scholarly journals Estimation of Winter Wheat Residue Coverage Using Optical and SAR Remote Sensing Images

2019 ◽  
Vol 11 (10) ◽  
pp. 1163
Author(s):  
Wenting Cai ◽  
Shuhe Zhao ◽  
Yamei Wang ◽  
Fanchen Peng ◽  
Joon Heo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Crop Residues ◽  
Regional Scale ◽  
Microwave Remote Sensing ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Optical Remote Sensing ◽  
Optimal Subset ◽  
Subset Regression

As an important part of the farmland ecosystem, crop residues provide a barrier against water erosion, and improve soil quality. Timely and accurate estimation of crop residue coverage (CRC) on a regional scale is essential for understanding the condition of ecosystems and the interactions with the surrounding environment. Satellite remote sensing is an effective way of regional CRC estimation. Both optical remote sensing and microwave remote sensing are common means of CRC estimation. However, CRC estimation based on optical imagery has the shortcomings of signal saturation in high coverage areas and susceptibility to weather conditions, while CRC estimation using microwave imagery is easily influenced by soil moisture and crop types. Synergistic use of optical and microwave remote sensing information may have the potential to improve estimation accuracy. Therefore, the objectives of this study were to: (i) Analyze the correlation between field measured CRC and satellite derived variables based on Sentinel-1 and Sentinel-2, (ii) investigate the relationship of CRC with new indices (OCRI-RPs) which combine optical crop residues indices (OCRIs) and radar parameters (RPs), and (iii) to estimate CRC in Yucheng County based on OCRI-RPs by optimal subset regression. The correlations between field measured CRC and satellite derived variables were evaluated by coefficient of determination (R2) and root mean square error (RMSE). The results showed that the normalized difference tillage index (NDTI) and radar indices 2 (RI2) had relatively higher correlations with field measured CRC in OCRIs and RPs (R2 = 0.570, RMSE = 6.560% and R2 = 0.430, RMSE = 7.052%, respectively). Combining OCRIs with RPs by multiplying each OCRI with each RP could significantly improve the ability of indices to estimate CRC, as NDTI × RI2 had the highest R2 value of 0.738 and lowest RMSE value of 5.140%. The optimal model for CRC estimation by optimal subset regression was constructed by NDI71 × σ V V 0 and NDTI × σ V H 0 , with a R2 value of 0.770 and a RMSE value of 4.846%, which had a great improvement when compared with the best results in OCRIs and RPs. The results demonstrated that the combination of optical remote sensing information and microwave remote sensing information could improve the accuracy of CRC estimation.

scholarly journals Mapping Paddy Rice with Satellite Remote Sensing: A Review

2021 ◽  
Vol 13 (2) ◽  
pp. 503
Author(s):  
Rongkun Zhao ◽  
Yuechen Li ◽  
Mingguo Ma
Keyword(s):  
Remote Sensing ◽  
Data Integration ◽  
Large Scale ◽  
Microwave Remote Sensing ◽  
Paddy Rice ◽  
Data Sources ◽  
Accurate Estimation ◽  
Global Changes ◽  
Optical Remote Sensing ◽  
Advantages And Disadvantages

Paddy rice is a staple food of three billion people in the world. Timely and accurate estimation of the paddy rice planting area and paddy rice yield can provide valuable information for the government, planners and decision makers to formulate policies. This article reviews the existing paddy rice mapping methods presented in the literature since 2010, classifies these methods, and analyzes and summarizes the basic principles, advantages and disadvantages of these methods. According to the data sources used, the methods are divided into three categories: (I) Optical mapping methods based on remote sensing; (II) Mapping methods based on microwave remote sensing; and (III) Mapping methods based on the integration of optical and microwave remote sensing. We found that the optical remote sensing data sources are mainly MODIS, Landsat, and Sentinel-2, and the emergence of Sentinel-1 data has promoted research on radar mapping methods for paddy rice. Multisource data integration further enhances the accuracy of paddy rice mapping. The best methods are phenology algorithms, paddy rice mapping combined with machine learning, and multisource data integration. Innovative methods include the time series similarity method, threshold method combined with mathematical models, and object-oriented image classification. With the development of computer technology and the establishment of cloud computing platforms, opportunities are provided for obtaining large-scale high-resolution rice maps. Multisource data integration, paddy rice mapping under different planting systems and the connection with global changes are the focus of future development priorities.

scholarly journals Northwest Territories and Nunavut Snow Characteristics from a Subarctic Traverse: Implications for Passive Microwave Remote Sensing

2009 ◽  
Vol 10 (2) ◽  
pp. 448-463 ◽  
Author(s):  
Chris Derksen ◽  
Arvids Silis ◽  
Matthew Sturm ◽  
Jon Holmgren ◽  
Glen E. Liston ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Northwest Territories ◽  
Snow Water Equivalent ◽  
Regional Scale ◽  
Winter Season ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
General Nature ◽  
Lake Ice ◽  
Longitudinal Gradients

Abstract During April 2007, a coordinated series of snow measurements was made across the Northwest Territories and Nunavut, Canada, during a snowmobile traverse from Fairbanks, Alaska, to Baker Lake, Nunavut. The purpose of the measurements was to document the general nature of the snowpack across this region for the evaluation of satellite- and model-derived estimates of snow water equivalent (SWE). Although detailed, local snow measurements have been made as part of ongoing studies at tundra field sites (e.g., Daring Lake and Trail Valley Creek in the Northwest Territories; Toolik Lake and the Kuparak River basin in Alaska), systematic measurements at the regional scale have not been previously collected across this region of northern Canada. The snow cover consisted of depth hoar and wind slab with small and ephemeral fractions of new, recent, and icy snow. The snow was shallow (<40 cm deep), usually with fewer than six layers. Where snow was deposited on lake and river ice, it was shallower, denser, and more metamorphosed than where it was deposited on tundra. Although highly variable locally, no longitudinal gradients in snow distribution, magnitude, or structure were detected. This regional homogeneity allowed us to identify that the observed spatial variability in passive microwave brightness temperatures was related to subgrid fractional lake cover. Correlation analysis between lake fraction and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature showed frequency dependent, seasonally evolving relationships consistent with lake ice drivers. Simulations of lake ice thickness and snow depth on lake ice produced from the Canadian Lake Ice Model (CLIMo) indicated that at low frequencies (6.9, 10.7 GHz), correlations with lake fraction were consistent through the winter season, whereas at higher frequencies (18.7, 36.5 GHz), the strength and direction of the correlations evolved consistently with the penetration depth as the influence of the subice water was replaced by emissions from the ice and snowpack. A regional rain-on-snow event created a surface ice lens that was detectable using the AMSR-E 36.5-GHz polarization gradient due to a strong response at the horizontal polarization. The appropriate polarization for remote sensing of the tundra snowpack depends on the application: horizontal measurements are suitable for ice lens detection; vertically polarized measurements are appropriate for deriving SWE estimates.

Regional-scale optical remote sensing by the SkyMed/Cosmo system

1999 ◽  
Author(s):  
Alessandro Barducci ◽  
Roberto Bonsignori ◽  
Peter Coppo ◽  
Ivan Pippi
Keyword(s):  
Remote Sensing ◽  
Regional Scale ◽  
Optical Remote Sensing

scholarly journals Assessment and Comparison of Six Machine Learning Models in Estimating Evapotranspiration over Croplands Using Remote Sensing and Meteorological Factors

2021 ◽  
Vol 13 (19) ◽  
pp. 3838
Author(s):  
Yan Liu ◽  
Sha Zhang ◽  
Jiahua Zhang ◽  
Lili Tang ◽  
Yun Bai
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Hybrid Model ◽  
Regional Scale ◽  
Hybrid Models ◽  
Gradient Boosting ◽  
Accurate Estimation ◽  
Support Vector ◽  
Extreme Gradient Boosting ◽  
Monteith Equation

Accurate estimates of evapotranspiration (ET) over croplands on a regional scale can provide useful information for agricultural management. The hybrid ET model that combines the physical framework, namely the Penman-Monteith equation and machine learning (ML) algorithms, have proven to be effective in ET estimates. However, few studies compared the performances in estimating ET between multiple hybrid model versions using different ML algorithms. In this study, we constructed six different hybrid ET models based on six classical ML algorithms, namely the K nearest neighbor algorithm, random forest, support vector machine, extreme gradient boosting algorithm, artificial neural network (ANN) and long short-term memory (LSTM), using observed data of 17 eddy covariance flux sites of cropland over the globe. Each hybrid model was assessed to estimate ET with ten different input data combinations. In each hybrid model, the ML algorithm was used to model the stomatal conductance (Gs), and then ET was estimated using the Penman-Monteith equation, along with the ML-based Gs. The results showed that all hybrid models can reasonably reproduce ET of cropland with the models using two or more remote sensing (RS) factors. The results also showed that although including RS factors can remarkably contribute to improving ET estimates, hybrid models except for LSTM using three or more RS factors were only marginally better than those using two RS factors. We also evidenced that the ANN-based model exhibits the optimal performance among all ML-based models in modeling daily ET, as indicated by the lower root-mean-square error (RMSE, 18.67–21.23 W m−2) and higher correlations coefficient (r, 0.90–0.94). ANN are more suitable for modeling Gs as compared to other ML algorithms under investigation, being able to provide methodological support for accurate estimation of cropland ET on a regional scale.

Introducing a Hybrid Model SAE-BP for Regression Analysis of Soil Temperature With Hyperspectral Data

2020 ◽  
Vol 11 (3) ◽  
pp. 66-79 ◽  
Author(s):  
Miaomiao Ji ◽  
Keke Zhang ◽  
Qiufeng Wu
Keyword(s):  
Remote Sensing ◽  
Soil Temperature ◽  
Hybrid Model ◽  
Terrestrial Ecosystems ◽  
Hyperspectral Data ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Bp Model

Soil temperature, as one of the critical meteorological parameters, plays a key role in physical, chemical and biological processes in terrestrial ecosystems. Accurate estimation of dynamic soil temperature is crucial for underground soil ecological research. In this work, a hybrid model SAE-BP is proposed by combining stacked auto-encoders (SAE) and back propagation (BP) algorithm to estimate soil temperature using hyperspectral remote sensing data. Experimental results show that the proposed SAE-BP model achieves a more stable and effective performance than the existing logistic regression (LR), support vector regression (SVR) and BP neural network with an average value of mean square error (MSE) = 1.926, mean absolute error (MAE) = 0.962 and coefficient of determination (R2) = 0.910. In addition, the effect of hidden structures and labeled training data ratios in SAE-BP is further explored. The SAE-BP model demonstrates the potential in high-dimensional and small hyperspectral datasets, representing a significant contribution to soil remote sensing.

The synergistic use of Sentinel SAR and optical remote sensing for mapping high-resolution soil moisture

2020 ◽  
Author(s):  
Jianxiu Qiu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Large Scale ◽  
Vegetation Index ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Biological Variables ◽  
Research Activities

<p>The launch of series of Sentinel constellations has provided data continuity of ERS, Envisat, and SPOT-like observations, in order to meet various observational needs for spatially explicit physical, biogeophysical, and biological variables of the ocean, cryosphere, and land research activities. The synergistic use of this publicly-accessible SAR images and temporally collocated optical remote sensing datasets has provided great potential for estimating high-resolution soil moisture information. In this study, advanced integral equation model (AIEM) which simulates the backscattering coefficient of bare soil and the Water-Cloud Model (WCM) accounting for the scattering effect from vegetation, are coupled to map high-resolution soil moisture. Validation conducted in large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research (HiWATER-MUSOEXE) in northwest of China showed RMSE of 0.04~0.071 m3m3. In addition, the accuracies in describing vegetation contribution from backscatter coefficient were intercompared between different models including WCM and ratio vegetation model. Sensitivity analysis of soil moisture estimation accuracy to vegetation index also extends to different optical remote sensing data sets including Sentinel-2, Landsat 8 and MODIS.</p>

scholarly journals Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China

2016 ◽  
Vol 10 (5) ◽  
pp. 2453-2463 ◽  
Author(s):  
Xiaodong Huang ◽  
Jie Deng ◽  
Xiaofang Ma ◽  
Yunlong Wang ◽  
Qisheng Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Winter Season ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
The Tibetan Plateau ◽  
Optical Remote Sensing ◽  
The North ◽  
The Mean

Abstract. By combining optical remote sensing snow cover products with passive microwave remote sensing snow depth (SD) data, we produced a MODIS (Moderate Resolution Imaging Spectroradiometer) cloudless binary snow cover product and a 500 m snow depth product. The temporal and spatial variations of snow cover from December 2000 to November 2014 in China were analyzed. The results indicate that, over the past 14 years, (1) the mean snow-covered area (SCA) in China was 11.3 % annually and 27 % in the winter season, with the mean SCA decreasing in summer and winter seasons, increasing in spring and fall seasons, and not much change annually; (2) the snow-covered days (SCDs) showed an increase in winter, spring, and fall, and annually, whereas they showed a decrease in summer; (3) the average SD decreased in winter, summer, and fall, while it increased in spring and annually; (4) the spatial distributions of SD and SCD were highly correlated seasonally and annually; and (5) the regional differences in the variation of snow cover in China were significant. Overall, the SCD and SD increased significantly in south and northeast China, and decreased significantly in the north of Xinjiang province. The SCD and SD increased on the southwest edge and in the southeast part of the Tibetan Plateau, whereas it decreased in the north and northwest regions.

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