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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| Published in: | International journal of remote sensing Vol. 44; no. 3; pp. 786 - 801 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Taylor & Francis
01-02-2023
Taylor & Francis Ltd |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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 (V1and V2), viz. VH polarized backscatter coefficient (σ0VH), Radar Vegetation Index (RVI) and depolarization ratio (χv), were used in the model. The model performed better when different parameters are used as vegetation descriptors (V1≠V2) in the WCM rather than using the same parameter for both the vegetation descriptors (V1= V2). The best results were observed when σ0VHwas considered as one of the vegetation descriptors (V1) while either χvor RVI were utilized as the other vegetation descriptor (V2) 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 0.096m3/m3. 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. |
| Author | Dave, Rucha Pandey, Dharmendra Kumar Parath, Nidhin Kushwaha, Amit Vithalpura, Manisha Saha, Koushik Murugesan, Abishek |
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| CitedBy_id | crossref_primary_10_1016_j_asr_2024_06_026 crossref_primary_10_3390_agriculture14050695 |
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| References | cit0033 Charbonneau F. (cit0009) 2005 cit0034 cit0032 cit0030 cit0039 cit0037 cit0038 cit0035 cit0036 cit0022 cit0023 cit0020 cit0021 Toan T. L. (cit0047) 1982 cit0028 cit0029 cit0026 cit0027 cit0024 cit0025 cit0011 cit0055 cit0012 cit0056 cit0053 cit0010 cit0054 cit0051 Kumar D. (cit0031) 2013 cit0052 cit0050 cit0019 cit0017 cit0018 cit0015 cit0016 cit0013 cit0057 cit0014 cit0058 cit0044 cit0001 cit0045 cit0042 cit0043 cit0040 cit0041 cit0008 cit0006 cit0007 cit0004 cit0048 cit0005 cit0049 cit0002 cit0046 cit0003 |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | Application of sentinel-1 SAR-derived vegetation descriptors for soil moisture retrieval and plant height prediction during the wheat growth cycle |
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