scholarly journals Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model

2022 ◽  
Vol 14 (2) ◽  
pp. 404
Author(s):  
Yaqing Gou ◽  
Casey M. Ryan ◽  
Johannes Reiche
Keyword(s):  
Soil Moisture ◽  
Aboveground Biomass ◽  
Cloud Model ◽  
Large Area ◽  
Moisture Effects ◽  
Radar Imagery ◽  
Water Cloud ◽  
L Band ◽  
The Difference ◽  
Moisture Conditions

Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil moisture in AGBC retrieval from L-band radar imagery in central Mozambique, where woodland ecosystems dominate. Cross-validation results suggest that (1) the standard WCM effectively accounts for soil moisture effects, especially for areas with AGBC ≤ 20 tC/ha, and (2) the standard WCM significantly improved the quality of regional AGBC mosaics by reducing the stripes between adjacent paths caused by the difference in soil moisture conditions between different acquisition dates. By applying the standard WCM, the difference in mean predicted AGBC for the tested path with the largest soil moisture difference was reduced by 18.6%. The WCM is a valuable tool for AGBC mapping by reducing prediction uncertainties and striping effects in regional mosaics, especially in low-biomass areas including African woodlands and other woodland and savanna regions. It is repeatable for recent L-band data including ALOS-2 PALSAR-2, and upcoming SAOCOM and NISAR data.

scholarly journals Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands

2020 ◽  
pp. 1-20
Author(s):  
Zhen Wang ◽  
Tianjie Zhao ◽  
Jianxiu Qiu ◽  
Xuesheng Zhao ◽  
Rui Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Cloud Model ◽  
Water Cloud ◽  
L Band

scholarly journals PolSAR-Decomposition-Based Extended Water Cloud Modeling for Forest Aboveground Biomass Estimation

2019 ◽  
Vol 11 (19) ◽  
pp. 2287 ◽  
Author(s):  
Kumar ◽  
Garg ◽  
Govil ◽  
Kushwaha
Keyword(s):  
Synthetic Aperture Radar ◽  
Aboveground Biomass ◽  
Cloud Model ◽  
Synthetic Aperture ◽  
Model Parameters ◽  
Semiempirical Model ◽  
Volume Scattering ◽  
Water Cloud ◽  
Forest Aboveground Biomass ◽  
Aperture Radar

Polarimetric synthetic aperture radar (PolSAR) remote sensing has been widely used for forest mapping and monitoring. PolSAR data has the capability to provide scattering information that is contributed by different scatterers within a single SAR resolution cell. A methodology for a PolSAR-based extended water cloud model (EWCM) has been proposed and evaluated in this study. Fully polarimetric phased array type L-band synthetic aperture radar (PALSAR) data of advanced land observing satellite (ALOS) was used in this study for forest aboveground biomass (AGB) retrieval of Dudhwa National Park, India. The shift in the polarization orientation angle (POA) is a major problem that affects the PolSAR-based scattering information. The two sources of POA shift are Faraday rotation angle (FRA) and structural properties of the scatterer. Analysis was carried out to explore the effect of FRA in the SAR data. Deorientation of PolSAR data was implemented to minimize any ambiguity in the scattering retrieval of model-based decomposition. After POA compensation of the coherency matrix, a decrease in the power of volume scattering elements was observed for the forest patches. This study proposed a framework to extend the water cloud model for AGB retrieval. The proposed PolSAR-based EWCM showed less dependency on field data for model parameters retrieval. The PolSAR-based scattering was used as input model parameters to derive AGB for the forest area. Regression between PolSAR-decomposition-based volume scattering and AGB was performed. Without deorientation of the PolSAR coherency matrix, EWCM showed a modeled AGB of 92.90 t ha−1, and a 0.36 R2 was recorded through linear regression between the field-measured AGB and the modeled output. After deorientation of the PolSAR data, an increased R2 (0.78) with lower RMSE (59.77 t ha−1) was obtained from EWCM. The study proves the potential of a PolSAR-based semiempirical model for forest AGB retrieval. This study strongly recommends the POA compensation of the coherency matrix for PolSAR-scattering-based semiempirical modeling for forest AGB retrieval.

scholarly journals SOIL pH AND MOISTURE EFFECTS ON VEGETABLE AMARANTH PRODUCTION

HortScience ◽  
1992 ◽  
Vol 27 (6) ◽  
pp. 647e-647
Author(s):  
Bharat P. Singh ◽  
Wayne F. Whitehead
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Soil Ph ◽  
Water Levels ◽  
Plant Parts ◽  
Moisture Effects ◽  
Wide Range ◽  
Significant Difference ◽  
Plant Grown ◽  
Moisture Conditions

The effect of soil moisture and pH levels on the vegetative growth of amaranth were studied in the greenhouse during 1990-91. Three soil pH levels: 4.5, 5.3, and 6.4 and four soil water levels: 3, 6, 12 and 18% (w/w) comprised the treatments of the two studies. The plants grown in pH 6.4 were significantly taller and had greater leaf area than plants grown in pH 5.3 or 4.7 soil. There was a significant decrease in all above ground plant parts with each increase in soil acidity. The top fresh weight of plants grown in 5.6 and 4.7 pH soil were 27% and 73% lower, respectively, than plant grown in 6.4 pH soil. Plant grown in 3% soil water had significantly lower leaf, stem and root fresh weights than other soil water levels. There was no significant difference in the performance of plants grown in 6, 12 or 18% soil water, suggesting that amaranth plant is adapted to a wide range of soil moisture conditions.

Soil moisture estimation over cereals fields using l-band alos2 data (merguellil case – KAIROUAN)

2021 ◽  
Author(s):  
Emna Ayari ◽  
Zeineb Kassouk ◽  
Zohra Lili Chabaane ◽  
Safa Bousbih ◽  
Mehrez Zribi
Keyword(s):  
Soil Moisture ◽  
Cloud Model ◽  
Radar Data ◽  
Radar Signal ◽  
Biophysical Parameters ◽  
Vegetation Development ◽  
L Band ◽  
Cereal Fields ◽  
Bare Soils ◽  
Band Signal

<p>Soil moisture is a key component for water resources management especially for irrigation needs estimation. We analyze in the present study, the potential of L-band data, acquired by (Advanced Land Observing Satellite-2) ALOS-2, to retrieve soil moisture over bare soils and cereal fields located in semi-arid area in the Kairouan plain.</p><p>In this context, we evaluate radar signal sensitivity to roughness, soil moisture and vegetation biophysical parameters. Based on multi-incidence radar data (28°, 32.5° and 36°), high correlations characterize relationships between backscattering coefficients in dual-polarization (HH and HV) and root mean square of heights (Hrms) and Zs, parameters, Sensitivity of radar data to soil moisture was discussed for three classes of NDVI (less than 0.25 for bare soils and dispersed vegetation, between 0.25 and 0.5 for medium vegetation and greater than 0.5 for dense cereals). With vegetation development, where NDVI values are higher than 0.25, SAR signal remains sensitive to soil moisture in HH pol. This sensitivity to moisture disappears, in HV pol for dense vegetation. For covered fields, L-band signal is very sensitive to Vegetation Water Content (VWC), with R² values ranging between 0.76 and 0.61 in HH and HV polarization respectively.</p><p>Simulating signal behavior is carried out through various models over bare soils and covered cereal fields. Over bare soils, proposed empirical expressions, modified versions of Integral Equation Model (IEM-B) and Dubois models (Dubois-B) are evaluated, generally for HH and HV polarizations. Best consistency is observed between real data and IEM-B backscattering simulations in HH polarization. More discrepancies between real and modelled data are observed in HV polarization.</p><p>Furthermore, to simulate L-band signal behavior over covered fields, the inversion of Water Cloud Model (WCM) coupled to different bare soil models is realized through direct equations and Look-up tables. Two options of WCM, are tested (with and without soil-vegetation interaction scattering term). For the first option, results highlight the good performance of IEM-B coupled to WCM in HH polarization with RMSE value between estimated and in situ moisture measurements equal to 4.87 vol.%. By adding soil – cereal interaction term in the second option of WCM, results reveal a stable accuracy in HH polarization and an important improvement of soil moisture estimations in HV polarization, with RMSE values are ranging between 6 and 7 vol.%.</p>

scholarly journals Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

2018 ◽  
Vol 10 (9) ◽  
pp. 1370 ◽  
Author(s):  
Junhua Li ◽  
Shusen Wang
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Cloud Model ◽  
Data Availability ◽  
Agricultural Crop ◽  
Sar Images ◽  
Area Index ◽  
Water Cloud ◽  
The Impact

The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use.

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