Application of sentinel-1 SAR-derived vegetation descriptors for soil moisture retrieval and plant height prediction during the wheat growth cycle

Application of sentinel-1 SAR-derived vegetation descriptors for soil moisture retrieval and plant height prediction during the wheat growth cycle

Soil moisture is the crucialparameter impacting the plant growth during its phenological cycle. The Water Cloud Model (WCM) is one of the most widely used semi-empirical models for retrieval of soil moisture of vegetated lands from synthetic aperture radar (SAR) images. The model considers the effec...

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Bibliographic Details
Published in:International journal of remote sensing Vol. 44; no. 3; pp. 786 - 801
Main Authors: Dave, Rucha, Saha, Koushik, Kushwaha, Amit, Pandey, Dharmendra Kumar, Vithalpura, Manisha, Parath, Nidhin, Murugesan, Abishek
Format: Journal Article
Language:English
Published: London Taylor & Francis 01-02-2023
Taylor & Francis Ltd
Subjects:
Backscatter
Backscattering
Correlation coefficient
Correlation coefficients
Depolarization
Empirical models
Height
Mathematical models
Modelling
Moisture effects
Parameters
Plant growth
Plant height
Plants
Radar
Radar backscatter
Radar imaging
Retrieval
Root-mean-square errors
SAR (radar)
Soil
Soil moisture
Synthetic aperture radar
Vegetation
Vegetation descriptors
Vegetation index
Water Cloud Model, Sentinel-1
Wheat
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Summary:Soil moisture is the crucialparameter impacting the plant growth during its phenological cycle. The Water Cloud Model (WCM) is one of the most widely used semi-empirical models for retrieval of soil moisture of vegetated lands from synthetic aperture radar (SAR) images. The model considers the effect of soil as well as vegetation on the radar backscatter. The study assesses the applicability of Sentinel-1 SAR-derived vegetation descriptors in the WCM for soil moisture retrieval during the wheat growth cycle. Various combinations of vegetation descriptors (V 1 and V 2 ), viz. VH polarized backscatter coefficient (σ 0 VH ), Radar Vegetation Index (RVI) and depolarization ratio (χ v ), were used in the model. The model performed better when different parameters are used as vegetation descriptors (V 1 ≠V 2 ) in the WCM rather than using the same parameter for both the vegetation descriptors (V 1 = V 2 ). The best results were observed when σ 0 VH was considered as one of the vegetation descriptors (V 1 ) while either χ v or RVI were utilized as the other vegetation descriptor (V 2 ) giving a Pearson correlation coefficient (R) of 0.959 and 0.958 and a root mean square error (RMSE) of 0.499 dB and 0.516 dB respectively. The validation of the model-retrieved soil moisture against the in-situ measured values gave an R value of 0.72 and a RMSE of 0.096m 3 /m 3 . The plant height was also predicted by the WCM in which the retrieved soil moisture from SAR data was used as a parameter. The predicted plant height was compared to in-situ measured plant height and an R value of 0.76 and RMSE of 0.214 was obtained as the best result. The study demonstrates the capability of SAR-derived parameters as vegetation descriptors in the WCM for soil moisture retrieval.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2023.2170193