A New Model for Surface Soil Moisture Retrieval From CBERS-02B Satellite Imagery

Abstract

This paper develops a new model for surface soil moisture (SSM) retrieval from CBERS-02B images. The paper first analyzes the existing SSM retrieval model from Landsat TM imagery and establishes the spectral radiance relationship of each band between Landsat TM and CBERS-02B. The model associated parameters including mean reflectance, mean atmospheric transmittance, and mean sun radial brightness of each band between Landsat TM and CBERS-02B is established. The model is finally adjusted by considering the differences of response frequency and sensitivity in the two satellite sensors. Two test areas, Jili Village of Laibin county, Guangxi Province, China and Yuanjiaduan Village of Jiujiang County, JiangXi Province, China are chosen to verify the correctness of the developed model. The SSMs retrieved from Landsat TM imagery are chosen as references. The accuracy of the proposed model is evaluated through correlation coefficient and root-mean-square error (RMSE) relative to the SSMs retrieved from Landsat TM images. The verified results discover that the relative accuracy of the average SSMs retrieved by the proposed model from CBERS-02B can reach over 91.0% when compared to the SSMs retrieved from Lansat TM. In addition, six types of lands are used to further evaluate the accuracy of the proposed model. The experimental results in two areas show that the correlation coefficient and the RMSE between two SSMs from CBERS-02B and Landsat TM achieves over 0.9 and 0.011 (m 3 /m 3 ), respectively, in both rocky desertification land and dry land; achieve over 0.81 and 0.09 (m 3 /m 3 ), respectively, in rice field, shrub land, and woodland. These results demonstrate that the model developed in this paper can effectively calculate the SSMs for CBERS-02B satellite imagery.Index Terms-Algorithms, CBERS-02B satellite, retrieval, surface soil moisture (SSM).

Soil Water Content Estimation With the Presence of Vegetation Using Ultra Wideband Radar-Drone

“…However, the high measurement accuracy of Gravimetric methods then becomes a consideration as ground truth. Remote sensing methods such as satellite imagery [8][9][10] and SAR [11][12][13][14] can support collecting the soil water content data from a large area. However, to increase detection accuracy in the presence of vegetation effects, it is necessary to develop a fairly complex processing technique.…”

“…5(b), the value of Z v (−d) is determined by the intrinsic impedance of the vegetation layer (η v ) and the reflection coefficient at the boundary of the vegetation layer Γ v (−d). The relation is written in (8). After Z v (−d) is obtained from Z v (−d), the Γ v (−d) can be calculated using (9).…”

“…The wave propagation phenomenon is generally used as a basic concept in the remote sensing method. These remote sensing methods include satellite imaging [8][9][10], Synthetic Aperture Radar (SAR) [11][12][13][14], radiometry [15], Time domain reflectometry (TDR) [16][17][18][19] and Ground Penetrating Radar (GPR) [20][21][22][23][24][25][26]. The measurement result from a contact or invasive sensor such as a TDR is limited to a certain point in representing the soil water content.…”

Color Night Light Remote Sensing Images Generation Using Dual-Transformation

“…(5) The root-mean-square error (RMSE) indicates the amount of spatial detail feature information, and the smaller the value, the better the fusion effect [ 43 ]. It can be expressed as follows: where denotes the fused image and denotes the source image.…”

“…(6) The correlation coefficient ( CC) indicates the correlation between the fused and source images [ 43 ]; the closer the value of the correlation coefficient to 1, the greater the correlation. It can be expressed as follows: …”