Regional equivalent water thickness modeling from remote sensing across a tree cover/lai gradient in mediterranean forests of northern Tunisia

The performance of vegetation indexes derived from moderate resolution imaging spectroradiometer (MODIS) sensors is explored for drought monitoring in the forests of Northern Tunisia; representing a transition zone between the Mediterranean Sea and the Sahara Desert. We investigated the suitability of biomass and moisture vegetation indexes for vegetation water content expressed by the equivalent water thickness (EWT) in a Mediterranean forest ecosystem with contrasted water budgets and desiccation rates. We proposed a revised EWT at canopy level (EWTCAN) based on weekly field measurements of fuel moisture in seven species during the 2010 dry period, considering the mixture of plant functional types for water use (trees, shrubs and herbaceous layers) and a varying vegetation cover. MODIS vegetation indexes computed and smoothed over the dry season were highly correlated with the EWTCAN. The performances of moisture indexes Normalized Difference Infrared Index (NDII6 and NDII7) and Global Moisture Vegetation Index (GVMI6 and GVMI7) were comparable, whereas for biomass vegetation indexes, Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI) and Adjusted Normalized Difference Vegetation Index (ANDVI) performed better than Enhanced Vegetation Index (EVI) and Soil Adjusted Vegetation Index (SAVI). We also identified the effect of Leaf Area Index (LAI) on EWTCAN monitoring at the regional scale under the tree cover/LAI gradient of the region from relatively dense to open forest. Statistical analysis revealed a significant decreasing linear relationship; indicating that for LAI less than two, the greater the LAI, the less responsive are the vegetation indexes to changes in EWTCAN; whereas for higher LAI, its influence becomes less significant and was not considered in the inversion models based on vegetation indexes. The EWTCAN time-course from LAI-adapted inversion models based on significantly-related vegetation indexes to EWTCAN showed close profiles resulting from the inversion models using NDVI, ANDVI, MSAVI and NDII6 applied during the dry season. The developed EWTCAN model from MODIS vegetation indexes for the study region was finally tested for its ability to capture the topo-climatic effects on the seasonal and the spatial patterns of desiccation/rewetting for keystone periods of Mediterranean vegetation functioning. Implications for further use in scientific developments or management are discussed

Potential of using remote sensing techniques for global assessment of water footprint of crops

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The WF of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use

Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R2 = 0.48; RMSE= 151.92 kg ha−1) compared with the unassimilated results (R2 = 0.23;RMSE= 373.6 kg ha−1) and the TM LAI results (R2 = 0.27; RMSE= 191.6 kg ha−1). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10–30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI \u3c 2m2/m2, AGB \u3c 500 g/m2) and optical data of LC8 and S2 at high vegetation cover (LAI \u3e 2m2/m2, AGB \u3e 500 g/m2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management

Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

1. Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). 2. We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. 3. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic‐related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi‐sensor/multi‐platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. 4. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

The application of the surface energy balance system model to estimate evapotranspiration in South Africa

Includes abstract.Includes bibliographical references.In a water scarce country like South Africa with a number of large consumers of water, it is important to estimate evapotranspiration (ET) with a high degree of accuracy. This is especially important in the semi-arid regions where there is an increasing demand for water and a scarce supply thereof. ET varies regionally and seasonally, so knowledge about ET is fundamental to save and secure water for different uses, and to guarantee that water is distributed to water consumers in a sustainable manner. Models to estimate ET have been developed using a combination of meteorological and remote sensing data inputs. In this study, the pre-packaged Surface Energy Balance System (SEBS) model was used for the first time in the South African environment alongside MODerate Resolution Imaging Spectroradiometer (MODIS) satellite data and validated with eddy covariance data measured in a large apple orchard (11 ha), in the Piketberg area of the Western Cape. Due to the relative infancy of research in this field in South Africa, SEBS is an attractive model choice as it is available as open-source freeware. The model was found to underestimate the sensible heat flux through setting it at the wet limit. Daily ET measured by the eddy covariance system represented 55 to 96% of the SEBS estimate, an overestimation of daily ET. The consistent underestimation of the sensible heat flux was ascribed to sensitivities to the land surface air temperature gradient, the choice of fractional vegetation cover formula as well as the height of the vegetation canopy (3.2 m) relative to weather station reference height (2 m). The methodology was adapted based on the above findings and was applied to a second study area (quaternary catchment P10A, near Grahamstown, Eastern Cape) where two different approaches for deriving surface roughness are applied. It was again demonstrated that the sensible heat flux is sensitive to surface roughness in combination with land surface air temperature gradient and again, the overestimation of daily ET persisted (actual ET being greater than reference ET). It was concluded that in complex environments, at coarse resolution, it is not possible to adequately describe the remote sensing derived input parameters at the correct level of accuracy and at the spatial resolution required for the accurate estimation of the sensible heat flux

How does the global Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Photosynthetically Active Radiation (FPAR) product relate to regionally developed land cover and vegetation products in a semi-arid Australian savanna?

Spatio-temporally variable information on total vegetation cover is highly relevant to water quality and land management in river catchments adjacent to the Great Barrier Reef, Australia. A time series of the global Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Photosynthetically Active Radiation (FPAR; 2000-2006) and its underlying biome classification (MOD12Q1) were compared to national land cover and regional, remotely sensed products in the dry-tropical Burdekin River. The MOD12Q1 showed reasonable agreement with a classification of major vegetation groups for 94% of the study area. We then compared dry-seasonal, quality controlled MODIS FPAR observations to (i) Landsat-based woody foliage projective cover (wFPC) (2004) and (ii) MODIS bare ground index (BGI) observations (2001-2003). Statistical analysis of the MODIS FPAR revealed a significant sensitivity to Landsat wFPC-based Vegetation Structural Categories (VSC) and VSC-specific temporal variability over the 2004 dry season. The MODIS FPAR relation to 20 coinciding MODIS BGI dry-seasonal observations was significant (ρ < 0.001) for homogeneous areas of low wFPC. Our results show that the global MODIS FPAR can be used to identify VSC, represent VSC-specific variability of PAR absorption, and indicate that the amount, structure, and optical properties of green and non-green vegetation components contribute to the MODIS FPAR signal