Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R²) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R² = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R² = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.

Accurate and updated finer resolution maps of rubber plantations and stand ages are needed to understand and assess the impacts of rubber plantations on regional ecosystem processes. This study presented a simple method for mapping rubber plantation areas and their stand ages by integration of PALSAR 50-m mosaic images and multi-temporal Landsat TM/ETM+ images. The L-band PALSAR 50-m mosaic images were used to map forests (including both natural forests and rubber trees) and non-forests. For those PALSAR-based forest pixels, we analyzed the multi-temporal Landsat TM/ETM+ images from 2000 to 2009. We first studied phenological signatures of deciduous rubber plantations (defoliation and foliation) and natural forests through analysis of surface reflectance, Normal Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) and generated a map of rubber plantations in 2009. We then analyzed phenological signatures of rubber plantations with different stand ages and generated a map, in 2009, of rubber plantation stand ages (≤5, 6–10, >10 years-old) based on multi-temporal Landsat images. The resultant maps clearly illustrated how rubber plantations have expanded into the mountains in the study area over the years. The results in this study demonstrate the potential of integrating microwave (e.g., PALSAR) and optical remote sensing in the characterization of rubber plantations and their expansion over time.

Oil palm plantations have expanded rapidly. Estimating either positive effects on the economy, or negative effects on the environment, requires accurate maps. In this paper, three classification algorithms (Support Vector Machine (SVM), Decision Tree and K-Means) were explored to map oil palm plantations in Cameroon, using PALSAR 50 m Orthorectified Mosaic images and differently sized training samples. SVM had the ideal performance with overall accuracy ranging from 86% to 92% and a Kappa coefficient from 0.76 to 0.85, depending upon the training sample size (ranging from 20 to 500 pixels per class). The advantage of SVM was more obvious when the training sample size was smaller. K-Means required the user’s intervention, and thus, the accuracy depended on the level of his/her expertise and experience. For large-scale mapping of oil palm plantations, the Decision Tree algorithm outperformed both SVM and K-Means in terms of speed and performance. In addition, the decision threshold values of Decision Tree for a large training sample size agrees with the results from previous studies, which implies the possible universality of the decision threshold. If it can be verified, the Decision Tree algorithm will be an easy and robust methodology for mapping oil palm plantations.

There is a pressing need for an automatic road extraction method due to the continuous development of transportation networks. Free from the influence of weather, satellite-mounted synthetic-aperture radar (SAR) opens the way for such a road detection application. This study introduces an automatic discrimination method based on a deep neural network (DNN) adjusted for roads from dual-polarimetric (VV and VH) Sentinel-1 SAR imagery. In this proposed method, the U-Net extended the convolutional neural network (CNN), is adjusted for road extraction from SAR images. To investigate the potential of using the U-Net based fully convolutional neural network (FCN) for road extraction, LeNet-5, is applied as a preliminary DNN model. Additionally, several training optimizations are introduced to improve accuracy. To assess the performance of the different polarization modes used in road extraction, both single-polarimetric and dual-polarimetric data were investigated. Moreover, four machine learning algorithms have been compared for accuracy and speed. As a result, the outcome evaluation of Precision, Recall, and F₁ obtained by FCN is better than the original CNN, and the training time has been significantly reduced. The DNN model (CNN and FCN) is superior to machine learning methods in accuracy and elapsed computation time.

Soil salinity is a significant environmental indicator, directly reflecting the land quality and its productivity. In this work, dual-polarized synthetic aperture radar (PolSAR) data were exploited to retrieve the soil salinity in saline-affected fields based on the deep neural network (DNN) theory. Two polarimetric features gotten from Cloude polarization decomposition, together with six other features, were calculated and selected from ten features according to the feature importance. The DNN regression model for soil salinity (DNNR-S) based on one-dimensional convolutional neural network, quantifying the relationship between the eight feature parameters and soil salinity, was established to retrieve the soil salinity in northeastern China. Compared with other five traditional machine learning regression methods [logistic regression (LOR), random forest regression (RFR), support vector regression (SVR), decision tree regression (DTR), and multilayer perceptron regression (MPR)], DNNR-S is the best-performing model with the performance of root mean square error (RMSE) = 0.28, mean absolute error (MAE) = 0.21, and $r = 0.74$ . Using the proposed model, the soil salinity was retrieved, and 52.37% was found to be affected by salinization which was in general agreement with the government report. Due to the training criteria in the proposed model, it was a little more computationally intensive than all the others, but the DNNR-S had the advantage of easy spread to other study areas.