Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of <0 °C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisis.

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

Remote Sensing ◽

10.3390/rs11101163 ◽

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.

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

The Cryosphere ◽

10.5194/tc-10-2453-2016 ◽

2016 ◽

Vol 10 (5) ◽

pp. 2453-2463 ◽

Cited By ~ 31

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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Mapping Paddy Rice with Satellite Remote Sensing: A Review

Sustainability ◽

10.3390/su13020503 ◽

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.

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Remote sensing of soil surface properties

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133309346644 ◽

2009 ◽

Vol 33 (4) ◽

pp. 457-473 ◽

Cited By ~ 42

Author(s):

K. Anderson ◽

H. Croft

Keyword(s):

Remote Sensing ◽

Spatial Data ◽

Research Question ◽

Soil Surface ◽

Microwave Remote Sensing ◽

Optical Remote Sensing ◽

Future Challenges ◽

Soil Surface Roughness ◽

Remote Sensing Techniques ◽

New Research

Remote sensing is now in a strong position to provide meaningful spatial data for use in soil science investigations. In the last 10 years, advancements in remote sensing techniques and technologies have given rise to a wealth of exciting new research findings in soil-related disciplines. This paper provides a critical insight into the role played by remote sensing in this field, with a specific focus on soil surface monitoring. Two key soil properties are considered in this review, soil surface roughness and moisture, because these two variables have benefited most from recent cutting-edge advances in remote sensing. Of note is the fact that the major recent advancements in spatial assessment of soil structure have emerged from optical remote sensing, while the soil moisture community has benefited from advancements in microwave systems, justifying the focus of this paper in these specific directions. The paper considers the newest techniques within active, passive, optical and microwave remote sensing and concludes by considering future challenges, multisensor approaches and the issue of scale — which is a key cross-disciplinary research question of relevance to soil scientists and remote sensing scientists alike.

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