8 This paper deals with the retrieval of agricultural crop height from space by using multipolarization This is a previous version of the article published in Remote Sensing of Environment. 2016Environment. , 187: 130-144. doi:10.1016Environment. /j.rse.2016 inversion models would provide a successful method of future precision farming studies.35

It has been recently shown that the TanDEM-X mission is capable of tracking the plant growth of rice paddies. The precision of the elevation measure depends on the physical interaction between the synthetic aperture radar (SAR) signal and the canopy. In this letter, this interaction is studied by considering the signal polarization. In particular, the vertical and horizontal wave polarizations are compared, and their performance in the temporal mapping of the crop height is analyzed. The temporal elevation difference analysis shows a monotonically increasing trend within the reproductive stage of the canopy, with maximum height discrepancies between polarizations of about 9 cm. From an operational point of view of InSAR-based vegetation height measurements, this letter demonstrates that the oriented structure of the canopy shall be considered not only in polarimetric InSAR studies but also in the interpretation of bistatic spaceborne interferometric elevation models.

Abstract: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.

Precision agriculture research, which aims to monitor agricultural fields and to manage agricultural practice by considering overall environmental impacts, has gained momentum with the recent improvements in the remote sensing area. The objective of this letter, as a part of precision farming, is to implement Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale assignment in plant growth monitoring by means of SAR. The proposed approach copes with structural heterogeneity in agricultural fields by grouping together similar morphologies. For this, densely cultivated paddy rice fields are analyzed using TerraSAR-X (TSX) co-polar SAR data. For generating structurally similar groups, K-means clustering is used in a polarimetric feature vector space, which is composed of backscattering intensities and polarimetric phase differences. This step is followed by a preliminary classification approach based on the temporal separability of the explanatory parameters. In the last step of the proposed methodology, assigned classes are updated based on the biological principles that are followed in rice cultivation. This letter provides the results of the proposed algorithm and compares them to the standard threshold-based approach in two independent agricultural areas. The results show the superiority of the feature-clustering-based classification compared with the standard approach in handling field heterogeneity.

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 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.

In this study, water depth distribution (bathymetric map) in a eutrophic shallow lake was determined using a WorldView-2 multispectral satellite image. Lake Eymir in Ankara (Turkey) was the study site.In order to generate the bathymetric map of the lake, image and data processing, and modelling were applied. First, the bands that would be used in depth prediction models were determined through statistical and multicollinearity analyses. Then, data screening was performed based on the standard deviation of standardized residuals (SD_SR) of depth values determined through preliminary linear regression models. This analysis indicated the sampling points utilized in depth modelling.Finally, linear and non-linear regression models were developed to predict the depths in Lake Eymir based on remotely sensed data. The non-linear regression model performed slightly better compared to the linear one in predicting the depths in Lake Eymir. Coefficients of determination (R 2 ) up to 0.90 were achieved. In general, the bathymetric map was in agreement with observations except at resuspension areas. Yet, regression models were successful in defining the shallow depths at shore, as well as at the inlet and outlet of the lake. Moreover, deeper locations were successfully identified.

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