Soil moisture (SM) is an important biophysical parameter by which to evaluate water resource potential, especially for agricultural activities under the pressure of global warming. The recent advancements in different types of satellite imagery coupled with deep learning-based frameworks have opened the door for large-scale SM estimation. In this research, high spatial resolution Sentinel-1 (S1) backscatter data and high temporal resolution soil moisture active passive (SMAP) SM data were combined to create short-term SM predictions that can accommodate agricultural activities in the field scale. We created a deep learning model to forecast the daily SM values by using time series of climate and radar satellite data along with the soil type and topographic data. The model was trained with static and dynamic features that influence SM retrieval. Although the topography and soil texture data were taken as stationary, SMAP SM data and Sentinel-1 (S1) backscatter coefficients, including their ratios, and climate data were fed to the model as dynamic features. As a target data to train the model, we used in situ measurements acquired from the International Soil Moisture Network (ISMN). We employed a deep learning framework based on long short-term memory (LSTM) architecture with two hidden layers that have 32 unit sizes and a fully connected layer. The accuracy of the optimized LSTM model was found to be effective for SM prediction with the coefficient of determination ($R^2$) of 0.87, root mean square error (RMSE) of 0.046, unbiased root mean square error (ubRMSE) of 0.045, and mean absolute error (MAE) of 0.033. The model’s performance was also evaluated concerning above-ground biomass, land cover classes, soil texture variations, and climate classes. The model prediction ability was lower in areas with high normalized difference vegetation index (NDVI) values. Moreover, the model can better predict in dry climate areas, such as arid and semi-arid climates, where precipitation is relatively low. The daily prediction of SM values based on microwave remote sensing data and geophysical features was successfully achieved by using an LSTM framework to assist various studies, such as hydrology and agriculture.

Precision agriculture aims to optimize field management to increase agronomic yield, reduce environmental impact, and potentially foster soil carbon sequestration. In 2015, the Copernicus mission, with Sentinel-1 and -2, opened a new era by providing freely available high spatial and temporal resolution satellite data. Since then, many studies have been conducted to understand, monitor and improve agricultural systems. This paper presents results from the SolumScire project, focusing on the prediction of the spatial distribution of soil zones and topsoil properties, such as pH, soil organic matter (SOM) and clay content in agricultural fields through random forest algorithms. For this purpose, samples from 120 fields were investigated. The zoning and soil property prediction has an accuracy greater than 90%. This is supported by a high agreement of the derived zones with farmer’s observations. The trained models revealed a prediction accuracy of 94%, 89% and 96% for pH, SOM and clay content, respectively. The obtained models for soil properties can support precision field management, the improvement of soil sampling and fertilization strategies, and eventually the management of soil properties such as SOM.

Rice crops are important in the global food economy, and new techniques are being implemented for their effective management. These techniques rely mainly on the changes in the phenological cycle, which can be investigated by remote sensing systems. High frequency and high spatial resolution Synthetic Aperture Radar (SAR) sensors have great potential in all-weather conditions for detecting temporal phenological changes. This study focuses on a novel approach for growth stage determination of rice fields from SAR data using a parameter space search algorithm. The method employs an inversion scheme for a morphology-based electromagnetic backscattering model. Since such a morphology-based model is complicated and computationally expensive, a surrogate metamodel-based inversion algorithm is proposed for the growth stage estimation. The approach is designed to provide estimates of crop morphology and corresponding growth stage from a continuous growth scale. The accuracy of the proposed method is tested with ground measurements from Turkey and Spain using the images acquired by the TerraSAR-X (TSX) sensor during a full growth cycle of rice crops. The analysis shows good agreement for both datasets. The results of the proposed method emphasize the effectiveness of X-band PolSAR data for morphology-based growth stage determination of rice crops.

This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study’s findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers’ ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study’s approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions.

Rice crops are important in global food economy and are monitored by precise agricultural methods, in which crop morphology in high spatial resolution becomes the point of interest. Synthetic aperture radar (SAR) technology is being used for such agricultural purposes. Using polarimetric SAR (PolSAR) data, plant morphology dependent electromagnetic scattering models can be used to approximate the backscattering behaviors of the crops. However, the inversion of such models for the morphology estimation is complex, ill-posed, and computationally expensive. Here, a metamodel-based probabilistic inversion algorithm is proposed to invert the morphology-based scattering model for the crop biophysical parameter mainly focusing on the crop height estimation. The accuracy of the proposed approach is tested with ground measured biophysical parameters on rice fields in two different bands (X and C) and several channel combinations. Results show that in C-band the combination of the HH and VV channels has the highest overall accuracy through the crop growth cycle. Finally, the proposed metamodel-based probabilistic biophysical parameter retrieval algorithm allows estimation of rice crop height using PolSAR data with high accuracy and low computation cost. This research provides a new perspective on the use of PolSAR data in modern precise agriculture studies.