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In this study, research on productivity and land cover monitoring is presented, with a focus on sugarcane, based on space-based remote sensing observations that were collected by Synthetic Aperture Radar (SAR) and optical sensors. The study aims to provide new insights into techniques and methodologies that allow for cost-efficient monitoring of sugarcane productivity and the wide-scale expansion of sugarcane over long time series. It is part of a wider framework of research projects, sharing the overarching goal to contribute to a biobased society, where the resources for the production of chemicals, materials and energy are based on biomass, produced in a competitive and sustainable manner. São Paulo state, Brazil, was selected as study area, primarily because it is one of the most prominent regions worldwide that hosts sugarcane, which is one of the most prominent crops for bio-energy production.
The study includes the following research.
First of all, results are presented that are based on a wide range of biophysical measurements that were collected during a year-long ground measurement campaign in several sugarcane fields. These results are accompanied by detailed quality assessments, illustrating their reliability when collecting such measurements through such campaigns. In addition, the methodology for setting up and carrying out the ground campaign is explained, which was designed to minimize biomass alterations in the field in light of the use of the measurements for validation of space-based SAR and optical remote sensing signals.
Secondly, remote sensing signals from various satellites are compared to the ground reference measurements in order to develop space-based sugarcane productivity monitoring techniques. It includes an analysis on the sensitivity of C-band and L-band SAR and optical observations to sugarcane biomass growth, to precipitation events and to SAR sensor configurations. In addition, the spatial features in satellite imagery from the various sensors are analyzed for their temporal consistencies in order to deduce time windows during which the satellite observations are most effective for productivity monitoring. It was found that especially saturation, precipitation and sensor configurations dictate the effectiveness, particularly for SAR. Furthermore, the highest spatial resolution optical imagery proved to perform best for mapping intra-field productivity differences that were measured in the field. In addition to this study, two related but smaller studies are presented. The first focuses on a specific remote sensing technique to identify patterns in a sugarcane field that occur persistently in time. The second demonstrates how plant gaps in a densely ground-measured sugarcane field affect signals from various SAR and how this effect is influenced by spatial averaging windows, precipitation events, sugarcane height and SAR sensor type.
Thirdly, the performance of a specific Bayesian land cover monitoring model that combines SAR and optical observations is demonstrated. The model is an adaptation of the Hidden Markov Model, which allows for the temporally-consistent tracking of vegetation states regardless of gaps in satellite observations. Attention is paid to the effect of precipitation during SAR observations on the model's performance and to certain vegetation conditions that cause classification confusion between land cover types. The research finally provides detailed insights into when SAR-only observations outperform optical-only observations and vice versa, in addition to the advantages when combining them.
Finally, a technique is introduced that exploits SAR signal fluctuations caused by varying (ground and plant) surface wetness conditions in order to improve the characterization of vegetation. Three scenarios that define the selection of SAR observations were investigated for their effect on the classification performance: (i) no distinction between wetness conditions, (ii) distinction between wetness conditions at the time of the SAR acquisitions and (iii) distinction between wetness conditions between consecutive SAR acquisitions. Particularly when the wetness conditions differ under the last scenario, it was found that performances improve. When combining this information with a-priori knowledge on soil types, the accuracy of the classification further increases. For this, maps are used that are a result from applying the previously introduced Hidden Markov Model over the entire state of São Paulo.
The datasets that are used in these studies were mainly acquired by the SAR satellites Sentinel-1, Radarsat-2 and ALOS-2, and by the optical satellites Landsat-8 and Worldview-2. For the studies that are related to land cover monitoring and vegetation characterization, high performance computing was required due to the vast amount of observation data and the complexity of the applied techniques. These facilities were mainly provided by the Dutch national supercomputer of SURF and by Google Earth Engine.
