The SARSense Campaign: Air- and Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Parameters in Agriculture

Mengen, David; Montzka, Carsten; Jagdhuber, Thomas; Fluhrer, Anke; Brogi, Cosimo; Baum, Sascha; Schüttemeyer, Dirk; Bayat, Bagher; Bogena, Heye; Coccia, Alex; Masalias, Gerard; Trinkel, Verena; Jakobi, Jannis; Jonard, François; Ma, Yueling; Mattia, F.; Palmisano, Davide; Rascher, Uwe; Satalino, G.; Schumacher, Maike; Koyama, Christian N.; Schmidt, Marius; Vereecken, Harry

doi:10.3390/rs13040825

Cited by 19 publications

(11 citation statements)

References 62 publications

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“…Synthetic aperture radar (SAR) can penetrate clouds and rain, compensating for the deficiency of optical images affected by clouds and rain in the second quarter. Therefore, it can be adopted to monitor the dynamic changes in cropland [56,57]. In future work, we will consider optical images in conjunction with SAR images to identify RCFs.…”

Section: Accuracy Improvementsmentioning

confidence: 99%

A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China

Wang

Yang

et al. 2023

Remote Sensing

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Rice–crayfish field (i.e., RCF) distribution mapping is crucial for the adjustment of the local crop cultivation structure and agricultural development. The single-temporal images of two phenological periods in the year were classified separately, and then the areas where the water disappeared were identified as RCFs in previous studies. However, due to the differences in the segmentation of lakes and rivers between the two images, the incorrect extraction of RCFs is unavoidable. To solve this problem, a bi-temporal-feature-difference-coupling object-based (BTFDOB) algorithm was proposed in order to map RCFs in Sihong County. We mapped RCFs by segmenting the bi-temporal images simultaneously based on the object-based method and selecting appropriate feature differences as the classification features. To evaluate the applicability, the classification results of the previous two years obtained using the single-temporal- and object-based (STOB) method were compared with the results of the BTFDOB method. The results suggested that spectral feature differences showed high feature importance, which could effectively distinguish the RCFs from non-RCFs. Our method worked well, with an overall accuracy (OA) of 96.77%. Compared with the STOB method, OA was improved by up to 2.18% across three years of data. The RCFs were concentrated in the low-lying eastern and southern regions, and the cultivation scale was expanded in Sihong. These findings indicate that the BTFDOB method can accurately identify RCFs, providing scientific support for the dynamic monitoring and rational management of the pattern.

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Section: Accuracy Improvementsmentioning

confidence: 99%

A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China

Wang

Yang

et al. 2023

Remote Sensing

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“…For further information, we refer to the European Drought Observatory reports as well as the monthly drought monitoring from Helmholtz Centre for Environmental Research [35,36]. The Rur catchment also includes the Selhausen test site, which is due to the very good availability of in-situ data used as a validation site for several air-and space-borne microwave soil moisture products and listed as a super-site by the Committee on Earth Observation Satellites Land Product Validation Subgroup (CEOS LPV) [37][38][39][40][41][42]. The second study area is located at the Segezia experimental farm (41 • 22 16 N 15 • 29 30 E) with an area of approximately 4 km 2 , located in the Cervaro basin (Northwest part of the Puglia region, Southern Italy) (Figure 2).…”

Section: Study Areamentioning

confidence: 99%

“…Indicating a decreasing sensitivity of the backscattering signal to changes in soil moisture with growing vegetation, this relationship can be described by an empirical function [77][78][79]. As the vegetation influence on the backscattering ratios can vary quite significantly, depending on the crop type and the vegetation parameters (e.g., plant height, plant geometry/structure, biomass, vegetation water content) [42,80], we choose a linear regression on pixel-basis to remove this trend by:…”

Section: Vegetation Correctionmentioning

confidence: 99%

“…While being able to improve soil moisture estimation under changing vegetation conditions in general, the retrieval of soil surface information using C-band is physically limited. In this regard, depending on the crop type and the vegetation density, for some periods during the growing cycle, C-band can become completely insensitive to soil moisture [42]. Even though the resampling to a coarser resolution of 200 m averages out the influence of vegetation, there might be pixels, where no correlation between a change in backscatter and a change in soil moisture is present.…”

Section: Effect Of Individual Processing Steps On Soil Moisture Estim...mentioning

confidence: 99%

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High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series

et al. 2023

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The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m3/m3.

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“…In contrast, synthetic aperture radar (SAR) data is independent of cloud cover and turned out to be sensitive to crop parameters like biomass, plant height and leaf area index (LAI) as well. Strong correlations between SAR data and biophysical parameters of agricultural crops has been shown by several studies mostly for C-Band [16][17][18][19][20][21], but also for other SAR wavelengths like X-Band [22,23] or L-Band [24][25][26]. In addition, many studies investigated the sensitivity of time series of backscatter and polarimetric decomposition parameters to phenological changes like heading or harvest [27][28][29].…”

Section: Introductionmentioning

confidence: 98%

Detecting Phenological Development of Winter Wheat and Winter Barley Using Time Series of Sentinel-1 and Sentinel-2

et al. 2021

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Monitoring the phenological development of agricultural plants is of high importance for farmers to adapt their management strategies and estimate yields. The aim of this study is to analyze the sensitivity of remote sensing features to phenological development of winter wheat and winter barley and to test their transferability in two test sites in Northeast Germany and in two years. Local minima, local maxima and breakpoints of smoothed time series of synthetic aperture radar (SAR) data of the Sentinel-1 VH (vertical-horizontal) and VV (vertical-vertical) intensities and their ratio VH/VV; of the polarimetric features entropy, anisotropy and alpha derived from polarimetric decomposition; as well as of the vegetation index NDVI (Normalized Difference Vegetation Index) calculated using optical data of Sentinel-2 are compared with entry dates of phenological stages. The beginning of stem elongation produces a breakpoint in the time series of most parameters for wheat and barley. Furthermore, the beginning of heading could be detected by all parameters, whereas particularly a local minimum of VH and VV backscatter is observed less then 5 days before the entry date. The medium milk stage can not be detected reliably, whereas the hard dough stage of barley takes place approximately 6–8 days around a local maximum of VH backscatter in 2018. Harvest is detected for barley using the fourth breakpoint of most parameters. The study shows that backscatter and polarimetric parameters as well as the NDVI are sensitive to specific phenological developments. The transferability of the approach is demonstrated, whereas differences between test sites and years are mainly caused by meteorological differences.

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The SARSense Campaign: Air- and Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Parameters in Agriculture

Cited by 19 publications

References 62 publications

A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China

A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China

High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series

Detecting Phenological Development of Winter Wheat and Winter Barley Using Time Series of Sentinel-1 and Sentinel-2

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