The Performance of Random Forest Classification Based on Phenological Metrics Derived from Sentinel-2 and Landsat 8 to Map Crop Cover in an Irrigated Semi-arid Region

Htitiou, Abdelaziz; Boudhar, Abdelghani; Lebrini, Youssef; Hadria, Rachid; Lionboui, Hayat; Elmansouri, Loubna; Tychon, Bernard; Benabdelouahab, Tarik

doi:10.1007/s41976-019-00023-9

Cited by 67 publications

(29 citation statements)

References 73 publications

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“…However, significant differences in crop growth status can be observed for each density of the NDVI time series. It is clear that combining S2 and L8 data made it possible to monitor perfectly and continuously the crop development during the season and help further to capture accurate temporal changes while preserving fine-spatial-resolution details, which was previously not achievable by the only use of L8 or S2 data as observed in Figure 5 and consistent with previous studies (Htitiou et al, 2019). This becomes clearer when a sudden change (wheat harvest, or alfalfa cut) in the temporal NDVI profile takes place since it cannot be estimated properly using only the Landsat or sentinel-2 time series.…”

Section: Phenological Analysissupporting

confidence: 86%

Deep Learning-Based Reconstruction of Spatiotemporally Fused Satellite Images for Smart Agriculture Applications in a Heterogeneous Agricultural Region

Htitiou

Boudhar

Lebrini

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Remote sensing offers spatially explicit and temporally continuous observational data of various land surface parameters such as vegetation index, land surface temperature, soil moisture, leaf area index, and evapotranspiration, which can be widely leveraged for various applications at different scales and contexts. One of the main applications is agricultural monitoring, where a smart system based on precision agriculture requires a set of satellite images with a high resolution, both in time and space to capture the phenological stages and fine spatial details, especially in landscapes with various spatial heterogeneity and temporal variation. These requirements sometimes cannot be provided by a single sensor due to the trade-off required between spatial and temporal resolutions and/or the influence of cloud cover. The data availability of new generation multispectral sensors of Landsat-8 (L8) and Sentinel-2 (S2) satellites offers unprecedented options for such applications. Given this, the current study aims to display how the synergistic use of these optical sensors can efficiently support such an application. Herein, this study proposes a deep learning spatiotemporal data fusion method to fill the need for predicting a dense time series of vegetation index with fine spatial resolution. The results show that the developed method creates more accurate fused NDVI time-series data that were able to derive phenological stages and characteristics in single-crop fields, while keeps more spatial details in such a heterogeneous landscape.

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Section: Phenological Analysissupporting

confidence: 86%

Deep Learning-Based Reconstruction of Spatiotemporally Fused Satellite Images for Smart Agriculture Applications in a Heterogeneous Agricultural Region

Htitiou

Boudhar

Lebrini

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It uses random resampling technology and node random splitting technology to construct multiple decision trees, and the final classification result is obtained through voting. Compared with traditional classification algorithms such as Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM), the RF algorithm has a faster training speed and a higher degree of intelligence, is not easy to overfit and has high classification accuracy, and is widely used in crop classification and area statistics [ 60 , 61 ].…”

Section: Methodsmentioning

confidence: 99%

Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data

Kang

Meng

Liu

et al. 2021

Sensors

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A red edge band is a sensitive spectral band of crops, which helps to improve the accuracy of crop classification. In view of the characteristics of GF-6 WFV data with multiple red edge bands, this paper took Hengshui City, Hebei Province, China, as the study area to carry out red edge feature analysis and crop classification, and analyzed the influence of different red edge features on crop classification. On the basis of GF-6 WFV red edge band spectral analysis, different red edge feature extraction and red edge indices feature importance evaluation, 12 classification schemes were designed based on GF-6 WFV of four bands (only including red, green, blue and near-infrared bands), stepwise discriminant analysis (SDA) and random forest (RF) method were used for feature selection and importance evaluation, and RF classification algorithm was used for crop classification. The results show the following: (1) The red edge 750 band of GF-6 WFV data contains more information content than the red edge 710 band. Compared with the red edge 750 band, the red edge 710 band is more conducive to improving the separability between different crops, which can improve the classification accuracy; (2) According to the classification results of different red edge indices, compared with the SDA method, the RF method is more accurate in the feature importance evaluation; (3) Red edge spectral features, red edge texture features and red edge indices can improve the accuracy of crop classification in different degrees, and the red edge features based on red edge 710 band can improve the accuracy of crop classification more effectively. This study improves the accuracy of remote sensing classification of crops, and can provide reference for the application of GF-6 WFV data and its red edge bands in agricultural remote sensing.

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“…to be selected when debugging the model, and the selection of the hyper-parameters is often complicated, and it usually requires experienced engineers to be able to produce better prediction results. RF, on the other hand, as an ensemble learning algorithm based on decision trees, is widely used not only to solve classification and regression problems, [23][24][25][26][27] but also to handle anomaly detection and clustering problems. [28][29][30][31][32] Relative to artificial neural network modeling, RF modeling is much simpler and only requires to adjust the number of learners, the depth of the tree and the maximum number of features to be able to adjust the better prediction results.…”

mentioning

confidence: 99%

An improved random forest algorithm and its application to wind pressure prediction

Lang

Tiancai

et al. 2021

Int J Intell Syst

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When making regression predictions, the traditional random forest (RF) algorithm can only make predictions within the training set, which can easily lead to overfitting when modeling data have some specific noise. To solve the problem of over‐fitting, an improved RF method is proposed in this paper for wind pressure prediction. With the aim to verify the prediction performance of the improved RF algorithm, this paper predicts the wind pressure coefficients of a high‐rise building model without wind pressure measurement points. The results show that the improved RF can achieve good results in predicting the mean and fluctuating wind pressure coefficients of high‐rise buildings, and its relative error for each measurement point is basically controlled at 5%, which is acceptable in engineering terms. Further applications show that this improved RF can be used for wind pressure distribution prediction in other large‐span building type wind tunnel tests.

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The Performance of Random Forest Classification Based on Phenological Metrics Derived from Sentinel-2 and Landsat 8 to Map Crop Cover in an Irrigated Semi-arid Region

Cited by 67 publications

References 73 publications

Deep Learning-Based Reconstruction of Spatiotemporally Fused Satellite Images for Smart Agriculture Applications in a Heterogeneous Agricultural Region

Deep Learning-Based Reconstruction of Spatiotemporally Fused Satellite Images for Smart Agriculture Applications in a Heterogeneous Agricultural Region

Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data

An improved random forest algorithm and its application to wind pressure prediction

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