Multitemporal c-band radar measurements on wheat fields

Mattia, F.; Toan, Thuy Le; Picard, Ghislain; Posa, F.; D'Alessio, Angelo Canio; Notarnicola, Claudia; Gatti, Andrea; Rinaldi, Michele; Satalino, G.; Pasquariello, G.

doi:10.1109/tgrs.2003.813531

Cited by 188 publications

(168 citation statements)

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“…Moreover, AGB is an essential parameter for synthetic aperture radar (SAR) applications and is also useful for the prediction of crop yields (Mattia et al, 2003). In 2014, dry AGB was determined once per ESU, on average 10 days before harvesting.…”

Section: Above-ground Biomass and Yieldmentioning

confidence: 99%

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Truckenbrodt

Schmullius

2018

Earth Syst. Sci. Data

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Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage ≤ 25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).

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Section: Above-ground Biomass and Yieldmentioning

confidence: 99%

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Truckenbrodt

Schmullius

2018

Earth Syst. Sci. Data

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“…As compared to optical sensors, synthetic aperture radar (SAR) sensors not only provide an increased opportunity for monitoring crops in the early season, given their acquisition capability regardless of weather and time of the day, but can also offer additional information on crop canopy structure, e.g., from polarimetry information [15]. In recent years, the advantages provided by polarimetric SAR (PolSAR) data for agricultural monitoring have been extensively studied for applications such as crop-type classification and mapping [16,17], crop phenology monitoring [18,19], productivity assessment based on the sensitivity of polarimetric parameters to indicators of crop conditions [20], and for the retrieval of soil moisture content underneath agricultural crops [21].…”

Section: Background and Rationalementioning

confidence: 99%

Temporal Polarimetric Behavior of Oilseed Rape (Brassica napus L.) at C-Band for Early Season Sowing Date Monitoring

Yang

Chen

et al. 2014

Remote Sensing

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Spatial monitoring of the sowing date plays an important role in crop yield estimation at the regional scale. The feasibility of using polarimetric synthetic aperture radar (SAR) data for early season monitoring of the sowing dates of oilseed rape (Brassica napus L.) fields is explored in this paper. Polarimetric SAR responses of six parameters, relying on polarization decomposition methods, were investigated as a function of days after sowing (DAS) during the entire growing season, by means of five consecutive Radarsat-2 images. A near-continuous temporal evolution of these parameters was observed, based on 88 oilseed rape fields. It provided a solid basis for determining the suitable temporal window and the best polarimetric parameters for sowing date monitoring. A high sensitivity of all polarimetric parameters to the DAS at different growing stages was shown. Simple linear models could be calibrated to estimate sowing dates at early growth OPEN ACCESS

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“…SAR has been used in vegetation/biomass studies at sub-arctic latitudes [21,22], but is very rarely used to study arctic vegetation [23], and has not been used at all in the context of the High Arctic. There are considerable benefits to using SAR when compared to optical data for arctic research.…”

Section: Introductionmentioning

confidence: 99%

“…These approaches, however, only apply when the vegetation is dense enough to form some sort of canopy, i.e., the above-ground phytomass must be of a sufficient height to have a noticeable effect on backscatter, which may not hold true in many parts of the arctic. Agricultural studies have demonstrated that even relatively short vegetation can produce an appreciable amount of HV backscatter due to depolarization [21,25] of the backscatter, as well as marked differences in HH and VV polarizations [22], though high arctic vegetation levels are generally even lower than short-crop agriculture. Low density grasses and sedges, which make up much of the vegetation cover in the high arctic, can also be difficult to distinguish from bare ground [26].…”

Section: Introductionmentioning

confidence: 99%

“…Regardless, polarimetric data could be key to this analysis if this depolarization holds true for very low levels of vegetation. Multiple incidence angle data may be important for similar reasons, with greater incidence angle backscatter having greater interaction (and therefore backscatter) with short vegetation than smaller incidence angle data [21,22,27] (similar to how surface roughness affects backscatter), though stopping short of full volumetric scatter.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

et al. 2014

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Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r 2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r 2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r 2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r 2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling OPEN ACCESSRemote Sens. 2014, 6 2135 above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

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Multitemporal c-band radar measurements on wheat fields

Cited by 188 publications

References 10 publications

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Temporal Polarimetric Behavior of Oilseed Rape (Brassica napus L.) at C-Band for Early Season Sowing Date Monitoring

Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

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