Name
Title | ||
|---|---|---|
Linking An Agro-Meteorological Model And A Water Cloud Model For Estimating Soil Water Content Over Wheat Fields |
Abstract | ||
|---|---|---|
Agricultural drought has great impact on crop yields. Soil water content can directly reflect the degree of agricultural drought. The Water Cloud Model (WCM) is a vegetation contribution model which has been widely used in soil water content retrieval over vegetated areas. However, when optical remote sensing images are absent or are of a poor quality due to cloud cover and haze, the WCM parameters, describing the characteristics of vegetation layer, are hard to obtain. In order to effectively retrieve the soil water content over wheat fields, an agro-meteorological model, Simple Algorithm For Yield estimate (SAFY) model, was introduced to simulate the daily values of the WCM parameters during the whole winter wheat growth stages. Firstly, the leaf area index (LAI), retrieved from two Sentinel-2 (high-resolution optical) satellite images, was used to calibrate the parameters of the SAFY model in the Guanzhong Plain, PR China. The calibrated SAFY model was applied to simulate daily LAI. Then, Sentinel-1 backscatter coefficient, average ground measured soil water content from a depth of 0-12 cm and the simulated LAI were used to calibrate the WCM parameters to retrieve soil water content. In a final step, the soil water content retrieval results were validated at rain-fed and irrigated sampling sites, respectively. The SAFY model simulates the LAI of the Sentinel-1 sensing dates and uses it as an accurate vegetation description parameter for the WCM, which effectively solves the problem that the vegetation description parameter in the WCM cannot accurately describe the surface vegetation state at the time of radar satellite passing territory due to the Sentinel-1 and Sentinel-2 data in the study area cannot be acquired on the same day, and thus improving the accuracy of the WCM soil water content retrieval. For all sampling sites, the soil water content retrieval results have a satisfactory overall accuracy, and the soil water content retrieval results from rain-fed sampling sites have better accuracy than those from irrigated sampling sites. The soil water content retrieval results at the harvesting stage of winter wheat have the best accuracy among the three field experiments (R-2 = 0.66; RMSE = 2.24%), followed by the tillering stage (R-2 = 0.56; RMSE = 2.56%) and jointing stage (R-2 = 0.36; RMSE = 2.74%). These results indicated that the combination of SAFY model and WCM can effectively retrieve the soil water content at different growth stages of winter wheat in the absence of optical remotely sensed images, and can provide significant benefit for soil water content retrieval over agricultural fields at the regional scale. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.compag.2020.105833 | COMPUTERS AND ELECTRONICS IN AGRICULTURE |
Keywords | DocType | Volume |
Sentinel-1, Sentinel-2, Simple Algorithm For Yield estimate model (SAFY), Water cloud model (WCM), Soil water content estimation | Journal | 179 |
ISSN | Citations | PageRank |
0168-1699 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Han | 1 | 0 | 0.34 |
| Peng Xin Wang | 2 | 14 | 6.53 |
| Kevin Tansey | 3 | 48 | 8.78 |
| Xijia Zhou | 4 | 0 | 0.68 |
| Shuyu Zhang | 5 | 14 | 4.74 |
| Huiren Tian | 6 | 0 | 0.68 |
| Jingqi Zhang | 7 | 0 | 0.34 |
| Hongmei Li | 8 | 0 | 1.35 |