This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well as a statistical Ordinary Least Squares (OLS) model were established, with the auxiliary data including the 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire data were divided into two parts corresponding to ascending (6:00 p.m. local time) and descending (6:00 a.m. local time) orbits of SMAP overpasses. Thus, the three models were trained using the descending data acquired during the five years (2015 to 2019), and validated using the ascending product of the same period. Consequently, three different temporal variations of the soil moisture were obtained based on the three models. To evaluate their accuracies, the retrieved soil moisture was compared against the SMAP level-2 soil moisture product, as well as to in-situ ground station data. The comparative results show that the soil moisture obtained using the OLS, RF and SVM algorithms are highly correlated to the SMAP level-2 product, with high coefficients of determination (R²_OLS = 0.981, R²_SVM = 0.943, R²_RF = 0.983) and low RMSE (RMSE_OLS = 0.016 cm³/cm³, RMSE_SVM = 0.047 cm³/cm³, RMSE_RF = 0.016 cm³/cm³). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R²_OLS = 0.376~0.85, R²_SVM = 0.376~0.814, R²_RF = 0.39~0.854; RMSE_OLS = 0.049~0.105 cm³/cm³, RMSE_SVM = 0.073~0.1 cm³/cm³, RMSE_RF = 0.047~0.102 cm³/cm³), but an overestimation issue is observed for high vegetation conditions. The RF algorithm outperformed the SVM and OLS, in terms of the agreement with the ground measurements. This study suggests an alternative soil moisture retrieval scheme, in complementary to the SMAP baseline algorithm, for a fast soil moisture retrieval.

This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and α of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R² and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m³/m³, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau.

The European Space Agency (ESA) Climate Change Initiative (CCI) project combines multi-sensors at different microwave frequencies to derive three harmonized soil moisture products using active, passive and combined approaches. These long-term soil moisture products assist in understanding the global water and carbon cycles. However, extensive validations are a prerequisite before applying the retrieved soil moisture into climatic or hydrological models. To fulfill this objective, we assess the performances of three CCI soil moisture products (active, passive and combined) with respect to in-situ soil moisture networks located in China, Spain and Canada. In order to compensate the scale differences between ground stations and the CCI product’s coarse resolution, we adopted two upscaling approaches of Inverse Distance Weighting (IDW) interpolation and simple Arithmetic Mean (AM). The temporal agreements between the satellite retrieved and ground-measured soil moisture were quantified using the unbiased root mean square error (ubRMSE), RMSE, correlation coefficients (R) and bias. Furthermore, the temporal variability of the CCI soil moisture is interpreted and verified with respect to the Tropical Rainfall Measuring Mission (TRMM) precipitation observations. The results show that the temporal variations of CCI soil moisture agreed with the in-situ ground measurements and the precipitation observations over the China and Spain test sites. In contrast, a significant overestimation was observed over the Canada test sites, which may be due to the strong heterogeneity in soil and vegetation characteristics in accordance with the reported poor performance of soil moisture retrieval there. However, despite a retrieval bias, the relatively temporal variation of the CCI soil moisture also followed the ground measurements. For all the three test sites, the soil moisture retrieved from the combined approach outperformed the active-only and passive-only methods, with ubRMSE of 0.034, 0.050, and 0.050–0.054 m³/m³ over the test sites in China, Spain and Canada, respectively. Thus, the CCI combined soil moisture product is suggested to drive the climatic and hydrological studies.

Monitoring and mapping agricultural cultural ecosystem services (CES) is essential, especially in areas with a sharp contradiction between agricultural land protection and urban development. Despite research assessing CES increasing exponentially in recent years, our knowledge of the CES of agricultural landscapes is still inadequate. This study used four types of agricultural landscapes in Hangzhou, China, as the study area, analyzed their CES spatial patterns, and explored their societal preferences by integrating the multi-sourced datasets, clustering algorithms, and Maxent model. The results indicated that hot spots of agricultural CES correspond to river valley plains, which were also easily vulnerable to urbanization. Moreover, we found that the CES level of paddy field and dry farmland were higher than tea garden and orchard. Based on the above spatial patterns of supply, demand, and flow of CES, we identified four groups of agricultural land by cluster analysis, distinguishing between significant, unimportant, little used, and potential CES. Further, our results showed that natural and human factors could explain societal preferences. This study can provide a valuable basis for stakeholders to develop balanced strategies by the aforementioned results.

High-spatiotemporal resolution soil moisture (SM) plays an essential role in optimized irrigation, agricultural droughts, and hydrometeorological model simulations. However, producing high-spatiotemporal seamless soil moisture products is challenging due to the inability of optical bands to penetrate clouds and the coarse spatiotemporal resolution of microwave and reanalysis products. To address these issues, this study proposed a framework for multi-source data merging based on the triple collocation (TC) method with an explicit physical mechanism, which was dedicated to generating seamless 1 km daily soil moisture products. Current merging techniques based on the TC method often lack seamless daily optical data input. To remedy this deficiency, our study performed a spatiotemporal reconstruction on MODIS LST and NDVI, and retrieved seamless daily optical soil moisture products. Then, the optical-derived sm₁, microwave-retrieved sm₂ (ESA CCI combined), and reanalysis sm₃ (CLDAS) were matched by the cumulative distribution function (CDF) method to eliminate bias, and their weights were determined by the TC method. Finally, the least squares algorithm and the significance judgment were adopted to complete the merging. Although the CLDAS soil moisture presented anomalies over several stations, our proposed method can detect and reduce this impact by minimizing its weight, which shows the robustness of the method. This framework was implemented in the Naqu region, and the results showed that the merged products captured the temporal variability of the SM and depicted spatial information in detail; the validation with the in situ measurement obtained an average ubRMSE of 0.046 m³/m³. Additionally, this framework is transferrable to any area with measured sites for better agricultural and hydrological applications.

Integrating satellite observations into hydrologic models has the potential to enhance soil moisture (SM) retrievals at high spatial and temporal resolutions. The hydrological model SWAT can produce spatiotemporally continuous soil moisture product with high resolution. This study proposed to physically couple the SWAT and Tau-omega radiative transfer model to retrieve SM with a spatial resolution of 30 m. First, the SWAT model integrated a physically-based SM module was used to simulate hourly SM, from which the brightness temperature ( TB ) at 30 m was obtained by forwardly simulating the Tau-omega model. Then, the simulated TB at fine resolution and SMAP TB at coarse resolution were merged by Kalman filter into a disaggregated TB. Finally, SM at 30m resolution was retrieved from the disaggregated TB . The proposed methods were evaluated over the Lanjiang River Basin. The results show that the retrieved SM is highly correlated with two official SMAP SM products and that it has a better accuracy than five SMAP SM products. Particularly, due to the high resolution, the retrieved product captured the heterogeneity of SM within and among fields, compared to the individual use of the hydrological model or satellite observation. Our proposed approach can aid the decision-making in agricultural management at the field scale.