resolution) to create new datasets at 30 m spatial and 8-day resolutions directly for NDVI images using equation (2).…”

Section: Landsat-modis Fusionmentioning

confidence: 99%

Assessing the applicability of Fusion Landsat-MODIS data for mapping agricultural land use - A case study in An Giang Province

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et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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This study aimed to evaluate the applicability of using time-series data of spatiotemporal fusion Landsat-MODIS imagery for mapping agricultural land use in An Giang province, Vietnam. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was adopted for fusion techniques to integrate the relatively high spatial resolution of Landsat (30 meters) and frequently revisit time of MODIS (MOD09Q1, 8-days). The Maximum Likelihood Classifier (MLC) was then used to classify the land cover categories based on variations of NDVI (Normalized Difference Vegetation Index) time-series over the observation period. The overall accuracy is about 84.9%, and a kappa coefficient of K=0.7, which revealed the effectiveness of using Fusion Landsat-MODIS NDVI data in land cover classification at the provincial scale. The current of the agricultural land use was finally mapped, including seven categories, namely built-up areas (10.49%), double rice crops (4.8%), triple rice crops (68.24%), perennial tree/orchards (4.08%), annual crops (7%), water surfaces (3.07%), and forest (2.32%). The results indicate that the agricultural land use cover can be detected in detail using Fusion Landsat-MODIS imagery. The classification is dramatically higher compared to the map classified by a conventional method of solely Landsat 8 image analysis (overall accuracy of 67.3% and Kappa coefficient K=0.35). The research outcomes will support the detailed information for managers in evaluating the impact of climate change on the rice cropping system toward sustainable agriculture development.

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Integrated method for rice cultivation monitoring using Sentinel-2 data and Leaf Area Index

Cited by 24 publications

References 24 publications

Modeling Land Suitability for Rice Crop Using Remote Sensing and Soil Quality Indicators: The Case Study of the Nile Delta

Modeling Land Suitability for Rice Crop Using Remote Sensing and Soil Quality Indicators: The Case Study of the Nile Delta

Deep Internal Learning for Inpainting of Cloud-Affected Regions in Satellite Imagery

Assessing the applicability of Fusion Landsat-MODIS data for mapping agricultural land use - A case study in An Giang Province

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