Soil moisture (SM) is a key variable in ecology, environment, agriculture, and hydrology. The Soil Moisture Active Passive (SMAP) satellite provides global SM products with reliable accuracy since 2015. However, significant gaps of SMAP SM appeared over Tibetan Plateau. Considering the important role of the Tibetan Plateau in global climate and environment, it is essential to develop methods to infill the gaps to generate seamless SMAP SM data. To address this issue, we proposed two methods, machine learning and geostatistics technique. For the machine learning technique, we train a Random Forest algorithm which aims to match the output of available SMAP L3 SM using a series of input variables such as SMAP brightness temperature (TB _H and TB _V ) in ascending orbits (6:00 PM local time), surface temperature, MODIS NDVI, land cover, DEM, and other auxiliary data. Then, the established RF estimators were applied to the SMAP brightness temperature from descending orbits (6:00 AM local time) to reconstruct complete SM data over the Tibetan Plateau. For the geostatistics technique, the Ordinary kriging was applied to the available SMAP L3 SM pixels to interpolate complete SM data. To cross-validate the performances of the algorithms, we assume certain areas with available SMAP SM values as missing, and then compared the gap-filling results with the actual ones. The cross-validations show that the gap-filling results from two algorithms were highly correlated to the official SMAP SM products with high coefficients of determination ( R ² _RF = 0.97 and R ² _OK = 0.85) and low RMSE (RMSE _RF = 0.015 cm ³ /cm ³ and RMSE _OK = 0.036 cm ³ /cm ³ ). Furthermore, the gap-filling SM data present a better correlation with the Soil Moisture and Ocean Salinity SM data ( R = 0.55–0.7) than the Global Land Data Assimilation System simulations ( R = 0.18–0.62). The reconstructed SM from RF ( R = 0.71) and OK ( R = 0.55) algorithms are well related to the Maqu network measurements. Thus, the machine learning and geostatistics algorithms have the potential to reproduce the missing SMAP SM products over the Tibetan Plateau.

This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil and vegetation characteristics and the time series of UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected in the framework of SMAP (Soil Moisture Active Passive) Validation Experiment 2012 (SMAPVEX12). The retrieval process is tested and analyzed in detail for a variety of crops during the phenological stages considered in this study. The results show that the performance of soil moisture retrieval depends on both the crop types and the crop phenological stage. Soybean and pasture fields present the higher inversion rate during the considered phenological stage, while over canola and wheat fields, the soil moisture can be retrieved only partially during the crop developing stage. RMSE of 0.06–0.12 m³/m³ and an inversion rate of 26%–38% are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition.

This article compares the ability of nine unmixing models, including radiative transfer (RT) models as well as a new nonlinear unmixing approach called neighbor-band ratio unmixing (NBRU), to obtain mineralogical information from hyperspectral data. Their performance in estimating mineral abundances of 94 crafted mineral mixtures was first assessed. NBRU led to the best results among non-RT models with mean and median errors of 9.8% and 7.4%, respectively. Hapke's and Shkuratov's RT models obtained 6.5% and 5.6%, and 6.7% and 4.7%, respectively. In a second experiment, the mapping ability of six non-RT models and their robustness when facing endmember variability were evaluated. The assessment was performed on an AVIRIS hyperspectral image of the widely studied Cuprite area, NV, USA. Comparisons with validation maps showed that NBRU retrieved the best spatial distributions for seven of the nine minerals mapped.

Carbon stocks in forest ecosystems, when released as a result of forest degradation, contribute to greenhouse gas (GHG) emissions. To quantify and assess the rates of these changes, the Intergovernmental Panel on Climate Change (IPCC) recommends that the REDD+ mechanism use a combination of Earth observational data and field inventories. To this end, our study characterized land-cover changes and forest-cover dynamics in Togo between 1985 and 2020, using the supervised classification of Landsat 5, 7, and 8 images on the Google Earth Engine platform with the Random Forest (RF) algorithm. Overall image classification accuracies for all target years ranged from 0.91 to 0.98, with Kappa coefficients ranging between 0.86 and 0.96. Analysis indicated that all land cover classes, which were identified at the beginning of the study period, have undergone changes at several levels, with a reduction in forest area from 49.9% of the national territory in 1985, to 23.8% in 2020. These losses of forest cover have mainly been to agriculture, savannahs, and urbanization. The annual change in forest cover was estimated at −2.11% per year, with annual deforestation at 422.15 km² per year, which corresponds to a contraction in forest cover of 0.74% per year over the 35-year period being considered. Ecological Zone IV (mountainous, with dense semi-deciduous forests) is the one region (of five) that has best conserved its forest area over this period. This study contributes to the mission of forestry and territorial administration in Togo by providing methods and historical data regarding land cover that would help to control the factors involved in forest area reductions, reinforcing the system of measurement, notification, and verification within the REDD+ framework, and ensuring better, long-lasting management of forest ecosystems.

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.