Abundant shallow underground brackish water resources could help in alleviating the shortage of fresh water resources and the crisis concerning agricultural water resources in the North China Plain. Improper brackish water irrigation will increase soil salinity and decrease the final yield due to salt stress affecting the crops. Therefore, it is urgent to develop a practical and low-cost method to monitor the soil salinity of brackish irrigation systems. Remotely sensed spectral vegetation indices (SVIs) of crops are promising proxies for indicating the salinity of the surface soil layer. However, there is still a challenge concerning quantitatively correlating SVIs with the salinity of deeper soil layers, in which crop roots are mainly distributed. In this study, a field experiment was conducted to investigate the relationship between SVIs and salinity measurements at four soil depths within six winter wheat plots irrigated using three salinity levels at the Yucheng Comprehensive Experimental Station of the Chinese Academy of Sciences during 2017–2019. The hyperspectral reflectance was measured during the grain-filling stage of winter wheat, since it is more sensitive to soil salinity during this period. The SVIs derived from the observed hyperspectral data of winter wheat were compared with the salinity at four soil depths. The results showed that the optimized SVIs, involving soil salt-sensitive blue, red-edge, and near-infrared wavebands, performed better when retrieving the soil salinity (R² ≥ 0.58, root mean square error (RMSE) ≤ 0.62 g/L), especially at the 30-cm depth (R² = 0.81, RMSE = 0.36 g/L). For practical applications, linear or quadratic models based on the screened SVIs in the form of normalized differential vegetation indices (NDVIs) could be used to retrieve soil salinity (R² ≥ 0.63, RMSE ≤ 0.62 g/L) at all soil depths and then diagnose salt stress in winter wheat. This could provide a practical technique for evaluating regional brackish water irrigation systems.

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.

Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes.

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%.

Since flood inundation hampers human life and the economy, flood inundation retrieval with high temporal resolution and accuracy is essential for the projection of the environmental impact. In this study, a novel cyclone global navigation satellite system (CYGNSS)-based index, named the annual threshold flood inundation index (ATFII) for flood inundation retrieval, is proposed, and the grades of flood inundation are quantified. First, the CYGNSS surface reflectivity with land surface properties (i.e., vegetation and surface roughness) calibration is derived based on the zeroth-order radiative transfer model. Then, an index named ATFII is proposed to achieve inundation retrieval, and the inundation grades are classified. The results are validated with the Visible Infrared Imaging Radiometer Suite (VIIRS) flood product and GPM precipitation data. The validation results between ATFII and GPM precipitation indicate that the ATFII enables flood inundation retrieval at rapid timescales and quantifies the inundation variation grades. Likewise, for monthly results, the R value between the VIIRS flood product and ATFII varies from 0.51 to 0.64, with an acceptable significance level (p < 0.05). The study makes contributions in two aspects: (1) it provides an index-based method for mapping daily flood inundation on a large scale, with the advantages of fast speed and convenience, and (2) it provides a new way to derive inundation grade variations, which can help in studying the behavior of inundation in response to environmental impacts directly.

The Cyclone Global Navigation Satellite System (CYGNSS), a publicly accessible spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data, provides a new alternative opportunity for large-scale soil moisture (SM) retrieval, but with interference from complex environmental conditions (i.e., vegetation cover and ground roughness). This study aims to develop a high-accuracy model for CYGNSS SM retrieval. The normalized surface reflectivity calculated by CYGNSS is fused with variables that are highly related to the SM obtained from optical/microwave remote sensing to solve the problem of the influence of complicated environmental conditions. The Gradient Boost Regression Tree (GBRT) model aided by land-type data is then used to construct a multi-variables SM retrieval model with six different land types of multiple models. The methodology is tested in southeastern China, and the results correlate very well with the existing satellite remote sensing products and in situ SM data (R = 0.765, ubRMSE = 0.054 m³m⁻³ vs. SMAP; R = 0.653, ubRMSE = 0.057 m³ m⁻³ vs. ERA5 SM; R = 0.691, ubRMSE = 0.057 m³m⁻³ vs. in situ SM). This study makes contributions from two aspects: (1) improves the accuracy of the CYGNSS retrieval of SM based on fusion with other auxiliary data; (2) constructs the SM retrieval model with multi-layer multiple models, which is suitable for different land properties.