Above-ground biomass estimates based on active and passive microwave sensor imagery in low-biomass savanna ecosystems

Abstract

Although many studies exist on the estimation and monitoring of above-ground biomass (AGB) of forest ecosystems by methods of remote sensing, very little research has been carried out for ecosystems of low primary production, such as grasslands, steppes, or savannas. Our study intends to approach this gap and investigates the correlation between space-borne radar information and AGB at the scale of 10 tons per hectare and below. Additionally, we introduce the integration of passive brightness temperature as an additional covariate for biomass estimation, based on the hypothesis that it contains information complementary to microwave backscatter of the active sensors. Our findings show that large-scale estimates of AGB can be conducted for grasslands and savannas at high accuracy (R 2 up to 0.52). Additionally, we found that the integration of passive radar can increase the quality of AGB estimates in terms of explained variance for selected cases. We hope that these indications are a starting point for more integrated approaches toward biomass estimations based on Earth observation methods.

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

“…In our study, using RF, we produced an AGB map of an area in the Brazilian Cerrado with a R 2 of 0.89 and RMSE of 7.58 Mg ha −1 . Previous studies tried to estimate the AGB in savannas using satellite data and reached performances lower than the results from our work [54,91,92].…”

“…Recent studies have explored the combination of different data types. Braun et al [91] used passive and active microwaves to estimate the AGB of savannas. They introduced the integration of passive brightness temperature as an additional variable for AGB estimation, based on the hypothesis that it contains information complementary to the microwave backscatter coefficient from active sensors.…”

Refugee Camp Monitoring and Environmental Change Assessment of Kutupalong, Bangladesh, Based on Radar Imagery of Sentinel-1 and ALOS-2

“…For the discrimination of forests from grasslands, the contribution of the sensor's different wavelengths would have become more evident. This was already demonstrated by Braun et al [129], who combined short and long radar signals of active and passive sensors to derive regional maps of above-ground biomass in Senegal. By the time this article was written, the only satellites capturing radar images at the L-band were ALOS-2 and SAOCOM-1A, which are commercially distributed by JAXA and CONAE respectively [130].…”

Review of Remote Sensing Applications in Grassland Monitoring

“…The performance of linear and exponential models based on various forms of the NDVI and simple ratio (SR) were compared, which indicated that the spectral saturation effect due to high biomass densities severely degraded the estimation accuracy, while the selection of indices depending on the spectral characteristics of grasslands was more crucial than the choice of regression models. In addition, Braun et al [15] explored the possibility of using satellite radar data alone. PCA was applied to integrate SAR data and passive brightness temperature data from radar satellites, which showed that the radar-based exponential regression model performed better in a savanna with low biomass but could not cope with high biomass, only achieving the R 2 of 0.52.…”

“…The R 2 values between the estimated and actual AGB have been extracted according to their best exper-imental results, which can be used to evaluate the conformance between estimated and actual values. Bao et al [69] linear regression semiarid fused spectral band satellite 0.79 Li et al [39] linear regression alpine EVI satellite 0.85 van der Merwe et al [25] linear regression tallgrass prairie vegetation height UAV 0.91 Ren et al [26] linear regression desert SAVI ground 0.64 Wijesingha et al [31] linear regression typical vegetation height ground 0.61 Rueda-Ayala et al [27] linear regression grazing vegetation height ground 0.88 Braun et al [15] exponential regression low-biomass savanna SAR satellite 0.52 Zeng et al [68] exponential regression alpine NDVI satellite 0.48 Zhang et al [65] exponential regression typical NDVI satellite 0.64 Chu [33] exponential regression alpine, temperate NDVI satellite 0.84 Wang et al [38] logarithmic regression semiarid NDVI satellite 0.71 Zhang et al [21] logarithmic regression alpine, desert, salt marsh vegetation height UAV 0.89 Shi et al [22] polynomial regression alpine RGBVI, surface bare ratio UAV 0.88 Grüner et al [60] reduced major axis regression temperate vegetation height UAV 0.…”