Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

Qadir, Abdul; Mondal, Pinki

doi:10.3390/rs12030522

Cited by 28 publications

(10 citation statements)

References 70 publications

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“…case of the TIP region. In India, Qadir and Mondal [42] found that the combination of SAR and optical data improves the monsoon cropland detection with an OA = 93%.…”

Section: Discussionmentioning

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

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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%.

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“…case of the TIP region. In India, Qadir and Mondal [42] found that the combination of SAR and optical data improves the monsoon cropland detection with an OA = 93%.…”

Section: Discussionmentioning

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

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“…Visual interpretation of reference imagery was based on elements that help to identify land cover features such as location, size, shape, tone, color, shadow, texture, and pattern. A similar procedure was used by [74,75].…”

Section: Training and Validation Samples Collectionmentioning

confidence: 99%

Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

2020

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Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Serviços Distritais de Actividades Económicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.

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“…Acquiring multiple cloudless S-2 images over a large area is difficult, so many researchers perform analyzes on combinations of a different number of images. In such approaches it is efficient to use cloud computing using ML methods, e.g., Google Earth Engine (some accuracy values reported by the authors: RF = 93.3% [12], SV Mmodi f ied = 98.07% [13], RF = 93% [14]).…”

Section: Introductionmentioning

confidence: 99%

Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images

et al. 2021

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The study presents the analysis of the possible use of limited number of the Sentinel-2 and Sentinel-1 to check if crop declarations that the EU farmers submit to receive subsidies are true. The declarations used in the research were randomly divided into two independent sets (training and test). Based on the training set, supervised classification of both single images and their combinations was performed using random forest algorithm in SNAP (ESA) and our own Python scripts. A comparative accuracy analysis was performed on the basis of two forms of confusion matrix (full confusion matrix commonly used in remote sensing and binary confusion matrix used in machine learning) and various accuracy metrics (overall accuracy, accuracy, specificity, sensitivity, etc.). The highest overall accuracy (81%) was obtained in the simultaneous classification of multitemporal images (three Sentinel-2 and one Sentinel-1). An unexpectedly high accuracy (79%) was achieved in the classification of one Sentinel-2 image at the end of May 2018. Noteworthy is the fact that the accuracy of the random forest method trained on the entire training set is equal 80% while using the sampling method ca. 50%. Based on the analysis of various accuracy metrics, it can be concluded that the metrics used in machine learning, for example: specificity and accuracy, are always higher then the overall accuracy. These metrics should be used with caution, because unlike the overall accuracy, to calculate these metrics, not only true positives but also false positives are used as positive results, giving the impression of higher accuracy. Correct calculation of overall accuracy values is essential for comparative analyzes. Reporting the mean accuracy value for the classes as overall accuracy gives a false impression of high accuracy. In our case, the difference was 10–16% for the validation data, and 25–45% for the test data.

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Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

Cited by 28 publications

References 70 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

Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images

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