Modelling gross primary production in semi-arid Inner Mongolia using MODIS imagery and eddy covariance data

We evaluate the modelling of carbon fluxes from eddy covariance (EC) tower observations in different water-limited land-cover/land-use (LCLU) and biome types in semi-arid Inner Mongolia, China. The vegetation photosynthesis model (VPM) and modified VPM (MVPM), driven by the enhanced vegetation index (EVI) and land-surface water index (LSWI), which were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface-reflectance product (MOD09A1), were used to model and validate the temporal changes in gross primary production (GPP) from the EC towers during the 2006 and 2007 growing seasons. The annual GPP predicted by the VPM model (GPP_VPM) was predicted reasonably well in 2006 and 2007 at the cropland (coefficient of determination, R ² = 0.67 and 0.71, for 2006 and 2007, respectively) and typical steppe (R ² = 0.80 and 0.73) sites. The predictive power of the VPM model varied in the desert steppe, which includes an irrigated poplar stand (R ² = 0.74 and 0.68) and shrubland (R ² = 0.31 and 0.49) sites. The comparison between GPP obtained from the eddy covariance tower (GPP_tower) and GPP obtained from MVPM (GPP_MVPM) (predicted GPP) showed good agreement for the typical steppe site of Xilinhaote (R ² = 0.84 and 0.70 in 2006 and 2007, respectively) and for the Duolun steppe site (R ² = 0.63) and cropland site (R ² = 0.63) in 2007. The predictive power of the MVPM model decreased slightly in the desert steppe at the irrigated poplar stand (R ² = 0.56 and 0.47 in 2006 and 2007 respectively) and the shrubland (R ² = 0.20 and 0.41). The results of this study demonstrate the feasibility of modelling GPP from EC towers in semi-arid regions.     

Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems

One of the most frequently applied methods for integrating controls on primary production through satellite data is the light use efficiency (LUE) approach, which links vegetation gross or net primary productivity (GPP or NPP) to remotely sensed estimates of absorbed photosynthetically active radiation (APAR). Eddy covariance towers provide continuous measurements of carbon flux, presenting an opportunity for evaluation of satellite estimates of GPP. Here we investigate relationships between eddy covariance estimated GPP, environmental variables derived from flux towers, Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and GPP across African savanna ecosystems. MODIS GPP was found to underestimate GPP at the majority of sites, particularly at sites in the Sahel. EVI was found to correlate well with estimated GPP on a site-by-site basis. Combining EVI with tower-measured PAR and evaporative fraction (EF, a measure of water sufficiency) improved the direct relationship between GPP and EVI at the majority of the sites. The slope of this relationship was strongly related to site peak leaf area index (LAI). These results are promising for the extension of GPP through the use of remote sensing data to a regional or even continental scale.     

Effects of forest type and environmental factors on forest carbon use efficiency assessed using MODIS and FIA data across the eastern USA

The carbon use efficiency (CUE) of a forest, calculated as the ratio of net primary productivity (NPP) to gross primary productivity (GPP), measures how efficiently a forest sequesters atmospheric carbon. Some prior research has suggested that CUE varies with environmental conditions, while other suggests that CUE is constant. Research using Moderate Resolution Imaging Spectroradiometer (MODIS) data has indicated a variable CUE, but those results are suspected because MODIS NPP data have not been well validated.

We tested two questions. First, whether MODIS CUE is constant or whether it varies by forest type, climate, and geographic factors across the eastern USA. Second, whether those results occur when field-based NPP data are employed. We used MODIS model-based estimates of GPP and NPP, and forest inventory and anlaysis (FIA) field-based estimates of NPP data. We calculated two estimates of CUE for forest in 390 km² hexagons: (1) MODIS CUE as MODIS NPP divided by MODIS GPP and (2) F/M Z_CUE as the standardized difference between FIA NPP and MODIS GPP.

MODIS CUE and F/M Z_CUE both varied similarly and significantly in relation to forest type, and climatic and geographic factors, strongly supporting a variable rather than a constant CUE. The CUE was significantly higher in deciduous than in mixed and evergreen forests. Regression models indicated that CUE decreased with increases in temperature and precipitation and increased with latitude and altitude. The similar trends in MODIS CUE and F/M Z_CUE support the use of the more easily obtained MODIS CUE.     

干旱对灌溉和雨养农田生态系统生产力的影响对比分析

灌溉是农作物应对干旱等极端气候条件的有效调节机制,在全球气候变化的背景下,未来干旱等极端气候事件发生的频率和严重程度预估会增加,定量分析灌溉和雨养条件下干旱对农田生态系统农作物生长的影响有助于更好地评估人类应对极端气候事件对生态系统的消极影响的能力,为制定合理有效的生态系统保护措施提供依据.以中国北方干旱区为研究区,基...     

Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach

Remote sensing is a potentially powerful technology with which to extrapolate eddy covariance-based gross primary production (GPP) to continental scales. In support of this concept, we used meteorological and flux data from the AmeriFlux network and Support Vector Machine (SVM), an inductive machine learning technique, to develop and apply a predictive GPP model for the conterminous U.S. In the following four-step process, we first trained the SVM to predict flux-based GPP from 33 AmeriFlux sites between 2000 and 2003 using three remotely-sensed variables (land surface temperature, enhanced vegetation index (EVI), and land cover) and one ground-measured variable (incident shortwave radiation). Second, we evaluated model performance by predicting GPP for 24 available AmeriFlux sites in 2004. In this independent evaluation, the SVM predicted GPP with a root mean squared error (RMSE) of 1.87 gC/m²/day and an R² of 0.71. Based on annual total GPP at 15 AmeriFlux sites for which the number of 8-day averages in 2004 was no less than 67% (30 out of a possible 45), annual SVM GPP prediction error was 32.1% for non-forest ecosystems and 22.2% for forest ecosystems, while the standard Moderate Resolution Imaging Spectroradiometer GPP product (MOD17) had an error of 50.3% for non-forest ecosystems and 21.5% for forest ecosystems, suggesting that the regionally tuned SVM performed better than the standard global MOD17 GPP for non-forest ecosystems but had similar performance for forest ecosystems. The most important explanatory factor for GPP prediction was EVI, removal of which increased GPP RMSE by 0.85 gC/m²/day in a cross-validation experiment. Third, using the SVM driven by remote sensing data including incident shortwave radiation, we predicted 2004 conterminous U.S. GPP and found that results were consistent with expected spatial and temporal patterns. Finally, as an illustration of SVM GPP for ecological applications, we estimated maximum light use efficiency (e_max), one of the most important factors for standard light use efficiency models, for the conterminous U.S. by integrating the 2004 SVM GPP with the MOD17 GPP algorithm. We found that e_max varied from ∼ 0.86 gC/MJ in grasslands to ∼ 1.56 gC/MJ in deciduous forests, while MOD17 e_max was 0.68 gC/MJ for grasslands and 1.16 gC/MJ for deciduous forests, suggesting that refinements of MOD17 e_max may be beneficial.     

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.     

The effects of aggregated land cover data on estimating NPP in northern Wisconsin

Ecosystem models are routinely used to estimate net primary production (NPP) from the stand to global scales. Complex ecosystem models, implemented at small scales (< 10 km²), are impractical at global scales and, therefore, require simplifying logic based on key ecological first principles and model drivers derived from remotely sensed data. There is a need for an improved understanding of the factors that influence the variability of NPP model estimates at different scales so we can improve the accuracy of NPP estimates at the global scale. The objective of this study was to examine the effects of using leaf area index (LAI) and three different aggregated land cover classification products-two factors derived from remotely sensed data and strongly affect NPP estimates-in a light use efficiency (LUE) model to estimate NPP in a heterogeneous temperate forest landscape in northern Wisconsin, USA. Three separate land cover classifications were derived from three different remote sensors with spatial resolutions of 15, 30, and 1000 m. Average modeled net primary production (NPP) ranged from 402 gC m^− 2 year^− 1 (15 m data) to 431 gC m^− 2 year^− 1 (1000 m data), for a maximum difference of 7%. Almost 50% of the difference was attributed each to LAI estimates and land cover classifications between the fine and coarse scale NPP estimate. Results from this study suggest that ecosystem models that use biome-level land cover classifications with associated LUE coefficients may be used to model NPP in heterogeneous land cover areas dominated by cover types with similar NPP. However, more research is needed to examine scaling errors in other heterogeneous areas and NPP errors associated with deriving LAI estimates.     

Changes in net primary production in the Tianmu Mountain Nature Reserve,China, from 1984 to 2014

Nature reserve establishment can lead to conflict with some stakeholders. Zoning management is useful to mitigate against the conflict between human development and nature reserves, and a nature reserve can be divided into three zones: the core zone, buffer zone, and experimental zone. So far, how to monitor and evaluate the effectiveness of zoning management in nature reserves is a problem faced by remote sensing scientists and ecologists. Net primary productivity (NPP) is a key indicator which can be used to monitor and evaluate the effectiveness of zoning management in nature reserves. However, to date there has been no research on the effectiveness of zoning management on NPP, and the estimation of NPP in the Tianmu Mountain Nature Reserve also has not been studied. Based on remote sensing data and in situ measurements, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate NPP in the Tianmu Mountain Nature Reserve during the period 1984–2014. We used the observed NPP to verify the simulated NPP, and the results show that the simulated NPP was consistent with the observed NPP (R² ≥ 0.85, p ≤ 0.0002, RMSE = 52.62 g C m^?2 year^?1, where R² represents coefficient of determination, p represents statistical significance, and RMSE represents root mean square error). This means that the CASA model is suitable for NPP estimation in the Tianmu Mountain Nature Reserve. The results also indicate that NPP showed an increasing trend during the period 1984–2014, and the increase over the whole period was 6.66%. The total of the annual averaged NPP was 3.07 × 10¹⁰ g C year^?1, while the annual averaged NPP per unit area was 708 g C m^?2 year^?1. The largest averaged annual NPP per unit appeared in the core zone (720 g C m^?2 year^?1), followed by the buffer zone (711 g C m^?2 year^?1), with the experimental zone having the smallest averaged annual NPP per unit (706 g C m^?2 year^?1). At the p < 0.1 level, there was no region where NPP had decreased significantly in the core zone and buffer zone, and the area of the regions where NPP had decreased significantly in the experimental zone was 8.04 ha. At the p < 0.05 level, there was no area where NPP had decreased significantly in the three zones of the Tianmu Mountain Nature Reserve. The results show that the zoning management on NPP was effective in the Tianmu Mountain Nature Reserve.     

Estimating carbon and showing impacts of drought using satellite data in regression-tree models

Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients (r) (r ≥ 94%) between training and estimated data for both GPP and RE. Overall, average annual GPP, RE, and NEP are relatively constant throughout the study period except during 2012 when almost 60% less carbon is sequestered than normal. These results allow us to conclude that this modelling method effectively estimates carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.     

An assessment of satellite-based agricultural water productivity over the Indian region

The preliminary analysis of agricultural water productivity (AWP) over India using satellite data were investigated through productivity mapping, water use (actual evapotranspiration (ET_a)/effective rainfall (R_eff) mapping and water productivity mapping. Moderate Resolution Imaging Spectroradiometer data was used for generating agricultural land cover (MCD12Q1 at 500 m), gross primary productivity (GPP; MOD17A2 at 1 km), and ET_a (MOD16A2 at 1 km). R_eff was estimated at 10 km using the United States Department of Agriculture soil conservation service method from daily National Oceanic and Atmospheric Administration Climate Prediction Center rainfall data. Six years’ (2007–2012) data were analysed from June to October. The seasonal AWP and rainwater productivity (RWP) were estimated using the ratios of seasonal GPP (kg C m^?2) and water use (mm) maps. The average AWP and RWP ranges from 1.10–1.30 kg Cm^?3 and 0.94–1.0 kg C m^?3, respectively, with no significant annual variability but a wide spatial variability over India. The highest AWP was observed in northern India (1.22–1.80 kg C m^?3) and lowest in western India (0.81–1.0 kg C m^?3). Large variations in AWP (0.69–1.80 kg C m^?3) were observed in Himachal Pradesh, Jammu and Kashmir, northeastern states (except Assam), Kerala, and Uttaranchal. The low GPP of these areas (0.0013–0.13 kg C m^?2) with low seasonal total ET_a (<101 mm) and R_eff (<72 mm) making the AWP high that do not correspond to high productivity but possible water stress. Gujarat, Rajasthan, Maharashtra, Madhya Pradesh, Jharkhand, and Karnataka showed low AWP (0.73–1.13 kg C m^?3) despite having high ET_a (261–558 mm) and high R_eff (287–469 mm), indicating significant scope for improving productivity. The highest RWP was observed in northern parts and Indo-Gangetic plains (0.80–1.6 kg C m^?3). The 6 years’ analysis reveals the status of AWP, leading to appropriate interventions to better manage land and water resources, which have great importance in global food security analysis.     

Spectral indices for remote sensing of phytomass,deciduous shrubs,and productivity in Alaskan Arctic tundra

The relationships among in situ spectral indices, phytomass, plant functional types, and productivity were determined through field observations of moist acidic tundra (MAT), moist non-acidic tundra (MNT), heath tundra (HT), and sedge-shrub tundra (SST) in the Arctic coastal tundra, Alaska, USA. The two-band enhanced vegetation index (EVI2) was found more useful for estimating vascular plant green phytomass, leaf carbon and nitrogen, leaf carbon and nitrogen turnover, and vascular plant net primary productivity (NPP) without root production than the normalized difference vegetation index (NDVI). Deciduous shrub green phytomass was strongly correlated with deciduous shrub index (DSI), defined as EVI2 × (R_blue + R_green – R_red)/(R_blue + R_green + R_red) (with a coefficient of determination (R²) of 0.63). R_blue, R_green, and R_red denote the blue, green, and red bands, respectively. This is because R_blue and R_green values were higher than the R_red values for green leaves, deciduous shrub stems, lichens, and rocks compared with other ecosystem components, and EVI2 values of lichens and rocks were very low. The vascular plant NPP without root production was estimated with an R² of 0.67 using DSI and EVI2. Our results offer empirical evidence that a new spectral index predicts the distribution of deciduous shrub and plant production, which influences the interactions between tundra ecosystems and the atmosphere.     

Remote sensing used to detect moisture status of pecan orchards grown in a desert environment

Remote-sensing techniques can detect and up-scale leaf-level physiological responses to large areas, and provide significant and reliable information on water use and irrigation management. The objectives of this study were to screen leaf-level physiological changes that occur during the cyclic irrigation of pecan orchards to determine which responses best represent changes in moisture status of plants and link plant physiological changes to remotely sensed surface reflectance data derived from the Landsat Thematic Mapper and Enhanced Thematic Mapper Plus (ETM+). The study was conducted simultaneously on two southern New Mexico mature pecan orchards. For both orchards, plant physiological responses and remotely sensed surface reflectance data were collected from trees that were either well watered or in water deficit. Remotely sensed variables included reflectance in band 1, the ratio between shortwave infrared (SWIR) bands (B5:B7), the normalized difference vegetation index, and SWIR moisture indices. Midday stem water potential (Ψ_smd) was the best performing leaf-level physiological response variable for detecting moisture status in pecans. The B5:B7 ratio positively and significantly correlated with Ψ_smd in five of six irrigation cycles while multiple linear regression weighted with six remotely sensed surface reflectance variables revealed a significant relationship with moisture status in all cycles in both orchards (R² > 0.73). Because changes in the B5:B7 band ratio and multiple regression of spectral variables correlate with the moisture status of pecan orchards, we conclude that remotely sensed data hold promise for detecting the moisture status of pecans.     

Can EVI-derived land-surface phenology be used as a surrogate for phenology of canopy photosynthesis?

Canopy phenology plays a prominent role in determining the timing and magnitude of carbon uptake by many ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) Global Land Cover Dynamics product developed from the enhanced vegetation index (EVI) provides broad spatial and temporal coverage of land-surface phenology (LSP), and may serve as a useful proxy for the phenology of canopy photosynthesis. Here, we compare the MODIS growing season start and end dates (SOS and EOS) with the seasonal phenology of canopy photosynthesis estimated using the eddy covariance approach. Using 153 site-years obtained from the Ameriflux database, we calculated the SOS and EOS of gross primary production (GPP) and canopy photosynthesis capacity (CPC) for seven different boreal and temperate vegetation types. CPC is GPP at maximum radiation, estimated by fitting half-hourly GPP and radiation to a rectangular hyperbolic function. We found large mean absolute differences of up to 53 days, depending on vegetation type, between the phenology of canopy development and photosynthesis, indicating that remotely sensed LSP is not a robust surrogate of seasonal changes in GPP, particularly for evergreen needleleaf forests. This limited correspondence of ecosystem carbon uptake with the MODIS LSP product points to the need for improved remotely sensed proxies of GPP phenology.     

Spatial–temporal dynamics of gross primary productivity,evapotranspiration, and water-use efficiency in the terrestrial ecosystems of the Yangtze River Delta region and their relations to climatic variables

Moderate Resolution Imaging Spectroradiometer (MODIS) products and climate data collected from meteorological stations were used to characterize the spatial–temporal dynamics of gross primary productivity (GPP), evapotranspiration (ET), and water-use efficiency (WUE) in the Yangtze River Delta (YRD) region and the response of these three variables to meteorological factors. The seasonal patterns of GPP and WUE showed a bimodal distribution, with their peak values occurring in May and August, and April and October, respectively. By contrast, the seasonal variation of ET presented a unimodal pattern with its maximum in July or August. The spatial distribution of ET and GPP was similar to higher values occurring in the south. From 2001 to 2012, GPP in the eastern YRD decreased, while GPP in the western part increased. In comparison, over the 12 years, ET in the northern part of YRD decreased, while ET in the southern part increased. The spatial distribution and spatial variation of WUE were both similar to those of GPP. This implies that the changes in WUE are primarily controlled by the variations in GPP. The annual average WUE over vegetation types followed the order of: evergreen broadleaf forest (1.95 g C kg^?1 H₂O) > deciduous broadleaf forest (1.87 g C kg^?1 H₂O) > evergreen needle leaf forest (1.70 g C kg^?1 H₂O) > deciduous needle leaf forest (1.68 g C kg^?1 H₂O) > grassland (1.66 g C kg^?1 H₂O) > cropland (1.61 g C kg^?1 H₂O). Both GPP and ET increased with increasing annual mean temperature (T_a) and annual mean precipitation across all of the plant function types. WUE decreased as vapour pressure deficit (VPD) increased in all of the biomes. Interestingly, the relationship between WUE and VPD was the most significant in broadleaf forest. Whether this phenomenon is universal should be investigated in future studies.     

Spatiotemporal crop NDVI responses to climatic factors in mainland China

Climate change has caused a great impact on vegetation growth, production and distribution through variations of precipitation, temperature and sunshine. In this study, a categorization of zones for vegetation responses to climatic variability was conducted. Seasonal and annual crop responses to climate change in each region were analysed with multiple linear regression. The results show that the annual impact of climatic factors on crop growth was most significant in lower North China (R² = 0.48) and most insignificant in Northeast China (R² = 0.22). Temperature is the limiting climatic factor for crop growth annually in North China and Northeast China (zones 1–3), (p ≤ 0.05), while sunshine duration plays an important role for crop growth in zones which are more southern (zones 3 ~ 5). Precipitation significantly affects the annual crop growth in Inner Mongolia-Hebei-Shandong zone (zone 2) and Southeast zone (zone 5). Therefore, more attention should be paid to these zones. The spring temperature is the limiting climatic factor for crop growth in all the zones (p ≤ 0.05). Spring warming is helpful for crop growth in mainland China. Different agricultural and administrative measures should be taken in each zone to adapt to future climate change.     

Evaluating the impact of adjusting surface temperature derived from Landsat 7 ETM+ in crop evapotranspiration assessment using high-resolution airborne data

Surface temperature (T_s) is an essential parameter in many land surface processes. When T_s is obtained from remotely sensed satellite data the consideration of atmospheric correction may be needed to obtain accurate surface temperature estimates. Most atmospheric correction methods adjust atmospheric transmissivity, path radiance and downward thermal radiation coefficients. Following a standardized atmospheric correction of Landsat 7 thermal data, some differences were found between these corrected data and surface temperature derived from very-high resolution airborne thermal data. Five different methods for determining atmospheric correction were evaluated comparing atmospherically corrected Landsat 7 data with airborne data for an area of olive orchards located at Southern Spain. When using standard default Landsat 7 calibration coefficients T_s differences between satellite and airborne observations ranged from 1 to 6 K, highlighting the need to perform more robust atmospheric correction. When applying the customized values for semi-arid temperate climate in Idaho, USA, and the values based on the National Centers for Environmental Prediction (NCEP) T_s differences ranged from 1 to 4 K, indicating that additional local calibration may be appropriate. Optimal coefficients were determined using the Generalized Reduced Gradient (GRG) approach, a nonlinear algorithm included in Solver tool, obtaining T_s differences around 1–3 K. In order to evaluate the impact of considering the proposed correction approaches, assessment of the evapotranspiration and crop coefficient values derived from the Mapping Evapotranspiration with Internalized Calibration (METRIC) energy balance model provided maximum errors of around 4%, indicating that the METRIC model does not require a robust atmospheric correction. However, the localized calibration approaches are proposed as useful alternatives when absolute land surface temperatures values are required, as in the case of the determination of crop water stress based on differences between canopy (T_c) and air temperature (T_air).     

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.     

Estimate of net primary productivity of aquatic vegetation of the Amazon floodplain using Radarsat and JERS‐1

Synthetic aperture radar images, combined with field measurements, were used to estimate net primary productivity (NPP) of aquatic vegetation in the lower Amazon. Input data for a NPP model are (i) the total biomass of aquatic vegetation, determined by radar imagery and field measurements and (ii) the area occupied by aquatic vegetation, determined from radar imagery. After correction for monthly biomass losses, the NPP of one growth cycle of aquatic vegetation was calculated in the image domain. The total net primary productivity of Hymenachne amplexicaules, the dominant aquatic vegetation in the area, was on average 19×10¹¹ g C yr^?1 for the entire area. Spatially, lower values of produced organic carbon (<900 g C m^?2 yr^?1) are confined to regions where the plants developed only in the beginning of the rising phase of the hydrological cycle. In general, values are higher (>5000 g C m^?2 yr^?1) in areas closer to the Amazon River where the availability and influence of nutrient‐rich water is greater.     

A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US

We present a dryland irrigation mapping methodology that relies on remotely sensed inputs from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument, globally extensive ancillary sources of gridded climate and agricultural data and on an advanced image classification algorithm. The methodology involves four steps. First, we use climate-based indices of surface moisture status and a map of cultivated areas to generate a potential irrigation index. Next, we identify remotely-sensed temporal and spectral signatures that are associated with presence of irrigation defined as full or partial artificial application of water to agricultural areas under dryland conditions excluding irrigated pastures, paddy rice fields, and other semiaquatic crops. Here, the temporal indices are based on the difference in annual evolution of greenness between irrigated and non-irrigated crops, while spectral indices are based on the reflectance in the green and are sensitive to vegetation chlorophyll content associated with moisture stress. Third, we combine the climate-based potential irrigation index, remotely sensed indices, and learning samples within a decision tree supervised classification tool to make a binary irrigated/non-irrigated map. Finally, we apply a tree-based regression algorithm to derive the fraction of irrigated area within each pixel that has been identified as irrigated. Application of the proposed procedure over the continental US in the year 2001 produces an objective and comprehensive map that exhibits expected patterns: there is a strong east-west divide where the majority of irrigated areas is concentrated in the arid west along dry lowland valleys. Qualitative assessment of the map across different climatic and agricultural zones reveals a high quality product with sufficient detail when compared to existing large area irrigation databases. Accuracy assessment indicates that the map is highly accurate in the western US but less accurate in the east. Comparison of area estimates made with the new procedure to those reported at the state and county levels shows a strong correlation with a small bias and an estimated RMSE of 2500 km², or little over 2% of the total irrigated area in the US. As a result, the future application of the new procedure at a global scale is promising but may require a better potential irrigation index, as well as the use of remotely sensed skin temperature measurements.     

Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security,determined using MODIS 250 m time-series data and spectral matching techniques,in the Krishna River basin (India)

The objective of this study was to investigate the changes in cropland areas as a result of water availability using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series data and spectral matching techniques (SMTs). The study was conducted in the Krishna River basin in India, a very large river basin with an area of 265 752 km² (26 575 200 ha), comparing a water-surplus year (2000–2001) and a water-deficit year (2002–2003). The MODIS 250 m time-series data and SMTs were found ideal for agricultural cropland change detection over large areas and provided fuzzy classification accuracies of 61–100% for various land‐use classes and 61–81% for the rain-fed and irrigated classes. The most mixing change occurred between rain-fed cropland areas and informally irrigated (e.g. groundwater and small reservoir) areas. Hence separation of these two classes was the most difficult. The MODIS 250 m-derived irrigated cropland areas for the districts were highly correlated with the Indian Bureau of Statistics data, with R ²-values between 0.82 and 0.86.

The change in the net area irrigated was modest, with an irrigated area of 8 669 881 ha during the water-surplus year, as compared with 7 718 900 ha during the water-deficit year. However, this is quite misleading as most of the major changes occurred in cropping intensity, such as changing from higher intensity to lower intensity (e.g. from double crop to single crop). The changes in cropping intensity of the agricultural cropland areas that took place in the water-deficit year (2002–2003) when compared with the water-surplus year (2000–2001) in the Krishna basin were: (a) 1 078 564 ha changed from double crop to single crop, (b) 1 461 177 ha changed from continuous crop to single crop, (c) 704 172 ha changed from irrigated single crop to fallow and (d) 1 314 522 ha changed from minor irrigation (e.g. tanks, small reservoirs) to rain-fed. These are highly significant changes that will have strong impact on food security. Such changes may be expected all over the world in a changing climate.