Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions

Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can contribute to their optimal management. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 high-resolution visible (HRV) data, to retrieve the surface water content values (from bare soil to completely covered soil) over wheat fields and detect irrigation supplies in an irrigated area. These indices are the normalized difference water index (NDWI) and the moisture stress index (MSI), covering the main growth stages of wheat. These indices were compared to corresponding in situ measurements of soil moisture and vegetation water content in 30 wheat fields in an irrigated area of Morocco, during the 2012–2013 and 2013–2014 cropping seasons. NDWI and MSI were highly correlated with in situ measurements at both the beginning of the growing season (sowing) and at full vegetation cover (grain filling). From sowing to grain filling, the best correlation (R² = 0.86; p < 0.01) was found for the relationship between NDWI values and observed soil moisture values. These results were validated using a k-fold cross-validation methodology; they indicated that NDWI can be used to estimate and map surface water content changes at the main crop growth stages (from sowing to grain filling). NDWI is an operative index for monitoring irrigation, such as detecting irrigation supplies and mitigating wheat water stress at field and regional levels in semi-arid areas.     

Relationship between light use efficiency and photochemical reflectance index in soybean leaves as affected by soil water content

Photosynthetic light response curves and reflectance spectra (380–2500 nm) were measured for soybean (Glycine max L. Merr.) leaves with a range of chlorophyll concentrations at various soil water contents. Regression lines for the relationship between the photosynthetic light use efficiency (LUEp) and photochemical reflectance index (PRI) under different soil water content θ almost all passed through a common point (PRI, LUEp) = (?0.04, 0), so that the LUEp could be expressed simply as LUEp = kAPRI using an adjusted PRI [APRI = (ρ531?ρ570)/(ρ531?ρ570)+0.04]. The effect of soil moisture was strong under dry conditions and gradually decreased with increasing θ. There was no effect of θ above 25% (v/v). The effect of θ on the APRI–LUEp relationship was expressed by a simple exponential function. These results should provide a new basis for applications in dynamic diagnosis of photosynthetic functioning of plant leaves and in the prediction of plant productivity. The change in the slope of LUE vs. APRI may provide further ways of assessing volumetric soil water content.     

A new three-band spectral index for mitigating the saturation in the estimation of leaf area index in wheat

The normalized difference vegetation index (NDVI) is a commonly used index for monitoring crop growth status. Previous studies have shown that the leaf area index (LAI) estimation based on NDVI is limited by saturation that occurs under conditions of relatively dense canopies (LAI > 2 m² m^–2). To reduce the saturation effect, we suggested new spectral indices through the spectral indices approach. The results suggested that the two-band normalized difference spectral index (NDSI = ((ρ_{940 –} ρ₇₃₀) /(ρ₉₄₀ + ρ₇₃₀))) resulted from the two-band spectral indices approach and the three-band modified normalized difference spectral index (mNDSI = ((ρ_{940 –} 0.8 × ρ₉₅₀) – ρ₇₃₀) /((ρ_{940 –} 0.8 × ρ₉₅₀) + ρ₇₃₀)) resulted from the three-band spectral indices approach, and they were able to mitigate saturation and improve the LAI prediction with a determination coefficient (R²) of 0.77 and 0.78, respectively. In the validation based on data from independent experiments, these new indices exhibited an accuracy with relative root mean square error (RRMSE) lower than 23.38% and bias higher than –0.40. These accuracies were significantly higher than those obtained with some existing indices with good performance in LAI estimation, such as the enhanced vegetation index (EVI) (RRMSE = 30.19%, bias = –0.34) and the modified triangular vegetation index 2 (MTVI2) (RRMSE = 29.30%, bias = –0.28), and the indices with the ability to mitigate the saturation, such as the wide dynamic range vegetation index (WDRVI) (RRMSE = 31.37%, bias = –0.54), the red-edge wide dynamic range vegetation index (red-edge WDRVI) (RRMSE = 26.34%, bias = –0.54), and the normalized difference red-edge index (NDRE) (RRMSE = 28.41%, bias = –0.56). Additionally, these new indices were more sensitive under moderate to high LAI conditions (between 2 and 8 m² m^–2). Between these two new developed spectral indices, there was no significant difference in the accuracy and sensitivity assessments. Considering the index structure and convenience in application, we demonstrated that the two-band spectral index NDSI((ρ_{940 –} ρ₇₃₀) /(ρ₉₄₀ + ρ₇₃₀)) is efficient in mitigating saturation and has considerable potential for estimating the LAI of canopies throughout the entire growing season of wheat (Triticum aestivum L.), whereas the three-band spectral index contributes lesser in the saturation mitigation provided the red-edge band has been contained.     

Aboveground forest biomass derived using multiple dates of WorldView-2 stereo-imagery: quantifying the improvement in estimation accuracy

ABSTRACT

The aim of this study was to investigate the capabilities of two date satellite-derived image-based point clouds (IPCs) to estimate forest aboveground biomass (AGB). The data sets used include panchromatic WorldView-2 stereo-imagery with 0.46 m spatial resolution representing 2014 and 2016 and a detailed digital elevation model derived from airborne laser scanning data. Altogether, 332 field sample plots with an area of 256 m² were used for model development and validation. Predictors describing forest height, density, and variation in height were extracted from the IPC 2014 and 2016 and used in k-nearest neighbour imputation models developed with sample plot data for predicting AGB. AGB predictions for 2014 (AGB₂₀₁₄) were projected to 2016 using growth models (AGB_{Projected_2016}) and combined with the AGB estimates derived from the 2016 data (AGB₂₀₁₆). AGB prediction model developed with 2014 data was also applied to 2016 data (AGB_{2016_pred2014}). Based on our results, the change in the 90^th percentile of height derived from the WorldView-2 IPC was able to characterize forest height growth between 2014 and 2016 with an average growth of 0.9 m. Features describing canopy cover and variation in height derived from the IPC were not as consistent. The AGB₂₀₁₆ had a bias of ?7.5% (?10.6 Mg ha^?1) and root mean square error (RMSE) of 26.0% (36.7 Mg ha^?1) as the respective values for AGB_{Projected_2016} were 7.0% (9.9 Mg ha^?1) and 21.5% (30.8 Mg ha^?1). AGB_{2016_pred2014} had a bias of ?19.6% (?27.7 Mg ha^?1) and RMSE of 33.2% (46.9 Mg ha^?1). By combining predictions of AGB₂₀₁₆ and AGB_{Projected_2016} at sample plot level as a weighted average, we were able to decrease the bias notably compared to estimates made on any single date. The lowest bias of ?0.25% (?0.4 Mg ha^?1) was obtained when equal weights of 0.5 were given to the AGB_{Projected_2016} and AGB₂₀₁₆ estimates. Respectively, RMSE of 20.9% (29.5 Mg ha^?1) was obtained using equal weights. Thus, we conclude that combination of two date WorldView-2 stereo-imagery improved the reliability of AGB estimates on sample plots where forest growth was the only change between the two dates.     

In situ validation of MERIS marine reflectance off the southwest Iberian Peninsula: assessment of vicarious adjustment and corrections for near-land adjacency

Water-leaving reflectance (ρ_w) data from the European Space Agency ocean colour sensor Medium Resolution Imaging Spectrometer (MERIS) was validated with in situ ρ_w between October 2008 and November 2011, off Sagres on the southwest coast of the Iberian Peninsula. The study area is exceptional, since Stations A, B, and C at 2, 10, and 18 km offshore are in optically deep waters at approximately 40, 100, and 160 m, respectively. These stations showed consistently similar bio-optical properties, characteristic of Case 1 waters, enabling the evaluation of adjacency effects independent of the usual co-varying inputs of coastal waters. Using the third reprocessing of MERIS with the standard MEGS 8.1 processor, four different combinations of procedures were tested to improve the calibration between MERIS products and in situ data. These combinations included no vicarious adjustment (NoVIC), vicarious adjustment (VIC), and, for mitigating the effects of land adjacency on MERIS ρ_w, the improved contrast between ocean and land (ICOL) processor (version 2.7.4) and VIC + ICOL. Out of approximately 130 potential matchups for each station, 38–77%, 74–86%, and 88–90% were achieved at Stations A, B, and C, respectively, depending on which of the four combinations were used. Analyses of ρ_w comparing these various procedures, including statistics, scatter plots, histograms, and MERIS full-resolution images, showed that the VIC procedure compared with NoVIC produced minimal changes to the calibration. For example, at the oceanic Station C, the regression slope was closer to unity at all wavelengths with NoVIC compared to VIC, whereas, with the exception of wavelengths 412 and 443 nm, the intercept, mean ratio (MR), absolute percentage difference (APD), and relative percentage difference (RPD) were better with NoVIC. The differences for MR and APD indicate that there was marginal improvement for these two bands with VIC, and an over-adjustment with RPD. ICOL also showed inconsistent results for improving the retrieval of the near-shore conditions, but under some conditions, such as ρ_w at wavelength 560 nm, the improvement was striking. VIC + ICOL showed results intermediate between those of VIC and ICOL implemented separately. In relation to other validation sites, the offshore Station C at Sagres had much in common with the Mediterranean deep water, BOUSSOLE buoy, although the matchup statistics between MERIS ρ_w and in situ ρ_w were much better for Sagres than for BOUSSOLE. Strikingly, the matchup statistics for ρ_w at Sagres were very similar to those for the Acqua Alta Oceanographic Tower (AAOT), where the AAOT showed more scatter at 412 nm, probably because of the atmospheric correction where the aerosol optical thickness is higher at the AAOT. Conversely, Sagres showed much greater scatter at 665 nm in the red as the values were generally close to the limits of detection owing to the clearer waters at Sagres compared to the more turbid waters at the AAOT.     

Evaluation of the consistency of the three MRPV model parameters provided by the MISR level 2 land surface products: a case study in Mainland Spain

The multiangular Rahman–Pinty–Verstraete modified (MRPV) semi-empirical model uses three parameters (ρ₀, Θ, and k) for describing the anisotropy of an arbitrary target. They have been usefully proved to characterize some forest attributes and land covers. However, there is no enough evaluation of the consistency of this product, and the possible affection from different factors in the reliability of them. Here, we explored the consistency of the MRPV parameters provided in the MISR L2 Land Surface (MIL2ASLS) product, with data from Mainland Spain, grouping MISR images into close time pairs. Thus, it was studied the three MRPV parameters through retrieving Spearman’s rank correlation coefficient (ρ) and mean relative differences related to every pair of images. The results showed the ρ₀ parameter presented higher consistency than the others, with ρ over 0.85 and meant relative differences around 15%. The k parameter showed ρ over 0.65 and average relative disagreements over 8%. Finally, the Θ parameter reached ρ around 0.60. The Θ mean differences were over 25% unless the combination of the blue band which was especially bad and its values were up to 50%. So, it is crucial having into account when the parameters of this product are used to look into the band and the own parameter.     

Applicability of the PROSPECT model for Norway spruce needles

The potential applicability of the leaf radiative transfer model PROSPECT (version 3.01) was tested for Norway spruce (Picea abies (L.) Karst.) needles collected from stress resistant and resilient trees. Direct comparison of the measured and simulated leaf optical properties between 450–1000 nm revealed the requirement to recalibrate the PROSPECT chlorophyll and dry matter specific absorption coefficients k _ab(λ) and k _m(λ). The subsequent validation of the modified PROSPECT (version 3.01.S) showed close agreement with the spectral measurements of all three needle age‐classes tested; the root mean square error (RMSE) of all reflectance (ρ) values within the interval of 450–1000 nm was equal to 1.74%, for transmittance (τ) it was 1.53% and for absorbance (α) it was 2.91%. The total chlorophyll concentration, dry matter content, and leaf water content were simultaneously retrieved by a constrained inversion of the original PROSPECT 3.01 and the adjusted PROSPECT 3.01.S. The chlorophyll concentration estimated by inversion of both model versions was similar, but the inversion accuracy of the dry matter and water content was significantly improved. Decreases in RMSE from 0.0079 g cm^?2 to 0.0019 g cm^?2 for dry matter and from 0.0019 cm to 0.0006 cm for leaf water content proved the improved performance of the recalibrated PROSPECT version 3.01.S.     

INSAT-3D and MODIS retrieved sea surface temperature validation and assessment over waters surrounding the Indian subcontinent

The INSAT-3D imager (4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on-board Aqua and Terra space-platforms level-2 (1 km) sea surface temperature (SST_skin) product accuracy has been analysed over waters surrounding the Indian subcontinent by indirect comparison method using collocated bulk in-situ measurements (SST_depth) for 3 years (October 2013–October 2016). Statistical results show that root mean square error of all the three satellites is in range of around 0.60–0.70°C. Retrieval error is found to be slightly more in case of validation against iQuam data set. INSAT-3D is showing more underestimation with bias ranging from about ?0.16°C to ?0.20°C than MODIS sensor having bias in range of about 0.06°C to ?0.12°C. All the three missions are slightly underestimating over open-ocean with bias ranging in 0–0.17°C. INSAT-3D is significantly underestimating in-situ observations over the Arabian Sea (approximate bias = 0.27°C). Seasonal validation analysis reveals relatively high retrieval error during monsoon season than pre-monsoon and post-monsoon seasons. MODIS sensor is showing significant underestimation during monsoon with bias ranging from approximately ?0.29°C to ?0.58°C. Overall, all the three missions are performing similarly well over the study area.     

Development of real-time reference evapotranspiration at the regional scale using satellite-based observations

Real-time data of reference evapotranspiration (ET₀) at different space-time scales are essential to regional agricultural drought assessment, water accounting at the watershed to basin scale, and provide irrigation advisory to farmers. Here, we present a data-fusion approach that integrates satellite-based insolation product (8 km) from an Indian geostationary satellite (Kalpana-1) sensor (VHRR; Very High Resolution Radiometer) and high-resolution (~ 5 km) short-range weather forecast into an FAO56 model based on the classical Penman–Monteith (P-M) formulation. Five year (2009–2013) mean monthly estimates from the daily ET₀ product over the Indian landmass were found to vary between 10 and 350 mm. It increased from January to May (70–350 mm), followed by a decrease to reach the lowest in November (10–140 mm), thus typically showing unimodal distribution. The comparison of daily space-based and station-based estimates (at six ground stations) produced a root mean square deviation (RMSD) ranging from 21% to 38% for 977 paired data sets with the correlation coefficient (r) varying from 0.32 to 0.82. The error was reduced from 25% to 10% with an increase in ‘r’ from 0.43 to 0.98 for daily to 10 day summation period. Spatial grid-to-grid comparison of monthly ET₀ estimates with Global Data Assimilation System (GDAS) potential evapotranspiration (PET) showed RMSD within a range of 1.4–18.4% for most of the months, except for two. Further ET₀ analysis over normal and drought years showed that it could be used for comprehensive drought assessment with other existing indicators.     

Characterizing the spatiotemporal evolution of soil salinization in Hetao Irrigation District (China) using a remote sensing approach

ABSTRACT

Soil salinization is a major problem of land degradation in arid and semiarid irrigation districts. This study aims to characterize the spatiotemporal evolution of soil salinization in Hetao Irrigation District (HID) in Inner Mongolia, China, using Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager datasets. Salty barren land and farmland are extracted using supervised classification. Then, we develop four integrated soil salinity models (ISSMs) to quantify the intensity of saline farmland. ISSMs are generated through deriving the parameters (E_VI-SIs), which integrate enhanced vegetation index (EVI) and Salinity Index-1 (SI1), EVI and Salinity Index-3 (SI3), Modified Soil Adjusted Vegetation Index (MSAVI) and SI1, and MSAVI and SI3, respectively, from the scatter plots of farmland soils with different salinity in four spectral feature spaces (SFSs). Exponential regression analyses reveal that the E_VI-SI from MSAVI-SI3 SFS has the best fit with in situ soil electrical conductivity measurements (R² = 0.74, root mean square error = 0.31 dS m^–1). Salty barren land clustered in the central and northeast of HID, while the area of salty barren land decreased during 1986–2016. After employing water-saving irrigation since 2000, saline farmland decreased and then remained relatively stable. This study indicates that the SFS integrating MSAVI and SI3 contains effective information for quantifying the saline farmland. Employing water-saving irrigation had a positive effect on controlling salinization.     

Anthropogenic impact on spring bloom dynamics in the Yangtze River Estuary based on SeaWiFS mission (1998–2010) and MODIS (2003–2010) observations

Nutrient output from the Yangtze River to the sea has increased dramatically since the 1960s, and over the past 50 years more than 50,000 reservoirs on the Yangtze River basin have had little impact on water discharge, but have drastically reduced the annual river-to-sea sediment flux, especially after 2000. This can be presumed to have a close link with the 73% (accumulated incidences) of algal bloom reported on China's eastern coast which have taken place in the Yangtze River Estuary (YRE) and its adjacent waters from 2000 to 2009. A conceptual view explains that the algal bloom zone varies between the YRE and mid-shelf waters of the East China Sea, where the optimum balance of light availability and nutrient supply exists. A reduction in turbidity with declining river-to-sea sediment load around the YRE would provide a deeper euphotic layer for the growth of phytoplankton, which is stimulated by eutrophication following increased river-to-sea sewage. Although the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) represents an immature state of ocean colour remote sensing, especially in coastal waters, in general, SeaWiFS serves as a useful tool to highlight the geographical spread and intensity of spring bloom dynamics at the basin scale in the YRE. We suggest that the problem of skin effect bias in spaceborne observation is minimal and that satellite-derived pigment data should be mainly from phytoplankton production when estimating Chl-a during the spring bloom season in the YRE by analysis of bio-optical properties of spring bloom waters. Comparisons between in situ and simultaneous SeaWiFS-derived Chl-a suggest that SeaWiFS standard Chl-a data show comparable results with ship survey data, with a mean ratio (in situ to satellite ratio) of 1.28 ± 0.78 (R ² = 0.71, n = 14, p < 0.001) in spring blooms. We took the seasonal average Chl-a in our defined multi-year highly productive zone in the YRE (28°30′–32°N, 122°–123°30′°E, A) compared with its neighbouring region B from 1998 to 2010, and determined a linear least-squares fit to the trend line. Further analysis in the form of t-tests was run for four seasonal periods, March–April, May–June (spring bloom season), July–August, and September–October. The SeaWiFS-derived 13-year Chl-a mean slope in the spring bloom season is significantly (P = 0.012) increasing by 0.212 mg m^?3 per year. However, such a trend in other seasons is not significant and there is also no significant variation in B over all seasons. Our results show that there is a positive relationship between the annual mean of Chl-a in the spring bloom season in A and annual sewage water discharge at the Yangtze River basin, and similarly a negative relationship between Chl-a and annual river sediment flux was found from 1998 to 2010. Variation in 3-year sediment flux, sewage water discharge, and Chl-a was –25% (±18%), 15% (±5%), and 14% (±6%), respectively. This result supports the findings of previous studies that human activity has a measurable effect on coastal phytoplankton biomass and that the eutrophication effect seems to stimulate increased Chl-a in the spring bloom season, but not on the enhancement of annual Chl-a levels in the YRE.     

Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing

The non-frozen (NF) season duration strongly influences the northern carbon cycle where frozen (FR) temperatures are a major constraint to biological processes. The landscape freeze-thaw (FT) signal from satellite microwave remote sensing provides a surrogate measure of FR temperature constraints to ecosystem productivity, trace gas exchange, and surface water mobility. We analysed a new global satellite data record of daily landscape FT dynamics derived from temporal classification of overlapping SMMR and SSM/I 37 GHz frequency brightness temperatures (T_b). The FT record was used to quantify regional patterns, annual variability, and trends in the NF season over northern (≥45°N) vegetated land areas. The ecological significance of these changes was evaluated against satellite normalized difference vegetation index (NDVI) anomalies, estimated moisture and temperature constraints to productivity determined from meteorological reanalysis, and atmospheric CO₂ records. The FT record shows a lengthening (2.4 days decade^?1; p < 0.005) mean annual NF season trend (1979–2010) for the high northern latitudes that is 26% larger than the Northern Hemisphere trend. The NDVI summer growth response to these changes is spatially complex and coincides with local dominance of cold temperature or moisture constraints to productivity. Longer NF seasons are predominantly enhancing productivity in cold temperature-constrained areas, whereas these effects are reduced or reversed in more moisture-constrained areas. Longer NF seasons also increase the atmospheric CO₂ seasonal amplitude by enhancing both regional carbon uptake and emissions. We find that cold temperature constraints to northern growing seasons are relaxing, whereas potential benefits for productivity and carbon sink activity are becoming more dependent on the terrestrial water balance and supply of plant-available moisture needed to meet additional water use demands under a warming climate.     

Urban green space dynamics and socio-environmental inequity: multi-resolution and spatiotemporal data analysis of Kumasi,Ghana

Urban green spaces (UGS) are crucial for urban sustainability and resilience to environmental vulnerabilities but are often relegated in cities in the global south. This article analysed the spatio-temporal change, composition, extent, and distributional inequities associated with UGS in Kumasi, Ghana. Spatial techniques and Gini index were combined in the assessment. Kumasi UGS coverage is currently 33% but declined fourfold faster in recent years (2009–2014) than previously (1986–2002). The overall accuracy of the change maps: 1986–2014 and 2009–2014 were, respectively, 0.96 ± 0.02 and 0.97 ± 0.02. The Shannon entropy for built-up sprawl in 1986 and 2014 were 0.80 and 0.99, respectively. The UGS area per capita for 2009 (R² = 0.50, p = 0.049) and 2014 (R² = 0.53, p = 0.0398) were moderately correlated with socioeconomic conditions of sub-metropolises. The Gini coefficient for both vegetation and tree cover was 0.26. UGS cover is plummeting and somewhat unevenly distributed across Kumasi. Strategic planning for UGS can ensure ample availability, equity in access, and resilience to climate-related vulnerabilities.     

Assessing corn water stress using spectral reflectance

Multiple remote-sensing techniques have been developed to identify crop-water stress; however, some methods may be difficult for farmers to apply. If spectral reflectance data can be used to monitor crop-water stress, growers could use this information as a quick low-cost guideline for irrigation management, thus helping save water by preventing over-irrigating and achieving desired crop yields. Data was collected in the 2013 growing season near Greeley, Colorado, where drip irrigation was used to irrigate 12 corn (Zea mays L.) treatments with varying water-deficit levels. Ground-based multispectral data were collected and three different vegetation indices were evaluated. These included the normalized difference vegetation index (NDVI), the optimized soil-adjusted vegetation index (OSAVI), and the Green normalized difference vegetation index (GNDVI). The three vegetation indices were compared to water stress as indicated by the stress coefficient (K_s), and water deficit in the root zone was calculated using a soil water balance. To compare the indices to K_s, vegetation ratios were developed from vegetation indices in the process of normalization. Vegetation ratios are defined as the non-stressed vegetation index divided by the stressed vegetation index. Results showed that vegetation ratios were sensitive to water stress as indicated by the good coefficient of determination (R² > 0.46) values and low root mean square error (RMSE < 0.076) values when compared to K_s. To use spectral reflectance to manage crop-water stress, an example irrigation trigger point of 0.93 for the vegetation ratios was determined for a 10–12% loss in yield. These results were validated using data collected from a different field. The performance of the vegetation ratio approach was better than when applied to the main field giving higher goodness of fit values (R² > 0.63), and lower error values (RMSE < 0.043) between K_s and the vegetation indices.     

Global irrigated area map (GIAM), derived from remote sensing,for the end of the last millennium

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3‐band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time‐series for 1997–1999, (b) Système pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50 km monthly time series for 1961–2000, (d) Global 30 Arc‐Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite‐1 Synthetic Aperture Radar (JERS‐1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992–1993. A single mega‐file data‐cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re‐sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments.

Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space–time spiral curve (ST‐SC) plots, (b) brightness–greenness–wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very‐high‐resolution imagery (VHRI) ‘zoom‐in views’ in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high‐resolution Landsat‐ETM+ Geocover 150 m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re‐classify the MDFC, and the class identification and labelling protocol repeated. The sub‐pixel area (SPA) calculations were performed by multiplying full‐pixel areas (FPAs) with irrigated area fractions (IAFs) for every class.

A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year‐round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but ‘equipped for irrigation’ at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467 million hectares (Mha), which is sum of the non‐overlapping areas of: (a) 252 Mha from season one, (b) 174 Mha from season two and (c) 41 Mha from continuous year‐round crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79–91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org).     

Using aerial imagery and digital photography to monitor growth and yield in winter wheat

Monitoring wheat (Triticum aestivum L.) performance throughout the growing season provides information on productivity and yield potential. Remote sensing tools have provided easy and quick measurements without destructive sampling. The objective of this study was to evaluate genetic variability in growth and performance of 20 wheat genotypes under two water regimes (rainfed and irrigated), using spectral vegetation indices (SVI) estimated from aerial imagery and percentage ground cover (%GC) estimated from digital photos. Field experiments were conducted at Bushland, Texas in two growing seasons (2014–2015 and 2015–2016). Digital photographs were taken using a digital camera in each plot, while a manned aircraft collected images of the entire field using a 12-band multiple camera array Tetracam system at three growth stages (tillering, jointing and heading). Results showed that a significant variation exists in SVI, %GC, aboveground biomass and yield among the wheat genotypes mostly at tillering and jointing. Significant relationships for %GC from digital photo at jointing was recorded with Normalized Difference Vegetation Index (NDVI) at tillering (coefficient of determination, R² = 0.84, p< 0.0001) and with %GC estimated from Perpendicular Vegetation Index (PVI) at tillering (R² = 0.83, p< 0.0001). Among the indices, Ratio Vegetation Index (RVI), Green-Red VI, Green Leaf Index (GLI), Generalized DVI (squared), DVI, Enhanced VI, Enhanced NDVI, and NDVI explained 37–99% of the variability in aboveground biomass and yield. Results indicate that these indices could be used as an indirect selection tool for screening a large number of early-generation and advanced wheat lines.     

Autumn leaf phenology: discrepancies between in situ observations and satellite data at urban and rural sites

ABSTRACT

Autumn phenophases, such as leaf colouration (LC) and leaf fall (LF), have received considerably less attention than their spring counterparts (budburst and leaf unfolding) but are equally important determinants of the duration of the growing season and thus have a controlling in?uence on the carbon-uptake period. Here, we examined THE trends (1968–2016) in in situ observations of the timing of LC and LF from a suite of deciduous trees at three rural sites and one urban site in Ireland. Satellite-derived autumn phenological metrics including mid-senescence (MS) and end of senescence (ES) based on two-band enhanced vegetation index (EVI2) from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) from 1982 to 2016 at a spatial resolution of 5km² were also examined. The aim of this study was to assess the effectiveness of satellite remote sensing in capturing autumn phenology as determined by in situ observations . Analysis of in situ data (1968–2016) revealed the urban site to be significantly different from the rural sites as LC and LF occurred later in the season and the duration of the autumn season (LF-LC) became shorter over time. These trends may be partly driven by the presence of artificial light in the city. On average (1982–2016), there was a 6-day delay in the timing of MS compared to LC and a much larger difference (21 days) between ES and LF. This resulted in a 31-day autumn duration as defined by satellite data compared to 16 days from in situ observations. Furthermore, there was little overlap in timing between LC and MS, and LF and ES at the rural sites only. Discrepancies between in situ and satellite data may be attributed to the satellite data integrating a much broader vegetation signal across a heterogeneous landscape than in situ observations of individual trees. Therefore, at present, satellite-derived autumn phenology may be more successful in capturing in situ observations across large homogeneous landscapes of similar vegetation types (e.g. forested areas) than in heterogeneous landscapes (e.g. small mixed farms, urban areas, etc.) as is the case in Ireland where the in situ observations of trees may not be reflective of the overall vegetation. Matching the scale of satellite data with in situ observations remains a challenging task but may, at least in part, be overcome by increasing the extent of observations to include a wider range of species and in future as satellite data become available at higher spatial and temporal resolutions.     

The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas

ABSTRACT

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter α_s. In addition to the parameter α_s from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR’2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9–12 and 9–15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters α_s and χ for ε_s > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season.     

Effect of leaf and spike morphological traits on the relationship between spectral reflectance indices and yield in wheat

While certain spectral reflectance indices have been shown to be sensitive to the expression of a range of performance-related traits in crops, knowledge of the potentially confounding effects associated with plant anatomy could help improve their application in phenotyping. Morphological traits (leaf and spike wax content, leaf and spike orientation, and awns on spikes) were studied in 20 contrasting advanced wheat lines to determine their influence on spectral indices and in their association with grain yield under well-irrigated conditions. Canopy reflectance (400–1100 nm) was determined at heading and grain filling during two growing seasons and three vegetation indices (VIs; red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), and simple ratio (SR)), and five water indices (WIs; one simple WI and four normalized WIs (NWI-1, NWI-2, NWI-3, and NWI-4)) were calculated. The major reflectance fluctuations caused by the differences in leaf and spike morphology mainly occurred in the infrared region (700–1100 nm) and little variation in the visible region (400–700 nm). The NWI-3 ((R₉₇₀ – R₈₈₀)/(R₉₇₀ + R₈₈₀)) consistently showed a stronger association with yield than the RNDVI by using uncorrected canopy reflectance (original raw data) and data adjusted by scattering and smoothing. When canopy reflectance was corrected by a scattering method, the NWI-3 and a modified RNDVI with 958 nm showed the strongest correlations with grain yield by grouping lines for waxy leaves and spikes, curved leaves, and erect and awnless spikes. The results showed that the relationship between the spectral indices and grain yield can be improved (higher correlations) by correcting canopy reflectance for confounding effects associated with differences in leaf and spike morphology.     

The influence of land-cover type on the relationship between NDVI–LST and LST-Tair

Air temperature (T_2m or T_air) measurements from 20 ground weather stations in Berlin were used to estimate the relationship between air temperature and the remotely sensed land surface temperature (LST) measured by Moderate Resolution Imaging Spectroradiometer over different land-cover types (LCT). Knowing this relationship enables a better understanding of the magnitude and pattern of Urban Heat Island (UHI), by considering the contribution of land cover in the formation of UHI. In order to understand the seasonal behaviour of this relationship, the influence of the normalized difference vegetation index (NDVI) as an indicator of degree of vegetation on LST over different LCT was investigated. In order to evaluate the influence of LCT, a regression analysis between LST and NDVI was made. The results demonstrate that the slope of regression depends on the LCT. It depicts a negative correlation between LST and NDVI over all LCTs. Our analysis indicates that the strength of correlations between LST and NDVI depends on the season, time of day, and land cover. This statistical analysis can also be used to assess the variation of the LST–T_2m relationship during day- and night-time over different land covers. The results show that LST_Day and LST_Night are correlated significantly (p = 0.0001) with T_2mDay (daytime air temperature) and T_2mNight (night-time air temperature). The correlation (r) between LST_Day and T_Day is higher in cold seasons than in warm seasons. Moreover, during cold seasons over every LCT, a higher correlation was observed during daytime than during night-time. In contrast, a reverse relationship was observed during warm seasons. It was found that in most cases, during daytime and in cold seasons, LST is lower than T_2m. In warm seasons, however, a reverse relationship was observed over all land-cover types. In every season, LST_Night was lower than or close to T_2mNight.