SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.

Soil lead content is an important parameter in environmental and industrial applications. Chemical analysis, the most commonly method for studying soil samples, are costly, however application of soil spectroscopy presents a more viable alternative. The first step in the method is usually to extract some appropriate spectral features and then regression models are applied to these extracted features. The aim of this paper was to design an accurate and robust regression technique to estimate soil lead contents from laboratory observed spectra. Three appropriate spectral features were selected according to information from other research as well as the spectrum interpretation of field collected soil samples containing lead. These features were then applied to common Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR) and Neural Network (NN) regression models. Results showed that although NN had adequate accuracy, it produced unstable results (i.e., variation of response in different runs). This problem was addressed with application of a Fuzzy Neural Network (FNN) with a least square training strategy. In addition to the stabilized and unique response, the capability of the proposed FNN was proved in terms of regression accuracy where a Ratio of Performance to Deviation (RPD) of 8.76 was achieved for test samples.

Remote sensing is the most promising technique for providing crop maps, thanks to the development of satellite data at various temporal and spatial resolutions. Three-dimensional (3 D) convolutional neural networks (CNNs) have the potential to provide rich features that represent the spatial and temporal patterns of crops when applied to time-series images. This study presents a novel 3 D-CNN framework for classifying crops that is based on the fusion of time-series radar and optical images and also fully exploits 3 D spatial-temporal information. To extract deep convolutional maps, the proposed technique uses one separate sequence for each time-series dataset. To determine the label of each pixel, these extracted feature maps are passed to the concatenating layer and subsequently transmitted to the sequentially fully connected layers. The weights and biases are fine-tuned driving features from samples of crops in the proposed strategy. The proposed approach not only takes advantage of CNNs, i.e.,, automatic feature extraction, but also discovers discriminative feature maps in both spatial and temporal dimensions and preserves the growth dynamics of crop cycles. The overall accuracy (OA) of 91.3% and the kappa coefficient (KC) of 89.9% confirm the proposed method's potential. It is also shown that the suggested method outperforms other methods.