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Global Navigation Satellite System Reflectometry (GNSS-R) is an emerging earth observation method for remote sensing of feature parameters using reflected signals from navigation satellites, and is a purely specular bistatic forward scattering observation means with special right-handed circular polarization incident wave. In this paper, the small-slope approximation model of non-Gaussian sea surface is used as the basis to construct the scattering model for the observation geometry of GNSS-R as well as the L-band characteristics, and the fully-polarization normalized bistatic radar scattering cross section (NBRCS) are simulated by the method of polarization synthesis to analyze the scattering characteristics under different wind speeds and directions on the ocean surface, which highlights the variation of NBRCS with wind direction, and the scattering modeling accuracy is improved by comparing with the data of CYGNSS. In addition, we adopt the observation geometry deviating from purely specular geometry, discuss the scattering azimuth angle, scattering influence, and the relative relationship between different polarizations of the scattering angle under the non-specular geometry. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS), has a significant impact on all areas of human activity, not only can provide users with shared global navigation, position and timing information, but also can provide a L-band microwave signal source of long term stability and high temporal-spatial resolution. In recent years, development of the navigation satellite remote sensing applications using GNSS as a external illuminator, it has been forming a new Global Navigation Satellite System METeorology (GNSS/MET), of which Global Navigation Satellite System-Reflection (GNSS-R) signals remote sensing technology is rising. It could be considered as a non-cooperative artificial illuminator, bistatic (multi-static) radar system, and has the advantages of both passive and active remote sensing. Then it gets more and more people’s attention and favor, and broadening into Atmosphere -ocean and land surface remote sensing fields. However, the address of this technology is very messy at home and abroad, and not able to accurately express its special meaning. This article attempts to give a new term: Exogenous-Aided Remote Sensing (EARS) for discussion.

Remotely sensed soil moisture (SM) with high accuracy and high spatial–temporal resolution is crucial to meteorological, agricultural, hydrological, and environmental applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that uses the L-band signal transmitted by the GNSS satellites to develop daily SM data products. In this study, a physics-based algorithm is proposed to couple CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature estimates for accurate SM retrieval. The algorithm is based on the radiative transfer model and the SMAP data to derive a combined parameter of the vegetation optical depth ($\tau$) and the surface roughness parameter ($h$). The CYGNSS L1 Version 2.1 data of the years 2017–2018 and 2019–2020 are used for calibration and validation, respectively. The SM estimates agree and correlate well with the SMAP SM and in situ SM data on a global scale ($R =0.679$ , RMSE $=0.051\,\,\text{m}^{3}\text{m}^{-3}$ , and MAE $=0.045\,\,\text{m}^{3}\text{m}^{-\mathrm {3}}$ against SMAP SM; $R = 0.729$ against in situ SM). The proposed algorithm makes contributions from two aspects. First, the proposed algorithm provides a physics-based algorithm using SMAP brightness temperature to calibrate the attenuation due to vegetation and surface roughness on the CYGNSS-derived SR. Unlike attenuation models that have been explored previously in the context of CYGNSS, this algorithm executes the calibration without relying on observations of $h$ or vegetation biophysical parameters as inputs but with the SMAP brightness temperature as the only observations. Second, the proposed algorithm provides a new way for the combined usage of CYGNSS and SMAP to improve the temporal and spatial coverages of global SM with temporal coverage increased by 38.2% and spatial coverage increased by 31.6%. [ABSTRACT FROM AUTHOR]

The precipitation phase (i.e., rain and snow) is important for the global hydrologic cycle and climate system. The objective of this study is to evaluate the precipitation-phase partitioning capabilities of remote sensing and reanalysis modeling methods on the global scale. Specifically, observation data from the National Centers for Environmental Prediction (NCEP) Automated Data Processing (ADP), from 2000 to 2007, are used to evaluate the rain–snow discrimination accuracy of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) and the fifth-generation reanalysis product of the European Centre for Medium Range Weather Forecasts (ERA5). The results show that: (1) the ERA5 performs better than the IMERG at distinguishing rainfall and snowfall events, overall. (2) The ERA5 has high accuracy in all continents except for South America, while the IMERG performs well only in Antarctica and North America. (3) Compared with the IMERG, the ERA5 can more effectively capture snowfall events at high latitudes but shows worse performance at mid-low latitude regions. Both the IMERG and ERA5 have lower accuracy for rain–snow partitioning under heavy precipitation. Overall, the results of this study provide references for the application and improvement of global rain–snow partitioning products. [ABSTRACT FROM AUTHOR]

Man-made reservoirs are key components of terrestrial hydrological systems. Identifying the location and number of reservoirs is the premise for studying the impact of human activities on water resources and environmental changes. While complete bottom-up censuses can provide a comprehensive view of the reservoir landscape, they are time-consuming and laborious and are thus infeasible on a global scale. Moreover, it is challenging to distinguish man-made reservoirs from natural lakes in remote sensing images. This study proposes a convolutional neural network (CNN)-based framework to recognize global reservoirs from Landsat 8 imageries. On the basis of the HydroLAKES dataset, a Landsat 8 cloud-free mosaic of 2017 was clipped for each feature (reservoir or lake) and was resized into 224 × 224 patches, which were collected as training and testing samples. Compared to other deep learning methods (Alexnet and VGG) and state-of-the-art traditional machine learning methods (support vector machine, random forest, gradient boosting, and bag-of-visual-words), we found that fine-tuning the pretrained CNN model, ResNet-50, could reach the highest accuracy (91.45%). Application cases in Kansas (USA, North America), Mpumalanga (South Africa, Africa), and Kostanay (Kazakhstan, Asia) resulted in classification accuracies of better than 99%, which showed the applicability of the proposed ResNet-50 model to the extraction of reservoirs from a vast amount of moderate resolution images. The framework that was developed in this paper is the first attempt to combine remote sensing big data and the deep learning technique to the recognition of reservoirs at a global scale. [ABSTRACT FROM AUTHOR]

Abstract Preparing experiment-ready images is a labor-intensive but necessary process for almost all remote sensing related studies. This short communication introduces a software to efficiently acquire large-scale high-quality and cloud-free Landsat images to support subsequent analysis and applications. The software fully leverages Google Earth Engine's processing of huge amount of remote sensing images, and the visualization on the availability and accessibility of Landsat images provided by heatmaps at both regional and global scales. The cloud percentage property and quality bands of Landsat surface reflectance products are used for masking clouds, cloud shadows, snow/ice, and low-quality pixels in this study. The software can be applied to effectively choose study areas and prepare data in different fields, such as hydrology, ecology, and human activities. This functioning open-source software can be accessible via GitHub webpages in both JavaScript and Python formats. [ABSTRACT FROM AUTHOR]

National Oceanography and Atmosphere Administration's Advanced Very High Resolution Radiometer (AVHRR) (1970s–present, ∼1 km) and China's GaoFen-1 (GF-1) image series (2013–present, ∼16 m), can both provide extremely useful information of land use and land cover, due to the long history and, wide scanning width of AVHRR and the high resolution of GF-1. However, both data sets have apparent weaknesses of large and inconsistent geometric and geolocation errors (AVHRR: 1–20 pixels, GF-1: 1–10 pixels), prohibiting them from many interdisciplinary applications. This paper, based on feature point detection method, presents a new approach to improve the georeferencing of AVHRR and GF-1 data using inland water bodies. No need of a prior knowledge of the error source and only based on the image information and the base map, this method is able to automatically search and find the optimal water threshold value (reflectance), conduct feature point detecting and matching, and perform affine transformation. In the case study of the Tibetan Plateau, water body (i.e., lake) with its lower reflectance and relatively stable location and shape, is used as the referencing mask (i.e., base map) for detecting feature point pairs. The accuracy of the geometric correction is found within subpixel level, with average RMSE = 0.76 for AVHRR and RMSE = 0.91 for GF-1. The developed georeferencing method can be potentially applied to other less accessible areas that are difficult to derive ground control points for numerous applications with improved location and geometric accuracies. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented which includes articles on topics including application of Synthetic Aperture Radar (SAR) Backscatter and InSAR coherence to monitor the wetland extent across Amazon lowland, and precipitation estimates, using remote sensing data.

The Global Navigation Satellite System Reflectometry (GNSS-R) has been proven to be a powerful technique for retrieving geophysical parameters of ocean and land. Airborne GNSS-R is an important experimental platform, because it is not only needed as validation for spaceborne application, but also possesses the advantages to be capable of remote sensing of small and medium scale targets, such as rivers and lakes. This paper presents an overview of China's airborne GNSS-R campaign conducted on May 30, 2014, in Henan. The campaign has two objectives, i.e.: (1) to examine the capability of a GNSS-R payload developed by National Space Science Center, Chinese Academy of Sciences (NSSC, CAS) for airborne observations and (2) to study the algorithms for soil moisture and river remote sensing, including altimetry and flow velocity measurement. A previous paper has presented results of soil moisture retrieval as part I, and in this paper, initial results of the Yellow River remote sensing are presented as part II. This paper presents the river altimetry results and explores a new potential application of GNSS-R technology, which is used to detect the flow velocity of the river. The river surface height results observed by code delay altimetry method were consistent with the height results of GPS dual-frequency differential positioning altimetry. The GNSS-R altimetry results showed that decimeter level heights were achieved in 1-min sliding average. Comparing with in situ measurements, the GNSS-R flow velocity result was reasonable; the error was about 0.027 m/s, which indicated the validity and feasibility of using GNSS-R technique to detect river flow velocity. [ABSTRACT FROM AUTHOR]

• The Carbon sink capacity of a typical ecotypic county was precisely estimated. • STARFM was employed to estimate interannual NEP at county scale. • The NEP dataset at a 30 m grid scale from 2000 to 2020 was generated. • Spatio-temporal dynamics of NEP in ecotype county were grasped. Net ecosystem productivity (NEP) serves as a pivotal metric for quantitatively elucidating the carbon sink function of terrestrial ecosystems. As a prototype county for the development of an ecological civilization in China, the quantitative estimation of the ecotypic county's ecosystem carbon sink capacity holds immense significance in comprehending the carbon cycle and facilitating the sustainable advancement of regional ecosystems. This study undertook the estimation of NEP in Wuning County from 2000 to 2020, employing a fusion of multi-source remote sensing data, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the improved Carnegie-Ames-Stanford Approach model, and the soil respiration model. Furthermore, we delved into the differences in NEP across various types of land cover. In addition, we employed the Theil-Sen Median trend analysis and Mann-Kendall test to discern the spatio-temporal trends of NEP. The findings indicated the following: (1) The downscaled NDVI generated by STARFM exhibited a remarkable consistency with Landsat NDVI overall (R 2 > 0.95, P < 0.01, 0 < RMSE < 0.1). (2) The gross NEP from 2000 to 2020 in the study area ranged from 542.78 to 720 Gg C, with a multi-year average NEP of 183.84 g C m−2 yr−1. The simulated NEP demonstrated higher accuracy when compared to the measured data (R 2 = 0.79, P < 0.01). (3) The NEP exhibited a spatial pattern characterized by lower values in the central area and higher values in the north and south. Approximately 89.60 % of the total area demonstrated an increase in NEP, with woodland acting as the primary contributor, while 4.50 % of the total area displayed a decreasing trend, predominantly due to the expansion of built-up land. (4) Notable variations in NEP existed among different types of land cover. In terms of vegetation types, the annual average NEP ranked as follows: woodland > grassland > cropland. The application of STARFM has provided valuable insights into the methodology for precise delineation of spatio-temporal dynamics of NEP at the county scale. The outcomes of this study have furnished support for implementing climate change mitigation strategies in ecologically-oriented counties and the bottom-up promotion of China's carbon peaking and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Abstract: As the quality and accuracy of remote-sensing instruments improve, the ability to quickly process remotely sensed data is in increasing demand. Quantitative remote-sensing retrieval is a complex computing process because of the terabytes or petabytes of data processed and the tight-coupling remote-sensing algorithms. In this paper, we intend to demonstrate the use of grid computing for quantitative remote-sensing retrieval applications with a workload estimation and task partition algorithm. Using a grid workflow for the quantitative remote-sensing retrieval service is an intuitive way to use the grid service for users without grid expertise. A case study showed that significant improvement in the system performance could be achieved with this implementation. The results of the case study also give a perspective on the potential of applying grid computing practices to remote-sensing problems. [ABSTRACT FROM AUTHOR]

• New method is proposed to assess accuracy of satellite products in ungauged basins. • Mutual cancellation in product errors is avoided in water budget closure. • Results of proposed method are examined by the application of FORM. • The proposed method showed lower dependence on precipitation. Remote sensing products have been widely used in water resources assessment and management. However, the accuracy varies in different products. Water budget closure provides an important breakthrough for better assessing the uncertainties and quantifying error sources of satellite-based data products using water budget imbalance, especially in ungauged basins where conventional verification methods are not applicable. However, the commonly used assessment methods mainly refer to closure residual of water budget Δ Re s or its proportion to corresponding mean precipitation (P) Δ Re s / P , which have apparent limitations due to the dimensional effects in the former method and the high dependence on precipitation in the latter one. More critically, these two methods do not consider measured values (referring to in-situ values in gauged basins or average values of multiple satellite products in ungauged basins as in this study) as benchmarks of water budget components in the assessment of satellite-based hydrological data products, resulting in a wrong choice because the closure result is subject to the mutual cancellation in the errors of water budget components from different satellite-based products. This study proposes a novel error-based method to assess the accuracy of satellite-based hydrological data products for both gauged and ungauged basins based on water budget equation, measured values, and the omission error (OE) which is generally overlooked in existing studies. The method is applied to a typical closed basin, the Tarim River Basin (TRB) of China, to verify the applicability and reliability of the proposed method. Sixty combinations of water budget components from various satellite sources can be designed based on five precipitation (P) products, four evapotranspiration (ET) products and three terrestrial water storage change (TWSC) products. First order reliability method (FORM) is employed to assess the reliability of water budget closure results caused by the errors of satellite sources. The results indicated that different combinations produced different annual Δ Re s and Δ Re s / P , indicating great water budget closure errors for different combinations due to errors in different satellite sources. The OE error from satellite-based hydrological data products of water budget components clearly showed a greater impact on water budget imbalance than water budget components, indicating that OE error is important for understanding quality of satellite products. By comparing the results of Δ Re s and Δ Re s / P , it can be seen that our proposed method had great advantages in assessment of satellite products with a low dependence on P, and a better comparability between different combinations. The value of our assessment criterion ranges from 0 to 1 which is easier to understand in practice. The combinations of water budget components from TRMM_3B43 (P), GPM_3IMERGHH (P), GLDAS_NOAH025 (ET), and GRACE (TWSC) products show satisfactory water budget closure results in TRB using our proposed method. The proposed method provides a new aspect for assessing the accuracy of satellite-based hydrological data products especially in ungauged basins, which provides insights into water resources management in basins. [ABSTRACT FROM AUTHOR]

• Rice planting areas in China from 2000 to 2021 were extracted. • Rice growth period in China from 2000 to 2021 were identified. • Rice biomass in China was evaluated during the growth period. • Driving mechanisms affecting rice biomass were quantified. China is a major rice-producing nation and understanding changes in rice planting areas and growth periods and the mechanisms driving rice biomass is important to ensure effective agricultural strategies and food security. In this study, rice planting areas from 2000 to 2021 in China were extracted using Savitzky–Golay filtering and the absolute distance index. Using MODIS remote sensing image data, we determined the rice growth period based on the inflection points of the enhanced vegetation index. In addition, the cumulative biomass of rice during the growth period since 2000 was determined based on the Carnegie–Ames–Stanford approach model. Finally, the key driving mechanisms and coupling relationships among the driving factors of rice biomass during the growth period were determined using geographically weighted regression and structural equation models. The results showed that the accumulation of net primary productivity (NPP) during the rice growth period in Northeast China, the middle and lower reaches of the Yangtze River, and Northwest China increased significantly (P < 0.01). The accumulation of NPP during the rice growth period in Huang-Huai-Hai, South China, and Southwest China decreased significantly (P < 0.01). In addition, Central Inner Mongolia, Qinghai–Tibet Plateau, and Loess Plateau have less rice planting area and no significant variation pattern in the accumulation of NPP during the rice growth period. In the nine major agricultural regions of China, precipitation, temperature, and evapotranspiration were among the most influential meteorological factors affecting rice biomass. For the inland areas with less precipitation, drainage density was an increasingly important influencing factor. Areas with high topographic relief were more likely to be affected by the slope. In areas with relatively poor soil, the fertilizer application rate influenced rice biomass growth. Our results provide insights into the spatio-temporal patterns of rice biomass and its driving mechanisms, and demonstrate the efficacy of using remote sensing data at a macro scale. [ABSTRACT FROM AUTHOR]

• Nearly 40 years of remote sensing-based water area data from Lake Chad are generated. • The total inundation area lags behind the open water area by approximately 2 – 3 months. • An interactive online application for inspecting yearly and monthly areas of Lake Chad is provided for the general public. As the second largest lake in Africa, the extent of Lake Chad has over 50% seasonal variations with large parts of water under aquatic vegetation. Although a great area shrinkage since the 1960s divided the lake into two parts, namely, the northern and southern basins, it still feeds millions of people from four countries around it. Given sparse in situ measurements, remote sensing with seamless spatial coverage has served for acquiring the long-term area series of Lake Chad. This study retrieves the open (unvegetated) surface water area series of Lake Chad and the total inundation area, including open water and water under macrophytes, combining multiple remote sensing data from around 1980 to 2020 with satellite-based water level and in situ observations at the Bol gauge as validation and auxiliary. Results show that the total inundation area continuously recovered at a rate of 145 km2/year from 1982 to 2020 with large annual fluctuations. Approximately two- to three-month time lags between the open surface water and the total inundation area reveal the hydrological asynchrony of Lake Chad, which can be attributed by the large area of rooted macrophytes. The overspill of the southern surface water flowing over the Great Barrier into the northern lake also has a one-month lag with the open surface water of the southern lake. The Google Earth Engine-based online application can be easily shared for use and data downloaded by the general public, and the open code can be adjusted for any other lakes worldwide. [ABSTRACT FROM AUTHOR]