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Genomic evidence suggests that lysogenic viruses significantly influence the evolution of their host communities and soil microbial ecology and functionality. However, the response of soil viral reproductive strategies (VRS) to environmental factors, in particular soil water stress, remains poorly understood. We investigated this by employing a laboratory microcosm incubation system with different soil moisture levels (30%, 60% and 90% field capacity). Our study focused on soil biochemical properties, bacterial and viral populations, lysogenic fractions and virus/bacteria ratio (VBR). The results showed that soil moisture significantly affected bacterial and viral counts, lysogenic fractions and VBR (p < 0.01), with bacterial counts increasing and viral counts decreasing with increasing soil moisture. The lysogenic fraction peaked at low moisture, suggesting a shift in viral strategy under hydration stress, which may affect virus‐bacteria interactions and nutrient dynamics, enhancing host adaptability. Analyses using correlation, random forest and structural equation modelling identified soil moisture as the dominant factor shaping VRS by altering nutrient availability and host population. These findings provide a new insight into microbial regulation of feedback to environmental change from the life history strategies of soil viruses. [ABSTRACT FROM AUTHOR]

In this paper, we will conclude the results of Bufeng-1 (BF-1) A/B data processing, calibration workflow, and validation of the calibrated sea surface winds, land surface soil moisture, and sea surface height measurements. Since 2019, the BF-1 mission has operated in-orbit for over 4 years. The Earth reflected delay Doppler maps (DDMs) are continuously collected to perform global sea surface and land observations. At the same time, the intermediate frequency (IF) raw data are also obtained for 12 seconds every pass in diagnostic mode. To begin with, a brief description of the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technique will be provided in the introduction. Next, we will present the overview of Chinese BF-1 mission and the data specifications used in our research. In the next section, the BF-1 mission-related spaceborne power calibration and validation are presented to show the support to power DDM observable production for sea surface and land surface applications. Then, the status of Chinese Beidou System (BDS) Equivalent Isotropic Radiated Power (EIRP) acquisition programme is then introduced. Furthermore, the latest sea surface height (SSH) measurements results including two modes (group delay and carrier phase) and wind speed derivation based on machine learning (ML) method will be spatial-temporal aligned and validated with auxiliary datasets including Denmark Technology University (DTU) mean sea surface (MSS) products and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. The previous published results of sea surface winds retrieval under Hurricane conditions and soil moisture retrieval are also reviewed for the BF-1 mission applications. Finally, the conclusion of BF-1 derived results will be discussed to draw out ongoing/future works. [ABSTRACT FROM AUTHOR]

GNSS Reflectometry (GNSS-R) is a currently developed remote sensing technology which belongs to the passive radar domain. This paper aims to deal with some issues on multi-polarization of GNSS-R technology. Four different polarization patterns of the received GNSS-R signal are discussed, including rl, rr, rv, rh. For each polarization, formulas for calculating the surface reflectivity (Γ) using dielectric constant (ε) and satellite elevation angle (θ) are derivated. The rationality of these formulas is validated using data from a ground-based GNSS-R soil moisture experiment. The results of this research can provide references for further GNSS-R research, including simulation, experiment design, model development and data processing.

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 feasibility of using China’s Beidou Navigation Satellite System (BDS) signal-to-noise ratio (SNR) data for estimating soil moisture is demonstrated in this letter. Previous studies for the Global Navigation Satellite System (GNSS) multipath for estimating soil moisture have concentrated on the Global Positioning System (GPS) SNR data. The SNR data recorded by BDS receivers can also be strongly impacted by the relative permittivity, which however, has not been comprehensively investigated. This letter used a commercially available geodetic-quality BDS/GPS receiver to collect BDS SNR data on both B1 and B2 frequencies. Two methods were investigated to demonstrate the relationship between BDS SNR metrics and soil moisture. One was the phase method proposed by Larson et al., and the other was an interference model proposed by Peng et al. The results show that both B1 and B2 frequencies perform well to reflect the moisture fluctuations, particularly for reflecting big precipitation events. Comparisons between BDS B1 and B2 with GPS L2C and L5 demonstrate the results from these two constellations are comparable. BDS could be a new data source for producing global high-temporal soil moisture products using GNSS-based approaches. [ABSTRACT FROM PUBLISHER]

• Distribution of maize planting areas were retrieved in a large spatiotemporal scale. • Spatiotemporal patterns of drought stress in the major maize planting areas of China were evaluated. • NPP which characterize maize biomass can determine the thresholds of maize drought stress. • Conceptual model of maize drought stress based on the TVDI and NPP was established for the first time. Droughts are characterized by limited water resources and extreme arid climates and have a notable influence on crop yield and food security. Therefore, evaluating the crop drought stress at a large spatiotemporal scale is important. In this study, maize planting areas in the Northeast China Plain (NECP) and North China Plain (NCP) over the past 20 years (2000–2019) were identified from the moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index, comprising 8-day time-series moving windows. The temperature vegetation drought index (TVDI) was retrieved using the MODIS normalized difference vegetation index (NDVI) and land surface temperature datasets to evaluate maize drought. Furthermore, by combining MODIS NDVI with measured meteorological data, monthly net primary productivity (NPP) was retrieved based on the Carnegie–Ames–Stanford approach (CASA) to characterize the maize biomass. Thus, the weight of drought stress in different growth stages of maize was obtained by combining the maize phenological information with TVDI and NPP. Moreover, TVDI drought stress was obtained to comprehensively assess maize drought stress. Finally, we fitted TVDI drought stress with NPP to establish a conceptual model for the response of maize biomass to drought stress in the NCP and NECP at a large spatiotemporal scale for the first time. The conceptual model revealed that the phenomenon of excessive moisture during maize growth was not evident (TVDI drought stress < 0.375) and that most maize plants were still affected by drought stress (TVDI drought stress > 0.375) in the NECP. Correspondingly, in the NCP, there was hardly any excessive moisture, and maize exhibited a certain resistance to low-intensity drought stress (TVDI drought stress was approximately 0.330). However, after the drought stress exceeded the tolerance range (TVDI drought stress > 0.375), the maize biomass was significantly affected by drought stress. Our findings illustrate the two response models of drought stress in the major maize producing areas of China for the first time, thereby providing new insights for preventing drought stress risk and improving maize yield on a large scale. [ABSTRACT FROM AUTHOR]

Remote sensing technology can cost-effectively access to a wide range of real-time land surface spatial information, therefore it is widely used in agricultural crops monitoring. And timely assessment of drought stress using remote sensing is valuable for improving the crop management level. In this study, the drought stress was evaluated in the maize planting areas of the Northeast and North China Plain using satellite remote sensing technology. The absolute distance index was used to identify the spatial pattern of the maize planting areas using the moderate resolution imaging spectroradiometer (MODIS) derived enhanced vegetation index data that processed with Savitzky–Golay filter. The temperature vegetation drought index (TVDI) was derived from MODIS normalized difference vegetation index and land surface temperature data. Simultaneously, soil moisture content (SMC) was obtained for an eight-day period and matched with satellite remote sensing data to characterize the drought stress of maize. After estimating the field capacity and wilting point, the SMC was further transformed into the real available water content (R AWC) of the soil. Our results indicated that negative correlations between R AWC and TVDI was observed for the 0–10 cm (R 2 = 0.594) and 20–30 cm (R 2 = 0.641) soil layers, respectively, which was significantly better than the correlation between the SMC and TVDI in the 0–10 (R 2 = 0.396) and 20–30 cm (R 2 = 0.499) soil layers. Moreover, the sensitive period regarding the maize water demand were identified (day of year 161, 169, 233, 241, and 249) via the linear regression analysis of the measured maize yield and TVDI for eight-day intervals. Furthermore, the weights of the water demand of maize in different growth stages was determined via multiple linear regression analysis, and a comprehensive drought index (TVDI SW) was established to evaluate the drought stress in the whole maize growth period. Eventually, a comprehensive analysis for the drought stress in the study area was conducted combined with various environmental factors. Furthermore, the performance of this method was independent of the meteorological and field survey data, which may facilitate the mapping of drought monitoring of crops planting areas at a large scale. • Drought stress in the maize planting area was evaluated. • The phenological period of maize growth was divided into four stages. • The soil moisture content was converted to the real available water content. • Crop drought stress and real available water content bared better correlation. • The moisture-sensitive period of maize was revealed. [ABSTRACT FROM AUTHOR]

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a new opportunity for land observation. This study is the first to compare and evaluate the performance of the only two spaceborne GNSS-R satellite missions whose data are publicly available, i.e., the UK's TechdemoSat-1 (TDS-1) and the US's Cyclone Global Navigation Satellite System (CYGNSS), for sensitivity analysis with SMAP SM on a daily basis and soil moisture (SM) estimates on a monthly basis over Mainland China. For daily sensitivity analysis, the two data were matched up and compared for the period (i.e., May 2017 through April 2018) when they coexisted (R = 0.561 vs. R = 0.613). For monthly SM estimates, a back-propagation artificial neural network (BP-ANN) was used to construct a model using data from more than two years. The model was subsequently used to derive long-term and continuous SM maps over Mainland China. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China (R = 0.676, MAE = 0.052 m3m−3, and ubRMSE = 0.060 m3m−3 for TDS-1; R = 0.798, MAE = 0.040 m3m−3, and ubRMSE = 0.062 m3m−3 for CYGNSS). The retrieved results were further validated using monthly in situ SM data from dense sites across Mainland China. It was found that the SM derived from the TDS-1/CYGNSS also correlated well with in situ SM (R = 0.687, MAE = 0.066 m3m−3, and ubRMSE = 0.056 m3m−3 for TDS-1; R = 0.724, MAE = 0.052 m3m−3, and ubRMSE = 0.053 m3m−3 for CYGNSS). The results in this study suggested that TDS-1/CYGNSS and the upcoming spaceborne GNSS-R mission could be new and powerful data sources to produce SM data set at a large scale and with relatively high precision. [ABSTRACT FROM AUTHOR]

Surface soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, and critical to meteorology, hydrology, and ecology. The Tibetan Plateau (TP), known as "The third pole of the world" and "Asia's water towers", exerts huge influences on and sensitive to global climates. In this situation, longer time series of soil moisture can provide sufficient information to understand the role of the TP. This paper presents the first comprehensive dataset (2002–2015) of spatio-temporal continuous soil moisture at 0.25° resolution, based on satellite-based optical (i.e. MODIS) and microwave (ECV) products using a machine learning method named general regression neural network (GRNN). The dataset itself reveals significant information on the soil moisture and its changes over the TP, and can aid to understand the potential driven mechanisms for climate change over the TP. Measurement(s) wetness of soil Technology Type(s) computational modeling technique Factor Type(s) land surface temperature • normalized difference vegetation index • albedo • digital elevation model Sample Characteristic - Environment soil Sample Characteristic - Location Tibetan Plateau Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9925067 [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the Earth’s ‘third pole’. Large-scale spatially consistent and temporally continuous soil moisture datasets are of great importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is a relatively new passive microwave product, with the satellite being launched on November 5, 2010. This study validates and reconstructs FY-3B/MWRI soil moisture across the TP. First, the validation is performed using in situ measurements within two in situ soil moisture measurement networks (1° × 1° and 0.25° × 0.25°), and also compared with the Essential Climate Variable (ECV) soil moisture product from multiple active and passive satellite soil moisture products using new merging procedures. Results show that the ascending FY-3B/MWRI product outperforms the descending product. The ascending FY-3B/MWRI product has almost the same correlation as the ECV product with the in situ measurements. The ascending FY-3B/MWRI product has better performance than the ECV product in the frozen season and under the lower NDVI condition. When the NDVI is higher in the unfrozen season, uncertainty in the ascending FY-3B/MWRI product increases with increasing NDVI, but it could still capture the variability in soil moisture. Second, the FY-3B/MWRI soil moisture product is subsequently reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and NDVI, LST, and albedo, but also the relationship between the soil moisture and four-dimensional variations using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R 2 higher than 0.56, RMSE less than 0.1 cm 3 cm −3 , and Bias less than 0.07 cm 3 cm −3 for both frozen and unfrozen seasons, compared with the in situ measurements at the two networks. Third, the reconstruction method is applied to generate surface soil moisture over the TP. Both original and reconstructed FY-3B/MWRI soil moisture products could be valuable in studying meteorology, hydrology, and ecosystems over the TP. [ABSTRACT FROM AUTHOR]