ORCID Profile
0000-0001-7236-7600
Current Organisations
University of South Australia
,
Agriculture Victoria
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Publisher: Elsevier BV
Date: 06-2016
Publisher: Wiley
Date: 31-10-2014
Publisher: MDPI AG
Date: 26-06-2022
DOI: 10.3390/RS14133071
Abstract: Remote sensing from optical radiometers in space offers a nondestructive approach to estimating above ground biomass (AGB) with high spatial and temporal resolution, but the application is challenged by cloud cover and differences in soil background and crop phenology. We present a framework based on Sentinel-2 imagery for relating the adjusted summed NDVI measurements to the AGB. The resulting R2 values for the measured and estimated AGB ranged from 0.79 to 0.98 for in idual paddocks, and the R2 from a pooled dataset (multiple crops, years, and locations) was 0.86. Application of the pooled dataset model to a separate validation dataset resulted in an R2 of 0.88 however, there was a bias that resulted in the underestimation of the measured biomass. Analysis of the impacts of the gaps in the time series showed a decrease of 0.43% per gap day for the summed NDVI values. To address the impacts of clouds, we demonstrate the use of active optical and additional satellite imagery to fill the gaps due to clouds in the Sentinel-2 imagery. The framework presented results of the spatial daily estimates of the AGB and crop growth rates.
Publisher: Wiley
Date: 25-11-2016
DOI: 10.1111/SUM.12310
Abstract: Soil bulk density ( BD ) and effective cation exchange capacity ( ECEC ) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions ( PTF s) for BD and ECEC . Multiple linear regression ( MLR ) and random forest model ( RFM ) were employed in developing PTF s using three different data sets: soil data ( PTF ‐1), environmental data ( PTF ‐2) and the combination of soil and environmental data ( PTF ‐3). In developing the PTF s, three depth increments were also considered: all depth, topsoil ( .40 m) and subsoil ( .40 m). Results showed that PTF ‐3 ( R 2 0.29–0.69) outperformed both PTF ‐1 ( R 2 0.11–0.18) and PTF ‐2 ( R 2 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF ‐3 ( R 2 0.61–0.86) performed comparably as PTF ‐1 ( R 2 0.58–0.76) with both PTF s out‐performing PTF ‐2 ( R 2 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTF s (especially PTF ‐2 and PTF ‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI , elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH , rainfall and SOC are the most important predictors of ECEC in the topsoil while pH , sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa ( SSA ).
No related grants have been discovered for Stephen Akpa.