The International Soil Moisture Network:Serving Earth system science for over a decade

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository

The use of remotely sensed data for forest biomass monitoring : a case of forest sites in north-eastern Armenia

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIn recent years there has been an increasing interest in the use of synthetic aperture radar (SAR) data and geospatial technologies for environmental monitoring․ Particularly, forest biomass evaluation was of high importance, as forests have a crucial role in global carbon emission. Within this study we evaluate the use of Sentinel 1 C-band multitemporal SAR data with combination of Alos Palsar L-band SAR and Sentinel 2 multispectral remote sensing (RS) data for mapping forest aboveground biomass (AGB) of dry subtropical forests in mountainous areas. Field observation from National Forest Inventory was used as a ground truth data. As the SAR data suffers greatly by the complex topography, a simple approach of aspect and slope information as forestry ancillary data was implemented directly in the regression model for the first time to mitigate the topography effect on radar backscattering value․ Dense time-series analysis allowed us to overcome the SAR saturation by the forest phenology and select the optimal C-band scene. Image texture measures of SAR data has been strongly related to the biomass distribution and has robustly contributed to the prediction․ Multilinear Stepwise Regression allowed to select and evaluate the most relevant variables for AGB. The prediction model combining RS with ancillary data explained the 62 % of variance with root-mean-square error of 56.6 t ha¯¹. The study also reveals that C-band SAR data on forest biomass prediction is limited due to their short wavelength. Further, the mountainous condition is a major constraint for AGB estimation. Additionally, this research demonstrates a positive outcome in forest AGB prediction with freely accessible RS data

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Book of abstracts, 4th World Congress on Agroforestry

International audienc

Modeling the contribution of ecological agriculture for climate change mitigation in cote d'Ivoire

The use of crop models is motivated by the prediction of crop production under climate change and for the evaluation of climate risk adaptation strategies. Therefore, in the present study the performance of DSSAT 4.6 was evaluated in a cropping system involving integrated soil fertility management options that are being promoted as ways of adapting agricultural systems to improve both crop yield and carbon sequestration on highly degraded soils encountered throughout middle Côte d’Ivoire. Experimental data encompassed two seasons in the Guinea savanna zone. Residues from the preceding vegetation were left to dry on plots like mulch on an experimental design that comprised the following treatments: (i) herbaceous savanna-maize, (ii)10 year-old of the shrub Chromolaena odorata fallow-maize (iii) 1 or 2 year-old Lalab pupureus stand-rotation, (iv) the legume L. pupureus -maize rotation; (v) continuous maize crop fertilized with urea; (vi) continuous maize crop fertilized with triple superphosphate; (vii) continuous maize crop, fertilized with both urea and triple superphosphate (TSP); (viii) continuous maize cultivation. The model’s sensitivity analysis was run to figure out how uncertainty of stable organic carbon (SOM3) can generate variation in the prediction of soil organic carbon (SOC) dynamics during the monitoring period of two years, within the first soil layer and to estimate the most suitable value. The observed variations were of 0.05 % in total SOC within the short-term and acceptable dynamics of changes were obtained for 0.80% of SOM3. The DSSAT model was calibrated using data from the 2007-2008 season and validated against independent data sets of yield of 2008-2009 to 2011-2012 cropping seasons. After the default values for SOM3 used in the model was substituted by the estimated one from sensitivity analysis, the model predicted average maize yields of 1 454 kg ha-1 across the sites versus an observed average value of 1 736 kg ha-1, R2 of 0.72 and RMSE of 597 kg ha-1. The impact of fallow residues and cropping sequence on maize yield was simulated and compared to conventional fertilizer and control data using historical climate scenarios over 12 years. Improving soil fertility through conservation agriculture cannot maintain grain yield in the same way as conventional urea inputs, although there is better yield stability against high climate variability according to our results