Wheat Yield Estimation Using Remote Sensing Indices Derived from Sentinel-2 Time Series and Google Earth Engine in a Highly Fragmented and Heterogeneous Agricultural Region

Imanni, Hajar Saad El; Harti, Abderrazak El; Iysaouy, Lahcen El

doi:10.3390/agronomy12112853

Cited by 18 publications

(9 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, machine learning algorithms have appeared as more accurate alternatives, particularly for large dimensional and complex data [20] (Table 1). The Random Forest (RF) algorithm has been used in several studies related to crop classification, which has demonstrated good performance [21]. Additionally, the RF algorithm has produced reliable results in numerous investigations that predicted soil properties using regression models [22].…”

Section: Introductionmentioning

confidence: 99%

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

et al. 2022

Self Cite

View full text Add to dashboard Cite

Accurate and rapid crop type mapping is critical for agricultural sustainability. The growing trend of cloud-based geospatial platforms provides rapid processing tools and cloud storage for remote sensing data. In particular, a variety of remote sensing applications have made use of publicly accessible data from the Sentinel missions of the European Space Agency (ESA). However, few studies have employed these data to evaluate the effectiveness of Sentinel-1, and Sentinel-2 spectral bands and Machine Learning (ML) techniques in challenging highly heterogeneous and fragmented agricultural landscapes using the Google Earth Engine (GEE) cloud computing platform. This work aims to map, accurately and early, the crop types in a highly heterogeneous and fragmented agricultural region of the Tadla Irrigated Perimeter (TIP) as a case study using the high spatiotemporal resolution of Sentinel-1, Sentinel-2, and a Random Forest (RF) classifier implemented on GEE. More specifically, five experiments were performed to assess the optical band reflectance values, vegetation indices, and SAR backscattering coefficients on the accuracy of crop classification. Besides, two scenarios were used to assess the monthly temporal windows on classification accuracy. The findings of this study show that the fusion of Sentinel-1 and Sentinel-2 data can accurately produce the early crop mapping of the studied area with an Overall Accuracy (OA) reaching 95.02%. The scenarios prove that the monthly time series perform better in terms of classification accuracy than single monthly windows images. Red-edge and shortwave infrared bands can improve the accuracy of crop classification by 1.72% when compared to only using traditional bands (i.e., visible and near-infrared bands). The inclusion of two common vegetation indices (The Normalized Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI)) and Sentinel-1 backscattering coefficients to the crop classification enhanced the overall classification accuracy by 0.02% and 2.94%, respectively, compared to using the Sentinel-2 reflectance bands alone. The monthly windows analysis indicated that the improvement in the accuracy of crop classification is the greatest when the March images are accessible, with an OA higher than 80%.

show abstract

Section: Introductionmentioning

confidence: 99%

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Remote sensing data is data that is often used to estimate rice yields, such as Landsat (Siyal et al, 2015), Sentinel-2 (Franch et al, 2021), and UAV (Duan et al, 2019;Bascon et al, 2022) owing to the close correlation between the rice yield and the spectral information from satellite imagery (Franch et al, 2021). The best estimation model of Wheat yield using Sentinel-2 time series and Multiple Linear Regression (MLR) techniques is the Green Normalized Difference Vegetation Index (GNDVI) in the tillering (R 2 =0.9) and maturity stages (R 2 =0.71) (Imanni et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Application of RGB UAV images to identify spectral patterns and estimate rice production

Munibah,

Iskandar,

Barus

et al. 2024

J. Agron. Indonesia (Indonesian J. Agron.)

View full text Add to dashboard Cite

Monitoring rice plant growth is crucial for evaluating rice field management and yield production. RGB images are generated from Unmanned Aerial Vehicles (UAV) with RGB cameras. UAVs produce high spatial and temporal resolution, while RGB cameras are commonly used and cheap. The objectives of this study were to identify the spectral pattern of rice plant growth and to estimate yield production based on the spectral value of RGB images. The spectral pattern and yield estimation were analyzed using confidence interval (CI) and regression, respectively. Results show that spectral pattern during the vegetative until ripening stage forms a concave with minimum value in the generative stage and decreases towards the harvest stage. Based on the CI value, the high interval between upper and lower happened in the vegetative and ripening stages while the low interval happened in the generative stage. The high CI in the vegetative and ripening stages was due to the soil background and complexity of the rice plant canopy, respectively while the low CI in the generative stage was due to the homogeneous response of the leaf canopy. The best rice yield estimation based on the spectral value occurs in the ripening stage with an R2 of 0.84. Keywords: chlorophyll content, confidence interval, drone images, rice plant, regression

show abstract

“…Cereals are vital for national food security worldwide, and information on early crop production is essential for planning emergency response and food aid initiatives [20]. Estimating production requires considering both area and yield.…”

Section: Introductionmentioning

confidence: 99%

“…Various sensors with a range of spatial and temporal resolutions have been used worldwide to estimate yield. Saad Ei Imanni et al [20] indicated several other studies that have successfully used vegetation indices derived from remote sensing data such as Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), and Weighted Difference Vegetation Index (WDVI) for crop yield monitoring and forecasting. In that study, a temporal series of six remote sensing indices and Multiple Linear Regression (MLR) methods were used for the real-time estimation of winter wheat yield using the Google Earth Engine (GEE) platform [20].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead University Agriculture Research Station, Thunder Bay, Canada

Fuentes,

Heenkenda,

Sahota

et al. 2024

Crops

View full text Add to dashboard Cite

Remote sensing technology currently facilitates the monitoring of crop development, enabling detailed analysis and monitoring throughout the crop’s growing stages. This research analyzed the winter wheat growth dynamics of experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada using high spatial and temporal resolution remote sensing images. The spectral signatures for five growing stages were prepared. NIR reflectance increased during the growing stages and decreased at the senescence, indicating healthy vegetation. The space–time cube provided valuable insight into how canopy height changed over time. The effect of nitrogen treatments on wheat did not directly influence the plant count (spring/autumn), and height and volume at maturity. However, the green and dry weights were different at several treatments. Winter wheat yield was predicted using the XGBoost algorithm, and moderate results were obtained. The study explored different techniques for analyzing winter wheat growth dynamics and identified their usefulness in smart agriculture.

show abstract

Wheat Yield Estimation Using Remote Sensing Indices Derived from Sentinel-2 Time Series and Google Earth Engine in a Highly Fragmented and Heterogeneous Agricultural Region

Cited by 18 publications

References 25 publications

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

Application of RGB UAV images to identify spectral patterns and estimate rice production

Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead University Agriculture Research Station, Thunder Bay, Canada

Contact Info

Product

Resources

About