NDVI-based Downscaling of the CREMAP Actual Evapotranspiration Maps

The increasingly used remote sensing-based evapotranspiration estimation techniques provide information about the spatial and temporal variability of evapotranspiration on the field and regional scales. For Hungary, the most reliable evapotranspiration mapping model is the CREMAP (Calibration-Free Evapotranspiration Mapping), which uses MODIS surface temperature data. The CREMAP evapotranspiration with its 1000×1000 m (1 km2) resolution can be used for examinations with larger scales, for example the comparison of the water balance of forests with different land cover types (agricultural areas, artificial surfaces, etc.). However, the 1 km2 spatial resolution is too coarse to be used for smaller scales like precision forest management or agroforestry systems. Therefore, a vegetation index-based (MODIS NDVI) downscaling process of the CREMAP evapotranspiration was developed, to a resolution of 250×250 m (6.25 hectares). The downscaling experiment was done for Hungary, for a drier (2003 May-October) and for a wetter (2005 May-October) period. The products were analyzed, according to forest stand types. The vegetation index-based evapotranspiration downscaling process can be used for getting hydrological data for forest resource management, climate change impact studies on smaller scales or agroforestry system research

Modeling the spatial distribution of soil nitrogen content at smallholder maize farms using machine learning regression and Sentinel-2 data

Nitrogen is one of the key nutrients that indicate soil quality and an important component for plant development. Accurate knowledge and management of soil nitrogen is crucial for food security in rural communities, especially for smallholder maize farms. However, less research has been done on generating digital soil nitrogen maps for these farmers. This study examines the utility of Sentinel-2 satellite data and environmental variables to map soil nitrogen at smallholder. maize farms. Three machine learning algorithms—random forest (RF), gradient boosting (GB), and extreme gradient boosting (XG) were investigated for this purpose. The findings indicate that the RF (R 2 = 0.90, RMSE = 0.0076%) model performs slightly better than the GB (R2 = 0.88, RMSE = 0.0083%) and XG (R2 = 0.89, RMSE = 0.0077%) models. Furthermore, the variable importance measure showed that the Sentinel-2 bands, particularly the red and red-edge bands, have a superior performance in comparison to the environmental variables and soil indices. The digital maps generated in this study show the high capability of Sentinel-2 satellite data to generate accurate nitrogen content maps with the application of machine learning. The developed framework can be implemented to map the spatial pattern of soil nitrogen. This will also contribute to soil fertility interventions and nitrogen fertilization management to improve food security in rural communities. This application contributes to Sustainable Development Goal number 2.The Agricultural Research Council, the National Research Foundation and the University of Pretoria.https://www.mdpi.com/journal/sustainabilityGeography, Geoinformatics and Meteorolog

Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

This is the final version. Available from MDPI via the DOI in this record. Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.European Space Agency (ESA)European Union’s Horizon 202

Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

Monitoring leaf phenology allows for tracking the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the PhenoCam Network, provide valuable information on phenological state at high temporal resolution, but have limited coverage. To more broadly remotely sense phenology, satellite-based data that has lower temporal resolution has primarily been used (e.g., 16-day MODIS NDVI 10 product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and -17) allow the monitoring of NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we examine the current capacity of this new data to measure the phenology of deciduous broadleaf forests for the first two full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the start, middle, and end of season transition dates to those obtained from PhenoCam and MODIS 16-day NDVI and EVI products. Compared to the MODIS 15 indices, GOES was more correlated with PhenoCam at the start and middle of spring, but had a larger bias (3.35 ± 0.03 days later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p-values £ 0.013) estimates of all transition dates than any of the satellite sources did. GOES transition date uncertainties were significantly smaller than those of MODIS EVI for all transition dates (all p-values £ 0.026), but were only smaller (based on p-value < 0.05) than those from MODIS NDVI for 20 the beginning and middle of spring estimates. GOES will improve the monitoring of phenology at large spatial coverages and is able to provide real-time indicators of phenological change even for spring transitions that might occur within the 16-day resolution of these MODIS products.https://doi.org/10.5194/bg-2020-30

Using Satellite Observations of Soil Moisture to Improve Modeling of Terrestrial Water Cycles

Terrestrial evapotranspiration (ET) describes the flux of water from the Earth’s surface to the atmosphere, calculated as the sum of evaporation from soil and leaf surfaces, and transpiration through plant stomata. ET is the largest terrestrial water flux, returning over half of the precipitation that falls on land back to the atmosphere, annually. Additionally, ET plays a key role in Earth’s carbon, water, and energy cycles, linking them together via the movement of water and CO2 through plant stomata. Because of its important role in these Earth system processes, it is essential that existing methods of measuring and modeling ET are accurate. A common method for estimating and monitoring ET at global scales is through satellite remote sensing. The remote sensing-based models use a combination of satellite observed vegetation and surface meteorology to estimate ET. Although these models can be effective at representing global patterns of ET, a common shortfall is that few use soil moisture as a direct model input. The lack of soil moisture information in these models can significantly degrade ET estimates, as soil moisture is tightly linked to both soil evaporation and plant transpiration. This thesis addresses this gap by introducing a satellite observed soil moisture control to an existing operational remote sensing-based ET model, MOD16. The results show that introducing a soil moisture control to MOD16 improves estimates of ET across a wide range of climates and vegetation types within the contiguous United States study area. This research provides an improved regional representation of ET and clarifies the role of soil moisture in regulating terrestrial ET and the water cycle. These results can be used to better understand and predict shifts in the regional water cycle induced by drought and climate change