Radar Remote Sensing of Agricultural Canopies: A Review

Steele‐Dunne, Susan; McNairn, Heather; Monsiváis-Huertero, Alejandro; Judge, Jasmeet; Liu, Pang‐Wei; Papathanassiou, Kostas

doi:10.1109/jstars.2016.2639043

Cited by 268 publications

(183 citation statements)

References 262 publications

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“…The frequent rain events in July and August allow the soil moisture at 5 cm and 10 cm to rise continuously, reaching near saturation by 9 September. 17 Figure 6 shows the surface soil moisture measured in each crop type throughout the growing season. Warm temperatures and limited precipitation in late-May and June are reflected in the relatively dry surface soil moisture values before 30 June.…”

Section: Weather Station Datamentioning

confidence: 99%

Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands

et al. 2019

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Agriculture is of huge economic significance in The Netherlands where the provision of real-time, reliable information on crop development is essential to support the transition towards precision agriculture. Optical imagery can provide invaluable insights into crop growth and development but is severely hampered by cloud cover. This case study in the Flevopolder illustrates the potential value of Sentinel-1 for monitoring five key crops in The Netherlands, namely sugar beet, potato, maize, wheat and English rye grass. Time series of radar backscatter from the European Space Agency's Sentinel-1 Mission are analyzed and compared to ground measurements including phenological stage and height. Temporal variations in backscatter data reflect changes in water content and structure associated with phenological development. Emergence and closure dates are estimated from the backscatter time series and validated against a photo archive. Coherence data are compared to Normalized Difference Vegetation Index (NDVI) and ground data, illustrating that the sudden increase in coherence is a useful indicator of harvest. The results presented here demonstrate that Sentinel-1 data have significant potential value to monitor growth and development of key Dutch crops. Furthermore, the guaranteed availability of Sentinel-1 imagery in clouded conditions ensures the reliability of data to meet the monitoring needs of farmers, food producers and regulatory bodies.

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Section: Weather Station Datamentioning

confidence: 99%

Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands

et al. 2019

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“…If cloud denies optical recognition of these parameters then SAR must also be used. The use of radar technologies to estimate LAI is well established (see [49,107,108]). These approaches are not without issues, the radar response can saturate at high LAI [49,109] and the results can be confounded by changes in soil moisture content.…”

Section: Eo and Field Irrigationmentioning

confidence: 99%

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

et al. 2018

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Abstract:Global information on the spatio-temporal variation of parameters driving the Earth's terrestrial water and energy cycles, such as evapotranspiration (ET) rates and surface soil moisture (SSM), is of key significance. The water and energy cycles underpin global food and water security and need to be fully understood as the climate changes. In the last few decades, Earth Observation (EO) technology has played an increasingly important role in determining both ET and SSM. This paper reviews the state of the art in the use specifically of operational EO of both ET and SSM estimates. We discuss the key technical and operational considerations to derive accurate estimates of those parameters from space. The review suggests significant progress has been made in the recent years in retrieving ET and SSM operationally; yet, further work is required to optimize parameter accuracy and to improve the operational capability of services developed using EO data. Emerging applications on which ET/SSM operational products may be included in the context specifically in relation to agriculture are also highlighted; the operational use of those operational products in such applications remains to be seen.

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“…Lopez-Sanchez et al analyzed several FP observables extracted from Radarsat-2 at C-band and showed the sensitivity of these observables to the rice growth cycle [3]. Research results from Agriculture and Agri-Food Canada also showed the potential and effectiveness of SAR data applied in crops' classification, growth parameters inversion and soil moisture inversion [19]. The identification accuracy for crop types in the C-band was about 85% and in X-band about 95% [20].…”

Section: Introductionmentioning

confidence: 99%

Compact Polarimetric Response of Rape (Brassica napus L.) at C-Band: Analysis and Growth Parameters Inversion

Zhang

Chen

et al. 2017

Remote Sensing

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Growth parameters like biomass, leaf area index (LAI) and stem height play an import role for crop monitoring and yield prediction. Compact polarimetric (CP) SAR has shown great potential and similar performance to fully-polarimetric (FP) SAR in crop mapping and phenology retrieval, but its potential in growth parameters inversion has not been fully explored. In this paper, a time series of images of CP SAR was simulated from five FP SAR data gathered during the entire growth season of rape. CP response of 27 parameters, relying on Stokes parameters and their child parameters, decomposition parameters and backscattering coefficients, were extracted and investigated as a function of days after sowing (DAS) during the whole rape growth cycle to interpret their sensitivity to each growth parameter. Then, random forest (RF) was chosen as an automatic approach for the growth parameters inversion method, and its results were compared with traditional single-parameter regression models. Most of the CP parameters showed high sensitivity with growth parameters and great potential for growth parameters inversion. Among all of the regression models, the quadratic regression model showed the best performance for all of the growth parameters inversion, the best result for biomass inversion was the third component of the Stokes parameters (g 3 ) with R 2 of 0.765 and RMSE of 73.20 g/m 2 . For LAI and stem height was one of the Stokes child parameters, the circular polarization ratio (U c ), with R 2 of 0.857 and 0.923 and RMSE of 0.66 and 18.71 cm, respectively. RF showed the highest accuracy and smallest RMSE for all of three growth parameters inversion; R 2 for biomass, LAI and stem height were 0.93, 0.96 and 0.95, respectively; RMSE were 46.24 g/m 2 , 0.25 and 13.5 cm, respectively. However, there are also some CP parameters, which showed low sensitivity to growth parameters, that had high importance for RF inversion. The results confirmed the potential of CP data and the RF method in growth parameters inversion, but they also confirmed that it was difficult to give a physical interpretation for the RF inversion model.

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Radar Remote Sensing of Agricultural Canopies: A Review

Cited by 268 publications

References 262 publications

Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands

Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

Compact Polarimetric Response of Rape (Brassica napus L.) at C-Band: Analysis and Growth Parameters Inversion

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