Search Results (226)

This review comprehensively summarizes the recent research progress of multi-source sensing technologies and intelligent modeling methods in vegetation monitoring. It elucidates the physical mechanisms and complementary natures of core active and passive technologies, including optical hyperspectral remote sensing, LiDAR structural detection, and microwave/millimeter-wave radar. Furthermore, it systematically analyzes advanced data processing methods, covering physics-based radiative transfer models, data-driven machine learning, Physics-Data Dual-Driven Modeling transfer learning, and multi-source data fusion strategies. The paper highlights successful applications across diverse typical scenarios, such as crop precision nitrogen diagnosis, forest biomass estimation, crop lodging risk prediction, and vegetation stress assessment. Finally, it discusses critical challenges like data heterogeneity and model generalization, while presenting a future outlook focused on building collaborative “space-air-ground” integrated monitoring networks and advanced AI fusion methods. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

Mirrored aperture synthesis radiometry (MAS) has been proposed for passive microwave remote sensing with high spatial resolution and relatively few antenna elements. However, low incident angles and improper azimuth angles result in oversized reflectors, posing difficulties for practical deployment. To address this issue, the center of the imaging region is shifted from the incident angle of 0° to the oblique direction of (θ = 45°, ϕ = 45°) in this paper, which is quite different from conventional aperture synthesis radiometry architecture. The problem of oversized reflectors and the necessity of shifting the imaging region are discussed. To match the shifted imaging region, a new design scheme based on spatial bandpass sampling and quad-beam antennas for MAS imaging is proposed. Instead of the Nyquist baseband sampling adopted in conventional aperture synthesis, the spatial bandpass sampling is adopted to process the spatial bandpass signal caused by the imaging region shift. Then, antennas with quad-beam pattern are proposed and designed to receive the incident wave and three reflected waves. Numerical simulation experiments are conducted to demonstrate the feasibility of the proposed method. This work focuses on the conceptual design and principle validation, and does not involve hardware implementation or prototype fabrication. Full article

Snow density influences Arctic climate, ecosystems, and surface energy exchange, yet spatially continuous observations remain limited. This study constructed an ERA5-supervised snow-density dataset for 60–90° N by collocating Advanced Microwave Scanning Radiometer 2 (AMSR-2) Level-1R brightness temperatures with ECMWF Reanalysis v5 (ERA5) snow density, Soil Moisture Active Passive (SMAP) surface roughness, and auxiliary variables. Ten models were evaluated using 29 observation days spanning September 2022–February 2023 under a chronological training–validation–test split. Extra Trees achieved the best overall performance, with a root mean square error of 18.54 kg m⁻³ and an R² of 0.87, while the bidirectional gated recurrent unit (BiGRU) was the strongest deep-learning model. Feature-attribution and ablation analyses showed that microwave brightness temperatures contained predictive information, although geographic and auxiliary variables also contributed substantially. The evaluated models could reproduce ERA5-referenced Arctic snow-density patterns, but their performance partly reflected regional information. Moreover, ERA5 showed limited consistency with station-based Northern Hemisphere Snow Water Equivalent estimates. Consequently, the reported metrics quantify agreement with ERA5 rather than accuracy against independently observed snow density. Temporally coincident and spatially independent field validation remains necessary in the future. Full article

Microwave-based remote sensing possesses the capability to penetrate through atmospheric obstructions such as cloud layers and fog, making it extensively utilized for estimating parameters including soil water content, atmospheric moisture levels, and terrestrial surface temperatures. Extended temporal datasets serve as fundamental requirements for climatological investigations; however, individual satellite operational lifespans remain constrained and prove inadequate for establishing multi-decade temporal sequences. Consequently, conducting comparative analyses and implementing cross-calibration procedures across measurements obtained from distinct sensors exhibiting comparable operational features becomes imperative. The FengYun (FY)-3G spacecraft, deployed into orbit during April 2023, hosts China’s most recent orbiting microwave radiometric instrument, designated as the Microwave Radiation Imager–Rainfall Mission (MWRI-RM). The FY-3G satellite’s unique drifting equator crossing time orbit plays a critical role in the calibration behavior of the MWRI-RM instrument, representing a key novelty of this study. The reliability of its brightness temperature (TB) observations has attracted considerable attention. Within this investigation, we conduct comparative assessments of orbital TB observations acquired from FY-3G/MWRI-RM against corresponding measurements obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2) installed on the Global Change Observation Mission–Water 1 (GCOM-W1) platform, and establish a straightforward linear inter-calibration methodology. Both sensing systems show strong consistency, with correlation coefficients exceeding 0.9 for all corresponding channels and systematic biases ranging from −1.40 K to −0.14 K. FY-3G/MWRI-RM generally reports lower TB values than GCOM-W1/AMSR2. The inter-sensor differences vary with frequency, land cover type, and TB range. Larger negative biases are mainly observed at 23.8 GHz and over water bodies, whereas the biases at 89 GHz are generally close to zero for most surface types. Latitude-dependent TB biases are most evident at 10.65 and 18.7 GHz, especially for vertical polarization at high latitudes, while orbit-dependent differences are more pronounced for vertically polarized low- and mid-frequency channels. After applying an inter-calibration procedure using AMSR2 as the reference, the agreement between FY-3G/MWRI-RM and GCOM-W1/AMSR2 is improved substantially, with mean biases below 0.25 K and RMSE values below 2 K for all channels. Validation using independent datasets further supports the stability of the calibration. The calibrated FY-3G/MWRI-RM TB data provide a basis for constructing long-term passive microwave brightness temperature records and for retrieving land and atmospheric parameters. Full article

Snow Water Equivalent (SWE) is a key variable for evaluating hydrological processes and the impacts of climate change in mountainous regions such as the Qilian Mountains. Passive microwave remote sensing provides large-scale SWE estimates, but its coarse spatial resolution and coverage gaps pose limitations, particularly in complex terrain with heterogeneous snow distribution. This study integrates multi-source data from 2018 to 2024, combining ground-based observations with multiple meteorological factors to develop a high-resolution SWE reconstruction model tailored to the Qilian Mountains. Eight machine learning algorithms—Support Vector Machine (SVM), CatBoost, LightGBM, XGBoost, Random Forest, AdaBoost, ElasticNet, and Bayesian Ridge Regression—were systematically compared, with LightGBM achieving the best performance on the test set. During feature selection, Granger causality inference was applied to screen input variables, resulting in an optimized reconstruction model with a mean absolute error (MAE) of only 1.984 mm, a root mean square error (RMSE) of 4.656 mm, and a coefficient of determination (R²) of 0.973. Model interpretability was enhanced using SHAP (Shapley Additive Explanations), which revealed that snow depth, surface soil temperature and moisture, and precipitation were the primary driving factors, with varying contributions to the model. The model generates SWE reconstruction sequences at 30 min intervals. This high-resolution dataset provides crucial support for studying snow dynamics in complex mountainous regions and contributes to improved water resource management and climate change assessments in the Qilian Mountains. Full article

Various global precipitation datasets have been used in precipitation-related fields such as hydrology, meteorology, climatology, and ecology to achieve different research objectives. Error analysis is an integral part before applying them to operational fields. However, the growing number of precipitation products and the absence of comprehensive error comparison research jointly impede users in distinguishing product-specific error patterns and constrain developers from enhancing precipitation estimation accuracy. To address this issue, we performed error analysis and comparison of thirteen global precipitation products—categorized as delayed time (DT), near real-time (NRT), and real-time (RT) types—across mainland China. Results revealed that GSMaP-Gauge (Gauge-adjusted Global Satellite Mapping of Precipitation) performed best in terms of detection indicators, while MGP (Multi-source merged global precipitation product) performed best in estimating precipitation accuracy. However, IMERG-Final (Integrated Multisatellite Retrievals for Global Precipitation Measurement Final Run) proved ineffective in reducing the overestimations of both storm and light precipitation events in regions of complex topography. Furthermore, two DT products (i.e., ERA5 (Fifth generation of ECMWF atmospheric reanalyses of the global climate) and MGP) overestimated the frequency of light precipitation events, with relative rainfall occurrence biases exceeding 80%. This bias is attributable to both false detections and the misclassification of high intensity rainfall as light precipitation. Although GSMaP-NOW (based exclusively on passive microwave data) detected precipitation more effectively than the infrared-only PDIRNow (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)—Dynamic Infrared Rain Rate (Now)), it achieved lower accuracy. This discrepancy reflects the tradeoff between the higher precipitation sensitivity of passive microwave observations and their sparse temporal sampling, compared with the continuous coverage provided by infrared data. Finally, our findings indicated that current evaluation approaches do not reliably determine the optimal precipitation product, since product superiority is contingent upon the selected error metric. This underscores the urgent need to develop theoretically grounded and operationally reliable methods for selecting optimal precipitation products to support data users in deriving robust and reliable conclusions in hydrology, meteorology, and ecology. Full article

As a key parameter of the Earth’s ecosystem, soil moisture significantly influences land-atmosphere interactions and has important applications in meteorology, hydrology, and agricultural studies. However, existing passive microwave remote sensing products of soil moisture are limited by their discontinuous temporal coverage and relatively coarse spatial resolution (typically 25–55 km), which cannot meet the requirements for fine-scale applications. This study developed and compared four machine learning-based downscaling approaches to improve the spatiotemporal resolution of MCCA SMAP soil moisture products. The methodology involved establishing complex nonlinear relationships between soil moisture and various high-resolution surface parameters including albedo, evapotranspiration, precipitation, and soil properties. High-resolution soil moisture maps were generated by leveraging the scale-invariant characteristics between soil moisture and surface parameters, followed by comprehensive evaluation using in situ ground observations and triple collocation analysis. The results demonstrated that all downscaling models showed excellent consistency with original MCCA SMAP observations (R > 0.93, RMSE < 0.033 m³ m⁻³), while successfully providing enhanced spatial details. The Random Forest (RF) model exhibited superior performance, showing higher correlation coefficients and lower biases when compared with in situ measurements. Uncertainty analysis revealed relatively low uncertainty levels for all models except Backpropagation Neural Network (BPNN) model. The RF-downscaled products accurately tracked temporal variations of soil moisture and showed good responsiveness to precipitation patterns, demonstrating their potential for fine-scale hydrological applications and regional environmental monitoring. Full article

Soil moisture is an essential climate variable exhibiting strong spatio-temporal dynamics, especially in the topsoil. Therefore, it is assessed multiple times by sensors within in situ networks, satellites, and by modeling of the Earth system. The resulting soil moisture fields from all methods are individual and non-congruent due to the imperfection of the methods and retrievals. But their spatial patterns have valuable similarities that call for investigation to foster intercomparison or even fusion of soil moisture products. In this research study, the similarity of spatial soil moisture patterns between passive microwave remote sensing products and Earth system modeling is investigated. We configure and apply spatial similarity metrics to enable a spatial comparison of the operational SMAP Dual Channel Algorithm (DCA) radiometer soil moisture product with the soil moisture output from IFS model runs of the ECMWF. The pattern assessment spans over the whole of Europe and aims to find the drivers behind the spatial soil moisture distributions at scales ranging from single grid cells (minimum) to continental (maximum) spatial scales, and between growing periods of wet (2021) and dry (2022) years. The two specifically configured metrics, total disagreement and mean category distance, showcase the opportunities and challenges when assessing spatial similarity in soil moisture fields across different scales. In addition, the potential drivers of the spatial moisture patterns were screened. Here, soil texture is the most influential single driver of spatial patterns in the IFS soil moisture runs, when analyzed in absolute terms [m³ m⁻³]. In relative terms of soil moisture [-] (soil wetness index), precipitation and soil temperature explain most of the variability of the IFS soil moisture for Europe. The SMAP retrievals are predominantly driven by the brightness temperatures, mostly influenced by surface temperature, vegetation water content, and soil roughness. These differences in drivers, as well as in methodology, culminate in an inherent discrepancy between the two soil moisture products. However, the assessment of their spatial patterns reveals the underlying similarity from the local to the continental scale. Full article

High-resolution snow depth monitoring is a crucial foundation for precise disaster early warning and optimal water resource management. Traditional snow depth estimation methods mainly rely on passive microwave remote sensing data, but due to their low spatial resolution, they have difficulties capturing the subtle changes in snow depth in complex terrain. Existing deep learning methods mostly adopt single-modal or simple band fusion, failing to fully utilize the complementarity among multi-source data and not considering that terrain factors can lead to misjudgment of the true snow signal. Therefore, this paper proposes a dual-branch intermediate fusion network (TACMF-Net) for high-latitude regions in Asia. By introducing terrain factors (DEM, slope, aspect) and conducting cross-modal feature interaction, it achieves efficient collaboration of multi-source remote sensing data. Research shows that our method has extremely high accuracy and robustness on the self-made multi-source snow depth terrain dataset. Full article

In passive microwave remote sensing, mirrored aperture synthesis (MAS) demonstrates significant potential for atmospheric observation from geostationary orbit. The amplitude and phase errors are among the key factors that degrade image reconstruction quality. The existing correction method requires additional mechanical structures to remove the reflector, thereby increasing system complexity. The method also requires that the external source used to extract error information be placed exactly at a specific location, which reduces the adaptability of the method and is difficult to achieve in practice. In this paper, an amplitude and phase error model based on the h function is established. Based on the error model, a new correction method for the amplitude and phase errors is proposed. The method uses the h function without errors as prior knowledge to extract error information. According to the extracted error information, the amplitude and phase errors are corrected. The proposed method does not require removing the reflector and is insensitive to the spatial offset of the h function. Simulation results show that the proposed method reduces the RMSE for an extended source from 162 K to 3.9 × 10⁻⁷ K . Experimental validation with a ceramic plate scene (extended source) further confirms its effectiveness, where the SSIM improves from –0.23 to 0.96 after correction, even under offset conditions. These results demonstrate the effectiveness and robustness of the proposed method. Full article

The spatial resolution of current microwave remote sensing soil moisture (SM) data is about 25 km in global scale. The coarse scale hinders the application of SM product at regional scale. The global 9 km SM can be released by radar observations of Soil moisture Active and Passive (SMAP) satellite since 2015. For the failed radar sensor, SMAP 9 km SM is less than three months. Therefore, European Space Agency Climate Change Initiative (CCI) SM data is downscaled to 9 km using spatial temporal fusion model in the study. And the 43-year 9 km SM is downscaled by CCI data from 1978 to 2020. Results display that downscaled 9 km SM gets more detailed spatial information than CCI data. Moreover, temporal variation of CCI data in anomaly can be well captured by downscaled data. The evaluations against in-situ data indicate that temporal accuracies of downscaled data (r = 0.676, μbRMSE = 0.069 m³/m³) are comparable with CCI data (r = 0.670, μbRMSE = 0.070 m³/m³). Overall, downscaled data improves the spatial resolution of CCI data and inherits the temporal accuracy with slight improvement. Higher spatial resolution SM offers greater application potential. Additionally, the model herein enriches SM downscaling techniques. Full article

Soil moisture (SM) is a key variable regulating land–atmosphere energy exchange, hydrological processes, and ecosystem functioning. Though important, there are still unresolved problems in accurate SM monitoring and the practical application and validation of existing methods. In this review, we integrate mechanistic classification and applicability and constraint discussions to develop a coherent understanding of current SM monitoring approaches. Within this framework, in situ measurements, optical and thermal infrared methods, active and passive microwave remote sensing (RS) techniques, and model-based simulations are compared, and publicly accessible SM dataset products are comparatively analyzed in terms of product characteristics and application limitations. Different from other published reviews, this study covers a large scope of SM monitoring methods varying from in situ observation to RS inversion, and classifies them based on their mechanisms, thereby constructing a complete comparative framework for SM research. Moreover, three types of open-access SM dataset products are investigated, optical and microwave RS products, model simulation and data fusion products, and reanalysis dataset products, and evaluated according to their resolution, depth, applicability, advantages, and limitations. By doing so, it is concluded that in situ observations remain essential for calibration and validation but are spatially limited. Optical and thermal infrared methods are restricted by atmospheric conditions and a shallow penetration depth, while microwave techniques exhibit varying performances under different vegetation and soil conditions. Existing datasets differ significantly in resolution, consistency, and coverage, making no single product universally applicable. Future research should focus on multi-source and spatiotemporal data fusions, the integration of machine learning with physical mechanisms, enhancement for cross-sensor consistency, the establishment of standardized uncertainty evaluation frameworks, and the refinement of high-order RTMs and parameterization. Full article

The occurrence and duration of droughts have increased in recent years, reinforcing their role as a major climate risk. This study evaluates a remote sensing soil moisture-based drought index, the Standardized Soil Moisture Index (SSI), as a tool to monitor different types of drought, from meteorological, agricultural to hydrological. The satellite-derived SSI at different integration times (from SSI-1 up to SSI-24) was compared with the Standardized Precipitation Index (SPI), calculated using precipitation data from 239 meteorological stations in the Ebro Basin. A good correlation ($R>0.6$) was found between the indices at all integration times. Our results suggest that, independently of the time scale, SSI tends to relate better to the SPI with an additional month for its integration time, reflecting soil moisture’s inertia. Comparison with a gridded SPI product further confirmed that SSI captures basin-wide drought variability, also suggesting that it can observe hydrological processes such as snowmelt and irrigation. These findings demonstrate that remote-sensed SSI is a robust and versatile drought index, capable of monitoring multiple drought types without relying on in situ measurements. Provided the existence of quality soil moisture data, satellite-derived SSI stands as a drought indicator with high coverage and enhanced spatial detail. Hence, this methodology paves the way for accurate drought monitoring in data-scarce regions. Full article

Passive microwave remote sensing of soil moisture is crucial for monitoring the Earth’s water cycle and surface dynamics. The penetration depth during this process is significant, as it influences the accuracy of retrieved soil moisture data. Within L-band remote sensing, tools such as the τ-z model interpret microwave emissions to estimate soil moisture, taking into account the complex interactions between soil and radiation. However, in validating these models against high-temporal-resolution, ground-based measurements, especially from extensive networks like the Terrestrial National Ecological Observatory Network (NEON), further research and validation efforts are needed. This study comprehensively validates the τ-z model’s ability to estimate the soil temperature sensing depth (z_Teff) using data from the NEON and Soil Moisture Active Passive (SMAP) satellite missions. A harmonization process was conducted to align the spatial and temporal scales of the two datasets, enabling rigorous validation. We compared soil optical depth (τ)—a parameter capable of theoretically unifying sensing depth representations across wet soil (~0.05 m) to extreme dry/frozen conditions (e.g., up to ~1500 m in ice-equivalent scenarios)—and geometric depth (z) frameworks against outputs from the τ-z model and NEON’s in situ profiles. The results show that: (1) for the profiles that satisfy the monotonic assumption by the τ-z model, z_Teff fits the prediction well at about 0.2 τ for the average; (2) Combining SMAP’s soil moisture, the τ-z model achieves high accuracy in estimating z_Teff, with RMSD (0.05 m) and unRMSD (0.03 m), and correlations (0.67) between estimated and observed values. The findings are expected to advance remote sensing techniques in various fields, including agriculture, hydrology, and climate change research. Full article