Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

Holtgrave, Ann-Kathrin; Röder, Norbert; Ackermann, Andrea; Erasmi, Stefan; Kleinschmit, Birgit

doi:10.3390/rs12182919

Cited by 61 publications

(36 citation statements)

References 90 publications

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“…Additionally, the performance of regression models differs remarkably between phenological stages, which was also found by previous studies for backscatter [7,13,16]. The best results are reached in the early BBCH stages from tillering to booting (21-49) because changes in the plant appearance indicated by increasing biomass, LAI, and plant height are most evident at this time.…”

Section: Discussionsupporting

confidence: 66%

“…Additionally, a smoothing of the time series could lead to better regression results, as found by Canisius et al [25]. Furthermore, the multiple regression analysis could be performed using additional parameters such as the Radar Vegetation Index (RVI) [61], Shannon entropy [35], or field statistics of SAR parameters, as was done by Holtgrave et al [16]. Additionally, it would be advantageous if existing field measurements are completed by additional parameters with the consideration of water content in different parts of the plant to catch also small water content differences, particularly during the ripening in the grains.…”

Section: Discussionmentioning

confidence: 99%

“…Time series in combination with regression analysis estimating biophysical parameters are used for this purpose [12,13]. Furthermore, the time series are often compared with vegetation indices derived from optical satellite data in different study areas in France [14], the Netherlands [15], or Germany [16] to evaluate the synergistic use of optical and SAR data. Additionally, phenological growth stages such as stem elongation and harvest are successfully extracted from Sentinel-1 time series [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

et al. 2021

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The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.

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Section: Discussionsupporting

confidence: 66%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

et al. 2021

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“…In the next step, we will test whether the higher spatial resolution SAR data gaofen-3 (the highest spatial resolution is 1 m) combined with object-oriented method can significantly improve the accuracy of crop classification in small plot area. In addition, some studies have shown that band ratio or sentinel-1 radar vegetation index can better monitor agricultural land use [63] and other studies have proved that deep learning algorithm combined with SAR data can obtain higher crop classification accuracy [64], these are the directions that need further research.…”

Section: Future Research Directionsmentioning

confidence: 99%

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Luo

Liu

et al. 2021

Remote Sensing

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The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). In this study, two study areas (Keshan farm and Tongnan town) with different average plot sizes in Heilongjiang Province, China, were selected. The research time was two consecutive years (2018 and 2019), which were used to verify the robustness of the method. Sentinel-1 images of the crop growth period (May to September) in each study area were composited with three time intervals (10 d, 15 d and 30 d). Then, the composite images were segmented by simple noniterative clustering (SNIC) according to different sizes and finally, the training samples and processed images were input into a random forest classifier for crop classification. The results showed the following: 1) the overall accuracy of using the object-oriented classification method combined composite Sentinel-1 image represented a great improvement compared with the pixel-based classification method in areas with large average plots (increase by 10%), the applicable scope of the method depends on the plot size of the study area; 2) the shorter time interval of the composite Sentinel-1 image was, the higher the crop classification accuracy was; 3) the features with high importance of composite Sentinel-1 images with different time intervals were mainly distributed in July, August and September, which was mainly due to the large differences in crop growth in these months; and 4) the optimal segmentation size of crop classification was closely related to image resolution and plot size. Previous studies usually emphasize the advantages of object-oriented classification. Our research not only emphasizes the advantages of object-oriented classification but also analyzes the constraints of using object-oriented classification, which is very important for the follow-up research of crop classification using object-oriented and synthetic aperture radar (SAR).

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“…The backscatter of VH polarization can partially improve the detection of low vegetation or crop [49], while the VV polarization can be more sensitive to soil variations [50], although these considerations should be further investigated for buildings and infrastructures.…”

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confidence: 99%

Land Consumption Monitoring with SAR Data and Multispectral Indices

et al. 2021

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Land consumption is the increase in artificial land cover, which is a major issue for environmental sustainability. In Italy, the Italian Institute for Environmental Protection and Research (ISPRA) and National System for Environmental Protection (SNPA) have the institutional duty to monitor land consumption yearly, through the photointerpretation of high-resolution images. This study intends to develop a methodology in order to produce maps of land consumption, by the use of the semi-automatic classification of multitemporal images, to reduce the effort of photointerpretation in detecting real changes. The developed methodology uses vegetation indices calculated over time series of images and decision rules. Three variants of the methodology were applied to detect the changes that occurred in Italy between the years 2018 and 2019, and the results were validated using ISPRA official data. The results show that the produced maps include large commission errors, but thanks to the developed methodology, the area to be photointerpreted was reduced to 7,300 km2 (2.4 % of Italian surface). The third variant of the methodology provided the highest detection of changes: 70.4% of the changes larger than 100 m2 (the pixel size) and over 84.0% of changes above 500 m2. Omissions are mainly related to single pixel changes, while larger changes are detected by at least one pixel in most of the cases. In conclusion, the developed methodology can improve the detection of land consumption, focusing photointerpretation work over selected areas detected automatically.

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Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

Cited by 61 publications

References 90 publications

Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Land Consumption Monitoring with SAR Data and Multispectral Indices

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