Forest ecosystems, as the primary component of terrestrial ecosystems, provide essential ecosystem services (ESs) critical for sustainable human development. However, changes in climate and vegetation can alter these forest ESs. Understanding the complex relationships between regional climate, vegetation, and ESs is key to ensuring the sustainable management of forest ESs. Therefore, this study, using Baishanzu National Park as a case example, analyzed the impacts of regional climate and vegetation dynamics (vegetation coverage, forest type, and forest structure) on forest ESs, specifically water yield (WY), soil conservation (SC), net primary productivity (NPP), and habitat quality (HQ). The results indicate that from 2000 to 2020, the forest Composite Index of Ecosystem Services (CIES) in Baishanzu National Park increased. Climate and vegetation dynamics have significant effects on forest ESs. Specifically, changes in WY and SC are primarily influenced by climate change, while changes in NPP and HQ are mainly affected by changes in forest type and structure. Complex trade-offs and synergies exist among different ESs, and the driving mechanisms of climate and vegetation changes on ES variations are also complex, involving both direct and indirect effects, with significant spatial heterogeneity. This study provides important references for the sustainable management and appropriate restoration of regional forest ESs.
The publicly available SLR-derived monthly Earth’s oblateness C20 (or J2) time series exhibits varying levels of noise or systematic errors depending on the processing strategies used during estimation. We propose the use of two strategies, variance component estimate (VCE) and equal weighting (EW), to combine the SLR-derived J2 time series from four internationally renowned institutions. The resulting combined J2 time series is theoretically expected to demonstrate enhanced quality by reducing noise and systematic errors compared to individual contributions. These two combined solutions alongside four individual solutions are then discussed by analyzing their effects on global and local mass changes, thus exploring the potential for an optimized J2 time series in GRACE applications. The two combined SLR-derived J2 time series can assess the accuracy of the GRACE-derived J2 time series, which has shown poor but progressively improving accuracy. Results indicate that the RL06 version model of GRACE-derived J2 significantly outperforms the RL05 version, thus rendering the 160-day ocean tide effect in RL06 negligible. Moreover, significant discrepancies and lower quality among the various institutions' GRACE-derived J2 lead to Antarctic/Greenland ice sheet mass change estimates notably deviating from SLR-derived results. Therefore, upon replacing the GRACE-derived J2 time series with the VCE-combined SLR-J2 time series, the difference between the calculated mass change of the Antarctic/Greenland ice sheet and the RACMO2.3p2 model is theoretically the smallest, displaying the highest correlation coefficient between them.
Quickly obtaining accurate soil quality information is the premise for accurate agricultural production and increased crop yield. With the development of the digital information industry, smart agriculture has become a new trend in agricultural development and there is increasing demand for efficiently and intelligently acquiring good soil quality information. Scientists worldwide have developed many remote sensing quantitative inversion models, which need to be systematized and intelligent for agricultural personnel to enjoy the dividends of information technology such as 3S (remote sensing, geographic information system, and global navigation satellite system) techniques. Accordingly, to meet the need of farmers, agricultural managers, and agricultural researchers to acquire timely information on regional soil quality, in this paper, we designed a cloud platform for inversion analysis of moisture, nutrient, salinity, and other important soil quality indicators. The platform was developed using ArcGIS (The software is produced by the Environmental Systems Research Institute, Inc. of America in Redlands, CL, USA) and GeoScene (The software is produced by GeoScene Information Technology Co.,Ltd., Beijing, China) software, with Java and JavaScript as programing languages and SQL Server as the database management system with a PC client, a web client, and a mobile app. On the basis of the existing quantitative remote sensing models, the platform realizes mapping functions, intelligent inversion of soil moisture–nutrient–salinity (SMNS) content, data analysis mining, soil knowledge base, platform management, and so on. It can help different users acquire, manage, and analyze data and make decisions based on the data. In addition, the platform can customize model parameters according to regional characteristics, improving analysis accuracy and expanding the application area. Overall, the platform employs 3S techniques, Internet technology, and mobile communication technology synthetically and realizes intelligent inversion and decision analysis of significant soil quality information, such as moisture–nutrient–salinity content. This platform has been applied to the analysis of soil indicators in several areas and has produced good operational results and benefits. This study will enable rapid data analysis and provide technical support for regional agriculture production, contributing to the development of smart agriculture.
Abstract Lake Qinghai is the most important of the Chinese radiometric calibration sites (CRCSs) for on-orbit radiometric calibration of satellite infrared (IR) channels. This letter introduces the site and describes annual CRCS field experiments, the buoy measurement facilities and surface temperature spatial distribution analysis based on Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra (EOS AM) satellite and buoy data for Lake Qinghai. The lake located at a high altitude (3196 m) has clean water, a stable geological substrate and a dry and clear atmosphere, with few aerosol particulates. The spatial distribution of the water surface temperature is very uniform. For these reasons, Lake Qinghai is a good thermal IR target for the radiometric calibration of Earth observation remote sensors and is used not only for Chinese satellites but also for on-orbit radiometric calibration of many Earth observation satellites. Acknowledgements This work is supported by National Natural Science Foundation of China (grant no. 41171275, no. 40905014 and no. 40701118) and the R&D Special Fund for Public Welfare Industry (grant no. GYHY200906036). We thank Lei Yang from China Center for Resources Satellite Data and Application for providing HJ-1B data.
Given that many operational satellite sensors are not calibrated, while a handful of research sensors are, cross-calibration between the two types of sensor is a cost-effective means of calibration. A new method of sensor cross-calibration is demonstrated here using the Chinese Multi-channel Visible Infrared Scanning radiometer (MVIRS) and the US Moderate Resolution Imaging Spectrometer (MODIS). MVIRS has six channels, equivalent to the current National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and four additional ones for remote sensing of ocean colour and moisture. The MVIRS on-board China's polar-orbiting meteorological satellite (FY-1D) was launched on 15 May 2002 with an earlier overpass time than Terra. The sensor has no on-board calibration assembly. This study attempts to calibrate MVIRS against the well-calibrated MODIS, by taking a series of measures to account for their differences. Clear-sky measurements made from the two sensors in July-October 2002 were first collocated. Using the 6S radiative transfer model, MODIS reflectances measured at the top-of-the atmosphere were converted into surface reflectances. They were corrected to the viewing geometry of the MVIRS using the bidirectional reflectance distribution function (BRDF) measured on the ground. The spectral response functions of the two sensors were employed to account for spectral discrepancies. After these corrections, very close linear correlations were found between radiances estimated from the MODIS and the digital readings from the MVIRS, from which the calibration gains were derived. The gains differ considerably from the pre-launch values and are subject to degradation over time. The calibration accuracy is estimated to be less than 5%, which is compatible to that obtained by the more expensive vicarious calibration approach.
In China, crop structure adjustment policy has brought great change of different breed's planting area in different years. Government managers of agricultural industry need timely crop structure information to monitor the performance of the crop structure adjustment policy. The objective of this research was to evaluate the synergistic effects of multitemporal RADAR SAT synthetic aperture radar (SAR) and Land sat ETM+ data for extracting agricultural crops structure using an object-oriented classification approach. This work instructs and analyses the crop structure near the Kaifeng city area in 2002. Four crop types were extracted: corn, soybean, cotton, and peanut. With the object-oriented classification approach, the overall accuracy of crop structure extracting from two-date F5 mode's SAR data (mid- to last-season) and two-date Land sat ETM+ is over 90%.
The meadow surface emissivita spectra of the Xilinhaote grassland of China is one of the key factors for calibration of thermal infrared remote sensors using land surface.Based on the iterative spectrally smooth temperature/emissivity separation(ISSTES) algorithm,Xilinhaote meadow surface emissivity spectra were measured using a BOMEM MR154 Fourier transform spectroradiometer and infrared golden board.Emissivity spectra data were obtained at different times and land surface conditions.With these measured emissivity spectra,all of the mainstream thermal infrared remote sensors can be calibrated and validated using Xilinhaote meadow surface.
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