Integrating RADAR and optical imagery improve the modelling of carbon stocks in a mopane-dominated African savannah dry forest

This study examined the integration of two satellite data sets, that is Landsat 7 ETM+ and ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture RADAR) in estimating carbon stocks in mopane woodlands of north-western Zimbabwe. Mopane woodlands cover large spatial extents and provide ecosystem benefits to the rural economies and grazing resources for both livestock and wildlife. In this study, artificial neural networks (ANN) were used to estimate carbon stocks based on spectral metrics derived from Landsat 7 ETM+ and ALOS PALSAR. To determine the utility of the two satellite-derived metrics, a two-pronged modelling framework was adopted. Firstly, we used spectral bands and vegetation indices from the two satellite data sets independently, and subsequently, we integrated the metrics from the two satellite data sets into the final model. Results showed that the ALOS PALSAR (R2 = 0.75 and nRMSE = 0.16) and Landsat ETM+ (R2 = 0.78 and nRMSE = 0.14) derived spectral bands and vegetation indices comparatively yielded accurate estimations of carbon stocks. Integrating spectral bands and vegetation indices from both sensors significantly improved the estimation of carbon stocks (R2 = 0.84 and nRMSE = 0.12). These findings underscore the importance of integrating satellite data in vegetation biophysical assessment and monitoring in poorly documented ecosystems such as the mopane woodlands

Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use

Caracterización de rotaciones de cultivos y su impacto en el rendimiento y funcionamiento de sistemas agrícolas

Tesis presentada para optar al título de Doctor en Área Ciencias Agropecuarias, de la Universidad de Buenos Aires, en 2023El análisis de las rotaciones de cultivos a escala regional requiere información histórica a nivel de lote que considere las diferentes condiciones presentes en el área. Esta información ha sido de escasa y dispersa en Argentina durante los últimos años, a pesar de existir herramientas que permitían obtenerla. Es posible a partir de sensores remotos la identificación de los cultivos sembrados en un área y la estimación del crecimiento y rendimiento. El objetivo de esta tesis fue la caracterización de las secuencias de cultivos a lo largo de una serie de campañas agrícolas y la evaluación de su impacto a nivel productivo y ambiental. Se generaron mapas de cultivos a lo largo de siete campañas agrícolas en un área piloto de la Pampa Ondulada donde se analizó la ocurrencia de casos de monocultivo y rotación en relación a variables humanas y ambientales. Se desarrolló un modelo de estimación de biomasa en soja como indicador del estado y rendimiento del cultivo. Se evaluó el efecto de diferentes secuencias de cultivos en la producción de biomasa de lotes de soja y en el carbono orgánico del suelo. A nivel nacional se analizaron las secuencias de cultivos a lo largo de tres campañas. Los casos de monocultivo estuvieron asociados a parcelas catastrales pequeñas comúnmente asignadas a arrendamiento y se agruparon en cercanías a centros de acopio para transporte e industrialización. Se observó un efecto positivo y significativo en biomasa de soja para índices relacionados con el número de períodos con rotación y con la proporción de gramíneas, mientras que se identificaron efectos negativos y significativos con la proporción de soja de primera y con la intensidad de siembra. El análisis espacial de secuencias de cultivos permite identificar controles de su distribución y proponer estrategias que favorezcan la implementación de buenas prácticas agrícolas.Crop rotation analysis at regional level requires the availability of information about planting history at field level that considers the different conditions in the area. This kind of information has been scarse and dispersed in Argentina during last years despite the availability of tools to generate it. Through remote sensing it is possible to identify crops planted in an area and the estimation of crop growing and yield. The objective of this thesis was to characterize crop sequences along a serie of growing seasons and to evaluate its impact in productivity and environment. Seven crop type maps were generated along consecutive growing seasons in a pilot site in Pampa Ondulada where the occurrence of monoculture and rotation was analyzed in relation to human and environmental variables. In addition, a model for the estimation of soybean biomass as a proxy of crop condition and yield was developed. The effect of different crop sequences was evaluated in relation to soybean biomass production and soil organic carbon. At national level, crop sequences were analyzed along three growing seasons. The occurrence of monoculture was associated to small cadastral units were positive and significant effect on soybean biomass was observed for indices related to the number of rotation periods and the proportion of cereals; while negative and significant effects were found for the number of periods with monoculture, the total number of periods with early soybean and with planting intensity. Spatial analysis of crop sequences allows to identify controls of its occurrence and to develop tools that promote the implementation of good agricultural practices.Fil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentin

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Deriving crop productivity indicators from satellite synthetic aperture radar to assess wheat production at field-scale.

Richter, G. M. Industrial supervisor ( Rothamsted Research) Burgess, Paul J. and Meersmans, Jeroen Associate supervisorsThe deployment of high-revisit satellite-based radar sensors raises the question of whether the data collected can provide quantitative information to improve agricultural productivity. This thesis aims to develop and test mathematical algorithms to describe the dynamic backscatter of high-resolution Synthetic Aperture Radar (Sentinel-1) in order to describe the development and productivity of wheat at field-scale. A time series of the backscatter ratio (VH/VV), collected over a cropping season, could be characterised by a growth and a senescence logistic curve and related to critical phases of crop development. The curve parameters, referred to as Crop Productivity Indicators (CPIs), compared well with the crop production for three years at the Rothamsted experimental farm. The combination of different parameters (e.g. midpoints of the two curves) helped to define CPIs, such as duration, that significantly (r = 0.61, p = 0.05) correlated with measured yields. Field observations were used to understand the wheat evolution by sampling canopy characteristics across the seasons. The correlation between the samples and the CPIs showed that structural changes, like biomass increase, influence the CPIs during the growth phase, and that declining plant water content was correlated with VH/VV values during maturation. The methodology was upscaled to other farms in Hertfordshire and Norfolk. The ANOVA identified significant effects (p<0.001) of farm management, year (weather conditions) and the interaction between soil type and year on the selected CPIs. Multilinear regression models between yields and selected CPIs displayed promising predictive power (R²= 0.5) across different farms in the same year. However, these models could not explain yield differences within high-yielding farms across seasons because of the dominant effect of weather patterns on the CPIs in each year. The potential impact of the research includes estimation of yield across the landscape, phenology monitoring and indication biophysical parameters. Future work on SAR-derived CPIs should focus on improving the correlations with biophysical properties, applying of the methodology in other crops, with different soils and climates.PhD in Environment and Agrifoo

Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms

Forests are one of the major carbon sinks that significantly contribute towards achieving targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG) emissions. In order to contribute to regular National Inventory Reporting, and as part of the on-going development of the Irish national GHG reporting system (CARBWARE), improvements in characterisation of changes in forest carbon stocks have been recommended to provide a comprehensive information flow into CARBWARE. The Irish National Forest Inventory (NFI) is updated once every six years, thus there is a need for an enhanced forest monitoring system to obtain annual forest updates to support government agencies and forest management companies in their strategic decision making and to comply with international GHG reporting standards. Sustainable forest management is imperative to promote net carbon absorption from forests. Based on the NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become a net emitter of carbon. Disturbances from human induced activities such as clear felling, thinning and deforestation results in carbon emissions back into the atmosphere. Funded by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR) satellite based sensors for monitoring changes in the small stand forests of Ireland. Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2 PALSAR-2 sensors have been used to map forest areas and characterise the different disturbances observed within three different regions of Ireland. Forest mapping and disturbance characterisation was achieved by combining the machine learning supervised Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis (ISODATA) classification techniques. The lack of availability of ground truth data supported use of this unsupervised approach which forms natural clusters based on their multi-temporal signatures, with divergence statistics used to select the optimal number of clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial where there is a dearth of ground-based information. When applied to the forests, mapped with an accuracy of up to 97% by RF, the ISODATA technique successfully identified the unique multi-temporal pattern associated with clear-fells which exhibited a decrease of 4 to 5 decibels (dB) between the images acquired before and after the event. The clustering algorithm effectively highlighted the occurrence of other disturbance events within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas of tree growth and afforestation. A highlight of the work is the successful transferability of the algorithm, developed using ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential continuity of annual forest monitoring. The higher spatial and radiometric resolutions of ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images. Moreover, even with some different backscatter characteristics of images acquired in different seasons, similar signature patterns between the sensors were retrieved that helped to define the cluster groups, thus demonstrating the robustness of the algorithm and its successful transferability. Having proven the potential to monitor forest disturbances, the results from both the sensors were used to detect deforestation over the time period 2007-2016. Permanent land-use changes pertaining to conversion of forests to agricultural lands and windfarms were identified which are important with respect to forest monitoring and carbon reporting in Ireland. Overall, this work has presented a viable approach to support forest monitoring operations in Ireland. By providing disturbance information from SAR, it can supplement projects working with optical images which are generally limited by cloud cover, particularly in parts of northern, western and upland Ireland. This approach adds value to ground based forest monitoring by mapping distinct forests over large areas on an annual basis. This study has demonstrated the ability to apply the algorithm to three different study areas, with a vision to operationalise the algorithm on a national scale. The main limitations experienced in this study were the lack of L-band SAR data availability and reference datasets. With typically only one image acquired per year, and discrepancies and omissions existing within reference datasets, understanding the behaviour of certain cluster groups representing disturbances was challenging. However, this approach has addressed some issues within the reference datasets, for example locating areas for which a felling licence was granted but where trees were never cut, by providing detailed systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B, P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited SAR image acquisitions provided more images per year are available, especially during the summer months