Soil moisture estimation based on the microwave scattering mechanism during different crop phenological periods in a winter wheat-producing region

Wu, Shangrong; Ren, Jianqiang; Chen, Zhongxin; Yang, Peng; Li, He

doi:10.1016/j.jhydrol.2020.125521

Cited by 15 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The characteristics of crop microwave radiation and the interactions between crops and radar microwaves are complex. With the rapid development of radar remote sensing technology, which can now obtain large amounts of data, determining how to extract target crop features from radar remote sensing data has become a key issue in the microwave remote sensing of crops [16], [17], [18]. Polarimetric decomposition technology can effectively obtain the radar polarization characteristics and accurately extract the land cover distribution [19], [20], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

The Application of Compact Polarization Decomposition in the Construction of a Dual-Polarization Radar Index and the Effect Evaluation of Rape Extraction

Liang

Zhao

Tan

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

The rape planting plots are fragmented and have the same temporal variation characteristics as other land types, the rape planting area is cloudy and rainy, and the optical remote sensing images are limited, all make it difficult to extract the rape planting areas. Aiming at the above-mentioned problems, this study used microwave remote sensing data combined with the compact polarimetric technique to construct a dual-polarization radar index to classify rape planting areas and analyzed the influence of time series combinations on the extraction accuracy of rape in South China. First, based on m-χ compact polarimetric decomposition, a dual-polarization radar vegetation index (RVI) was constructed. Then, based on Sentinel-1 data, the dynamic time warping (DTW) threshold classification method was used to extract rape planting areas in six counties in the main rape-producing areas in the middle and lower reaches of the Yangtze River, China. Finally, a random forest algorithm was used to analyze and screen the optimal time series combination of the extracted regional rape planting areas. Among the 94 rape samples obtained in the field, 74 samples were accurately classified as rape, and the overall accuracy was 78.72%. Six ground samples were used to verify the accuracy of the rape planting area extraction results, and the F-1 score was 81.00%. The above-mentioned results indicated that the rape planting area extraction approach based on the RVI m-χ and DTW threshold classification methods yields high accuracy in regional rape planting

show abstract

Section: Introductionmentioning

confidence: 99%

The Application of Compact Polarization Decomposition in the Construction of a Dual-Polarization Radar Index and the Effect Evaluation of Rape Extraction

Liang

Zhao

Tan

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lately, more and more studies using microwave time series data to estimate the soil moisture of agricultural areas-especially for the crop type winter wheat-have been conducted [19][20][21]. Often, only a single satellite orbit constellation and, therefore, data from one satellite with the same acquisition geometry are used [19,[22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-1 Backscatter Analysis and Radiative Transfer Modeling of Dense Winter Wheat Time Series

et al. 2021

View full text Add to dashboard Cite

This study evaluates a temporally dense VV-polarized Sentinel-1 C-band backscatter time series (revisit time of 1.5 days) for wheat fields near Munich (Germany). A dense time series consisting of images from different orbits (varying acquisition) is analyzed, and Radiative Transfer (RT)-based model combinations are adapted and evaluated with the use of radar backscatter. The model shortcomings are related to scattering mechanism changes throughout the growth period with the use of polarimetric decomposition. Furthermore, changes in the RT modeled backscatter results with spatial aggregation from the pixel to field scales are quantified and related to the sensitivity of the RT models, and their soil moisture output are quantified and related to changes in backscatter. Therefore, various (sub)sets of the dense Sentinel-1 time series are analyzed to relate and quantify the impact of the abovementioned points on the modeling results. The results indicate that the incidence angle is the main driver for backscatter differences between consecutive acquisitions with various recording scenarios. The influence of changing azimuth angles was found to be negligible. Further analyses of polarimetric entropy and scattering alpha angle using a dual polarimetric eigen-based decomposition show that scattering mechanisms change over time. The patterns analyzed in the entropy-alpha space indicate that scattering mechanism changes are mainly driven by the incidence angle and not by the azimuth angle. Besides the analysis of differences within the Sentinel-1 data, we analyze the capability of RT model approaches to capture the observed Sentinel-1 backscatter changes due to various acquisition geometries. For this, the surface models “Oh92” or “IEM_B” (Baghdadi’s version of the Integral Equation Method) are coupled with the canopy model “SSRT” (Single Scattering Radiative Transfer). To resolve the shortcomings of the RT model setup in handling varying incidence angles and therefore the backscatter changes observed between consecutive time steps of a dense winter wheat time series, an empirical calibration parameter (coef) influencing the transmissivity (T) is introduced. The results show that shortcomings of simplified RT model architectures caused by handling time series consisting of images with varied incidence angles can be at least partially compensated by including a calibration coefficient to parameterize the modeled transmissivity for the varying incidence angle scenarios individually.

show abstract

Analysis and Survey of Soil Moisture Prediction Techniques for Agricultural Applications

Patil

Ankayarkanni

2022

Information Systems and Management Science

View full text Add to dashboard Cite

Soil moisture estimation based on the microwave scattering mechanism during different crop phenological periods in a winter wheat-producing region

Cited by 15 publications

References 45 publications

The Application of Compact Polarization Decomposition in the Construction of a Dual-Polarization Radar Index and the Effect Evaluation of Rape Extraction

The Application of Compact Polarization Decomposition in the Construction of a Dual-Polarization Radar Index and the Effect Evaluation of Rape Extraction

Sentinel-1 Backscatter Analysis and Radiative Transfer Modeling of Dense Winter Wheat Time Series

Analysis and Survey of Soil Moisture Prediction Techniques for Agricultural Applications

Contact Info

Product

Resources

About