scholarly journals The Generation of Soil Spectral Dynamic Feedback Using Landsat 8 Data for Digital Soil Mapping

2020 ◽  
Vol 12 (10) ◽  
pp. 1691
Author(s):  
Canying Zeng ◽  
Lin Yang ◽  
A-Xing Zhu
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Landsat 8 ◽  
Soil Drying ◽  
Dynamic Feedback ◽  
Drying Process ◽  
Fusion Data

The soil spectral dynamic feedback captured from high temporal resolution remote sensing data, such as MODIS, during the soil drying process after a rainfall could assist with digital soil mapping. However, this method is ineffective in utilizing the images with a relatively high spatial resolution. There are an insufficient number of images in the soil drying process since those high spatial resolution images tend to have a low temporal resolution. This study is aimed at generating soil spectral dynamic feedback by integrating the feedback captured from the images with a high spatial resolution during the process of multiple drying after different rainfall events. The Landsat 8 data with a temporal resolution of 16 day was exemplified. Each single spectral feedback obtained from Landsat 8 was first adjusted to eliminate the impact of different rainfall magnitudes. Then, the soil spectral dynamic feedback was reorganized and generated based on the adjusted feedback. Finally, the soil spectral dynamic feedback generated based on Landsat 8 was used for mapping topsoil texture and compared with the mapping results based on the MODIS data and the fusion data of MODIS and Landsat 8. As revealed by the results, not only could the generated soil spectral dynamic feedback based on Landsat 8 data improve the details of the spatial distribution of soil texture, but it also enhances the accuracy of mapping. The mapping accuracy based on Landsat 8 data is higher than that based on the MODIS data and fusion data. The improvements of accuracy are more obvious in the areas with more complex surface conditions. This study widens the scope of application for soil spectral dynamic feedback and provides support for large-scale and high-precision digital soil mapping.

scholarly journals Dual-satellite (Sentinel-2 and Landsat 8) remote sensing of supraglacial lakes in Greenland

2018 ◽  
Author(s):  
Andrew G. Williamson ◽  
Alison F. Banwell ◽  
Ian C. Willis ◽  
Neil S. Arnold
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Greenland Ice Sheet ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Supraglacial Lakes ◽  
Dual Sensor ◽  
Sentinel 2

Abstract. Although remote sensing is commonly used to monitor supraglacial lakes on the Greenland Ice Sheet, most satellite records must trade-off high spatial resolution for high temporal resolution (e.g. MODIS) or vice versa (e.g. Landsat). Here, we overcome this issue by developing and applying a dual-sensor method that can monitor changes to lake areas and volumes at high spatial resolution (10–30 m) with a frequent revisit time (~ 3 days). We achieve this by mosaicking imagery from the Landsat 8 OLI with imagery from the recently launched Sentinel-2 MSI for a ~ 12 000 km2 area of West Greenland in summer 2016. First, we validate a physically based method for calculating lake depths with Sentinel-2 by comparing measurements against those derived from the available contemporaneous Landsat 8 imagery; we find close correspondence between the two sets of values (R2 = 0.841; RMSE = 0.555 m). This provides us with the methodological basis for automatically calculating lake areas, depths and volumes from all available Landsat 8 and Sentinel-2 images. These automatic methods are incorporated into an algorithm for Fully Automated Supraglacial lake Tracking at Enhanced Resolution (FASTER). The FASTER algorithm produces time series showing lake evolution during the 2016 melt season, including automated rapid (≤ 4 day) lake-drainage identification. With the dual Sentinel-2-Landsat 8 record, we identify 184 rapidly draining lakes, many more than identified with either imagery collection alone (93 with Sentinel-2; 66 with Landsat 8), due to their inferior temporal resolution, or would be possible with MODIS, due to its omission of small lakes 

A four-channel ICCD framing camera with nanosecond temporal resolution and high spatial resolution

2021 ◽  
pp. 1-9
Author(s):  
Yuman Fang ◽  
Minrui Zhang ◽  
Junfeng Wang ◽  
Lehui Guo ◽  
Xueling Liu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Framing Camera

scholarly journals Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

2019 ◽  
Vol 11 (14) ◽  
pp. 1683 ◽  
Author(s):  
Yangchengsi Zhang ◽  
Long Guo ◽  
Yiyun Chen ◽  
Tiezhu Shi ◽  
Mei Luo ◽  
...  
Keyword(s):  
Time Series ◽  
Organic Carbon ◽  
Time Series Data ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Series Data ◽  
Landsat 8 ◽  
Auxiliary Variables ◽  
Jianghan Plain ◽  
Ndvi Time Series

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

scholarly journals A CNN-Based Pan-Sharpening Method for Integrating Panchromatic and Multispectral Images Using Landsat 8

2019 ◽  
Vol 11 (22) ◽  
pp. 2606 ◽  
Author(s):  
Zhiqiang Li ◽  
Chengqi Cheng
Keyword(s):  
Spatial Resolution ◽  
Mean Squared Error ◽  
High Spatial Resolution ◽  
Multispectral Image ◽  
Landsat 8 ◽  
Spectral Angle Mapper ◽  
Learning Capacity ◽  
Panchromatic Image ◽  
Highly Nonlinear ◽  
Construction Tasks

The increasing availability of sensors enables the combination of a high-spatial-resolution panchromatic image and a low-spatial-resolution multispectral image, which has become a hotspot in recent years for many applications. To address the spectral and spatial distortions that adversely affect the conventional methods, a pan-sharpening method based on a convolutional neural network (CNN) architecture is proposed in this paper, where the low-spatial-resolution multispectral image is upgraded and integrated with the high-spatial-resolution panchromatic image to produce a new multispectral image with high spatial resolution. Based on the pyramid structure of the CNN architecture, the proposed method has high learning capacity to generate more representative and robust hierarchical features for construction tasks. Moreover, the highly nonlinear fusion process can be effectively simulated by stacking several linear filtering layers, which is suitable for learning the complex mapping relationship between a high-spatial-resolution panchromatic and low-spatial-resolution multispectral image. Both qualitative and quantitative experimental analyses were carried out on images captured from a Landsat 8 on-board operational land imager (LOI) sensor to demonstrate the method’s performance. The results regarding the sensitivity analysis of the involved parameters indicate the effects of parameters on the performance of our CNN-based pan-sharpening approach. Additionally, our CNN-based pan-sharpening approach outperforms other existing conventional pan-sharpening methods with a more promising fusion result for different landcovers, with differences in Erreur Relative Globale Adimensionnelle de Synthse (ERGAS), root-mean-squared error (RMSE), and spectral angle mapper (SAM) of 0.69, 0.0021, and 0.81 on average, respectively.

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

scholarly journals Evaluating the Impacts of Continuous and Rotational Grazing on Tallgrass Prairie Landscape Using High-Spatial-Resolution Imagery

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 238 ◽  
Author(s):  
Shengfang Ma ◽  
Yuting Zhou ◽  
Prasanna H. Gowda ◽  
Liangfu Chen ◽  
Patrick J. Starks ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Tallgrass Prairie ◽  
Landscape Metrics ◽  
High Spatial Resolution ◽  
Shrub Encroachment ◽  
Rotational Grazing ◽  
Grass Growth

This study evaluated the impacts of different grazing treatments (continuous (C) and rotational (R) grazing) on tallgrass prairie landscape, using high-spatial-resolution aerial imagery (1-m at RGB and near-infrared bands) of experimental C and R pastures within two replicates (Rep A and Rep B) in the southern Great Plains (SGP) of the United States. The imagery was acquired by the National Agriculture Imagery Program (NAIP) during the agricultural growing season of selected years (2010, 2013, 2015, and 2017) in the continental United States. Land cover maps were generated by combining visual interpolation, a support vector machine, and a decision tree classifier. Landscape metrics (class area, patch number, percentage of landscape, and fragmentation indices) were calculated from the FRAGSTATS (a computer software program designed to compute a wide variety of landscape metrics for categorical map patterns) based on land cover results. Both the metrics and land cover results were used to analyze landscape dynamics in the experiment pastures. Results showed that both grass and shrubs of different pastures differed largely in the same year and had significant annual dynamics controlled by climate. High stocking intensity delayed grass growth. A large proportion of bare soil occurred in sub-paddocks of rotational grazing that were just grazed or under grazing. Rep A experienced rapid shrub encroachment, with a large proportion of shrub at the beginning of the experiment. Shrub may occupy 41% of C and 15% of R in Rep A by 2030, as revealed by the linear regression analysis of shrub encroachment. In contrast, shrub encroachment was not significant in Rep B, which only had a small number of shrub patches at the beginning of the experiment. This result indicates that the shrub encroachment is mainly controlled by the initial status of the pastures instead of grazing management. However, the low temporal resolution of the NAIP imagery (one snapshot in two or three years) limits our comparison of the continuous and rotational grazing at the annual scale. Future studies need to combine NAIP imagery with other higher temporal resolution imagery (e.g., WorldView), in order to better evaluate the interannual variabilities of grass productivity and shrub encroachment.

scholarly journals Estimating 2009–2017 Impervious Surface Change in Gwadar, Pakistan Using the HJ-1A/B Constellation, GF-1/2 Data, and the Random Forest Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 443 ◽  
Author(s):  
Bian ◽  
Li ◽  
Zuo ◽  
Lei ◽  
Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Satellite Images ◽  
Reference Data ◽  
High Spatial Resolution ◽  
Impervious Surface ◽  
Urbanization Process ◽  
Rapid Urbanization

The China–Pakistan Economic Corridor (CPEC) is the flagship project of the Belt and Road Initiative. At the end of the CPEC, the Gwadar port on the Arabian Sea is being built quickly, providing an important economical route for the flow of Central Asia’s natural resources to the world. Gwadar city is in a rapid urbanization process and will be developed as a modern, world-class port city in the near future. Therefore, monitoring the urbanization process of Gwadar at both high spatial and temporal resolution is vital for its urban planning, city ecosystem management, and the sustainable development of CPEC. The impervious surface percentage (ISP) is an essential quantitative indicator for the assessment of urban development. Through the integration of remote sensing images and ISP estimation models, ISP can be routinely and periodically estimated. However, due to clouds’ influence and spatial–temporal resolution trade-offs in sensor design, it is difficult to estimate the ISP with both high spatial resolution and dense temporal frequency from only one satellite sensor. In recent years, China has launched a series of Earth resource satellites, such as the HJ (Huangjing, which means environment in Chinese)-1A/B constellation, showing great application potential for rapid Earth surface mapping. This study employs the Random Forest (RF) method for a long-term and fine-scale ISP estimation and analysis of the city of Gwadar, based on the density in temporal and multi-source Chinese satellite images. In the method, high spatial resolution ISP reference data partially covering Gwadar city was first extracted from the 1–2 meter (m) GF (GaoFen, which means high spatial resolution in Chinese)-1/2 fused images. An RF retrieval model was then built based on the training samples extracted from ISP reference data and multi-temporal 30-m HJ-1A/B satellite images. Lastly, the model was used to generate the 30-m time series ISP from 2009 to 2017 for the whole city area based on the HJ-1A/B images. Results showed that the mean absolute error of the estimated ISP was 6.1–8.1% and that the root mean square error (RMSE) of the estimation results was 12.82–15.03%, indicating the consistently high performance of the model. This study highlights the feasibility and potential of using multi-source Chinese satellite images and an RF model to generate long-term ISP estimations for monitoring the urbanization process of the key node city in the CPEC.

scholarly journals An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

2017 ◽  
Vol 44 ◽  
pp. 89-100 ◽  
Author(s):  
Luca Cenci ◽  
Luca Pulvirenti ◽  
Giorgio Boni ◽  
Marco Chini ◽  
Patrick Matgen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Hydrological Model ◽  
High Spatial Resolution ◽  
Flash Flood ◽  
Soil Moisture Data ◽  
Time Period ◽  
The Impact

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

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