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.     

Broad‐scale increase in NPP quantified for the African Sahel, 1982–1999

In association with a recently discovered greening trend in the Sahel, several interesting new perspectives have appeared in the literature regarding its climate and ecology. In this Letter, satellite data from 1982 to 1999 and a light use efficiency model are used to map net primary production (NPP) increases throughout the Sahel (total area of 1.13×10¹³ m²). A patchy, east‐west band of increasing NPP is identified, with several hotspots showing large increases. The total rate of NPP change for the Sahel is estimated to be 51.0 Mt C year^?1 over the 18‐year period, yielding an absolute net gain of 918.0 Mt C. This increase is associated with a decrease in the inter‐annual variability of NPP for the 1990s compared to the 1980s. These results lay the groundwork for untangling the effects of direct, localised human impact and climate forcing on land cover by conducting model intercomparison experiments, contextualizing the role the Sahel may play in the tropical carbon cycle, and for reducing the uncertainty regarding Sahelian carbon sequestration.     

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.     

Estimation of net primary productivity by integrating remote sensing data with an ecosystem model

This paper describes a method for integrating leaf area index (LAI) derived from remote sensing data with an ecosystem model for accurate estimation of net primary productivity (NPP). The ecosystem model used in this study was Sim-CYCLE, with which LAI retrieved from the data acquired by MODIS sensor (MODIS-LAI) was integrated. Global annual NPP was estimated as 59.6 Gt C year⁻¹ by MOD-Sim-CYCLE (Sim-CYCLE after integration of MODIS-LAI), whereas it was 62.7 Gt C year⁻¹ in case of Sim-CYCLE for the year 2001. Both models predicted highest NPP around the equator with another smaller peak occurring around 60°N. These two regions represented the tropical and boreal forests biomes, respectively. The NPP estimated by MOD-Sim-CYCLE exceeded the NPP estimated by Sim-CYCLE in these two regions. Other than the tropical and boreal forests biomes, NPP values estimated by the MOD-Sim-CYCLE were typically lower than Sim-CYCLE across the latitudes. Validations of results in Australia and USA showed that MOD-Sim-CYCLE estimated NPP more accurately than Sim-CYCLE. Our results demonstrate the utility of combining satellite-observation with an ecosystem process model to achieve improved accuracy in estimates and monitoring global net primary productivity.     

On the upstream inputs into the MODIS primary productivity products using biometric data from oil palm plantations

This study evaluated the influence of upstream inputs into the Moderate Resolution Imaging Spectroradiometer (MODIS) primary productivity products, termed the MOD17, at tropical oil palm plantations (Elaeis guineensis Jacq.). Evaluation of MOD17 using oil palm plantations as test sites is ideal because the plantations are cultivated on large areas which are comparable with the size of MODIS pixels. It is difficult to find test sites covered by other single species in a whole pixel. The upstream inputs studied included (1) MODIS land cover, (2) the National Centers for Environmental Prediction–Department of Energy (NCEP-DOE) Reanalysis 2 meteorological data set, (3) MODIS leaf area index/fraction of photosynthetically active radiation (LAI/fPAR), and (4) MODIS maximum light-use efficiency (maximum LUE). Oil palm biometric and local meteorological data were utilized as ground data. Furthermore, scaling up oil palm LAI and fPAR from plot scale to regional scale (Peninsular Malaysia) was done empirically by correlating oil palm LAI derived from the hemispherical photography technique with radiance information from the Disaster Monitoring Constellation 2 satellite (UK-DMC 2). The upscaled LAI/fPAR developed in this study was used to evaluate the MODIS LAI/fPAR. The results showed that the MODIS land-cover product has an overall accuracy of 78.8% when compared to the Peninsular Malaysia land-use map produced by the Department of Agriculture, Malaysia. Regarding the NCEP-DOE Reanalysis 2 data set, vapour pressure deficit (VPD) and photosynthetically active radiation (PAR) contain large uncertainties in our study area. However, MODIS LAI and fPAR were correlated relatively well with the upscaled LAI (R² = 0.50) and the upscaled fPAR (R² = 0.60), respectively. The constant values of maximum LUE for croplands and evergreen broadleaf forest ecosystems are lower than the maximum LUE of oil palm. The relative predictive error assessment showed that the MOD17 net primary productivity (NPP) overestimated oil palm NPP derived from biometric methods by 142–204%. We replaced the upstream inputs of MOD17 by the local inputs for estimating oil palm GPP and NPP in Peninsular Malaysia. This was done by (1) assigning maximum LUE for oil palm plantations as a constant at 1.68 g C m^?2 day^?1, (2) utilizing meteorological data from local meteorological stations, and (3) using the upscaled fPAR of oil palm plantations. The amount of oil palm GPP and NPP for Peninsular Malaysia in 2010 were estimated to be ~0.09 Pg C year^?1 (or equivalent to ~0.33 Pg CO₂ year^?1) and ~0.03 Pg C year^?1 (~0.11 Pg CO₂ year^?1), respectively, indicating that oil palm plantations in Peninsular Malaysia can play an important role in global carbon sequestration. In the future there is likely to be a demand for MODIS GPP and NPP products that are more accurate than those currently generated by MOD17. We recommend future developments of the MOD17 processing system to allow improvements in the upstream input parameters, in the manner described in this article, both for global processing and for the production of more accurate values for GPP and NPP at regional and local scales.     

A new approach to evaluate the MODIS annual NPP product (MOD17A3) using forest field data from Turkey

In this study, we present the first evaluation of the MODIS (Moderate Resolution Imaging Spectroradiometer) annual net primary production (NPP) for Turkey’s forest ecosystems using field measurements. Due to lack of country scale field measurements (i.e. flux tower for forest ecosystems), tree DBH (diameter at breast height) data set provided by Ministry of Forest and Water Affairs (MFWA) of Turkey is used to calculate NPP of Turkey’s forest ecosystems. The lack of a reliable NPP data set leads the researchers to use global NPP models such as MODIS annual NPP product. The MODIS MOD17A3 product of vegetation NPP is one of the most highly used data sources for studies of global carbon cycle. However, it is still necessary to test its predictions in multiple biomes, especially for heterogeneous areas in terms of its accuracy and potential bias. Here, we studied a new approach to evaluate coarse scale NPP estimates from the MODIS NPP-MOD17A3 data product, using 2008–2013 field measurements of tree growth throughout Turkey. Three different methods were used to calculate field NPP, including standardized growth coefficients (ministry coefficients [MC]), growth coefficients from North America (Jenkins coefficients [JC]), and annual expected increment (AEI). The average NPP values for all the country is calculated as 2.06 kgC m^–1/5 years (0.412 kgC m^–2 year^–1) (SD = 1.15 kgC m^–1/5 years) from MOD17A3, 0.90 kgC m^–1/5 years (0.18 kgC m^–2 year^–1) (SD = 0.57 kgC m^–1/5 years) with MC, 0.63 kgC m^–1/5 years (0.126 kgC m^–2 year^–1) (SD = 0.37 kgC m^–1/5 years) with JC and 0.58 kgC m^–2 year^–1 (SD = 0.29 kgC m^–1/5 years) with AEI for the studied plots. We found that the MODIS NPP product has a clear relation with both the NPP estimates obtained by using MC (R² = 0.34, root mean square error (RMSE) = 1.51 kgC m^–1/5 years) and JC (R² = 0.32, RMSE = 1.73 kgC m^–1/5 years). In addition to that, the relation between MOD17A3 product and AEI-derived NPP is relatively strong (R² = 0.48, RMSE = 0.26 kgC m^–2 year^–1). We discuss possible reasons for these trade-offs among different methods. This study lays out a new approach to validate coarse scale MODIS product using field data directly, including for highly heterogeneous areas.     

Use of GRACE time-variable data and GLDAS-LSM for estimating groundwater storage variability at small basin scales: a case study of the Nzoia River Basin

This study integrates time-variable Gravity Recovery and Climate Experiment (GRACE) gravimetric measurements and Global Land Data Assimilation System (GLDAS) land surface models (LSM) in order to understand the inter-annual variations and groundwater storage changes (GWSC) in the Nzoia River Basin in Kenya, using the water balance equation and parameters. From averaged GRACE and GWSC data, the results showed that over the 10-year period, the basin experienced a groundwater depth gain of 6.38 mm year^?1, which is equivalent to aquifer recharge of 298 million cubic metres (mcm) year^?1. The deseasonalized groundwater variation analysis gave a net gain in groundwater storage of 6.21 mm year^?1 that is equal to a groundwater recharge gain of 290 mcm year^?1. The observed results are comparable to the groundwater safe yield of 330 mcm year^?1 as estimated by the Water Resource Management Authority in Kenya. Through cross-plotting and analysis with averaged satellite altimetry data and in situ measurements from rainfall and streamflow discharge, the total water storage change (TWSC) and GWSC in the basin were consistent and closely correlated in variation trends. The inter-annual standard deviation of groundwater change was determined as ±0.24 mm year^?1, which is equivalent to 85% degree of confidence in the obtained results. The results in this study show that GRACE gravity-variable solutions and GLDAS-LSM provide reliable data sets suitable for the study of small to large basin groundwater storage variations, especially in areas with scarce and sparsely available in situ data.     

Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands

Global demands for biomass and arable lands are expected to double in the next 35 years. Scarcity of water resources in arid and semi-arid areas poses a serious threat to their agricultural productivity and hence their food security. In this study, we examine whether crop yields can be predicted from remotely sensed vegetation indices and remotely sensed estimates of primary productivity. Spatial relationships between remotely sensed enhanced vegetation index (EVI), net photosynthesis (P_Net), and gross and net primary production (GPP and NPP, respectively) in irrigated semi-arid and arid agro-ecosystems since the beginning of the century are analysed. The conflict-affected country of Syria is selected as the case study. Relationships between EVI and crop yield are investigated in an effort to enhance food production estimates in affected areas outside governmental jurisdictions. Estimates of NPP derived from reported irrigated agriculture crop data in a semi-arid and an arid zone are compared to remotely sensed NPP in a geospatial environment. Results show that winter crop yields are correlated with spring GPP in semi-arid zones of the study area (R² = 0.85). Summer crop yield can be predicted from either cumulative summer EVI (R² = 0.77) or P_Net in most zones. Where fully irrigated fields are surrounded by hyper-arid landscape, summer P_Net was negative in all instances and EVI was inversely correlated with yield. NPP from crops was much higher (290 gC m^?2 year^?1) in those regions than MOD17 NPP (70 gC m^–2), where 1.0 g of carbon is equivalent to 2.2 g of oven-dry organic matter (= 45% carbon by weight). The gap was less in semi-arid zones (2–39% difference). Overall crop-derived NPP for the period 2000–2013 was 322 versus 300 gC m^–2 for that remotely sensed within the cropped zones of the political units. The results of this study are crucial to derive accurate estimates of irrigated agriculture productivity and to study the effect of the latter on net ecosystem carbon storage.     

Land deformation monitoring using coherent target-neighbourhood networking method combined with polarimetric information: a case study of Shanghai,China

In this article, an advanced approach for land deformation monitoring using synthetic aperture radar (SAR) interferometry combined with polarimetric information is presented. The linear and nonlinear components of the deformation, the error of the digital elevation model (DEM) and the atmospheric artefacts can be achieved by a coherent target (CT)-neighbourhood networking approach. In order to detect recent land deformation in Shanghai, China, 12 ENVISAT advanced synthetic aperture radar (ASAR) alternating polarization images acquired from January 2006 to August 2008 are employed for deformation analysis. Over a 2.5-year period, two deformation velocity fields from HH and VV modes over Shanghai are derived using the CT-neighbourhood networking SAR interferometry (InSAR), then integrated into a final deformation map by a fusion scheme. It is found that the annual subsidence rates in the study area range from??20 to 10 mm year^?1 and the average subsidence rate in the downtown area reaches??7.5 mm year^?1, which is consistent with the local government statistics published in 2007.     

Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China

Daily actual evapotranspiration over the upper Chao river basin in North China on 23 June 2005 was estimated based on the Surface Energy Balance Algorithm for Land (SEBAL), in which the parameterization schemes for calculating the instantaneous solar radiation and daily integrated radiation were improved by accounting for the variations in slope and azimuth of land surface and terrain shadow in mountainous areas. The evapotranspiration (ET) estimated from satellite data in this study for the whole watershed ranges from 0 mm to 7.3 mm day^?1 with a mean of 3.4 mm day^?1, which was validated by Penman–Monteith approaches for water body and paddy land. The comparison of ET estimates for a wide range of land cover types reflected distinct mechanisms of energy partition and water removal of various land cover types, showing differences in the spatial distribution pattern of ET, which could be not only the reflection but also the driving force of advection and local circulation that may violate the surface energy balance equation in the vertical direction. The spatial variation in daily solar radiation and ET estimates under the complex terrain of forest land were elaborated and evaluated by exploring the relationship between ET estimates and elevations for wood land and grass land. In addition, the utility and limitations of SEBAL's applicability to watersheds with various land cover types and complex terrain were analysed.     

Recent changes in the snout position and surface velocity of Gangotri glacier observed from space

Glacier mass variations have a direct impact on some of the key components of the global water cycle, including sea level rise and freshwater availability. Apart from being one of the largest Himalayan glaciers, Gangotri is one of the sources of water for the Ganges river, which has a considerable influence on the socioeconomic structure of a largely over-populated catchment area accounting for ～26% of India’s landmass. In this study, we present the most recent assessment of the Gangotri glacier dynamics, combining the use of interferometric techniques on synthetic aperture radar data and sub-pixel offset tracking on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. Results show that on average, the Gangotri glacier snout has receded at a rate of 21.3 ± 3 m year^?1 over a period of 6 years (2004–2010). While glacier surface velocity near the snout is estimated to be between 24.8 ± 2.3 and 28.9 ± 2.3 m year^?1, interior portions of the glacier recorded velocities in the range of 13.9 ± 2.3 to 70.2 ± 2.3 m year^?1. Further, the average glacier surface velocity in the northern (lower) portions (28.1 ± 2.3 m year^?1) is observed to be significantly lower than in the southern (higher) portions (48.1 ± 2.3 m year^?1) of the Gangotri glacier. These values are calculated with an uncertainty of less than 5 m year^?1. Results also highlight a consistent retreat and non-uniform dynamics of the Gangotri glacier.     

Application of multitemporal Landsat data to monitor land cover changes in the Eastern Nile Delta region,Egypt

Due to the progressive increase in development of desert land in Egypt, the demand for efficient and accurate land cover change information is increasing. In this study, we apply the methodology of post‐classification change detection to map and monitor land cover change patterns related to agricultural development and urban expansion in the desert fringes of the Eastern Nile Delta region. Using a hybrid classification approach, we employ multitemporal Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images from 1984, 1990 and 2003 to produce three land cover/land‐use maps. Post‐classification comparison of these maps was used to obtain ‘from–to’ statistics and change detection maps. The change detection results show that agricultural development increased by 14% through the study period. The average annual rate of land reclamation during 1990–2003 (4511 ha a^?1) was comparable to that during 1984–1990 (4644 ha a^?1), reflecting a systematic national plan for desert reclamation that went into effect. We find that the increase in urbanization (by ca 21 300 ha) during 1990–2003 was predominantly due to encroachment into traditionally cultivated land at the fringes of urban centres. Our results accurately quantify the land cover changes and delineate their spatial patterns, demonstrating the utility of Landsat data in analysing landscape dynamics over time. Such information is critical for making efficient and sustainable policies for resource management.     

Identifying local anthropogenic CO2 emissions with satellite retrievals: a case study in South Korea

We used multiyear Greenhouse Gases Observing Satellite (GOSAT) dry air, column-integrated CO₂ (XCO₂) retrievals (2010–2013) to evaluate urban and local-scale CO₂ emissions over East Asia and examined whether GOSAT XCO₂ captures the impact of strong local CO₂ emissions over South Korea, an East Asian downwind region with high atmospheric aerosol loading and strong summer monsoons. We chose a region in western Mongolia (upwind region) as the XCO₂ background, and estimated XCO₂ enhancements in South Korea to gauge local and regional emissions. We found that the cold season (November–February) was better suited for estimating XCO₂ enhancements of local emissions due to the summer monsoon and stronger transboundary impacts in other seasons. In particular, we focused on three local GOSAT XCO₂ footprints (about 10.5 km in diameter) in South Korea: the Seoul Metropolitan Area (SMA), the Gwangyang Steelworks and Hadong power plants (GYG), and the Samcheonpo power plants (SCH). The range of XCO₂ enhancement was 7.3–10.7 ppm (14.1–21.3 mg m^?3 in standard temperature and pressure (STP)). By estimating other important contributions to XCO₂ enhancements such as the XCO₂ latitudinal gradients and Chinese fossil fuel combustions, we estimated the net enhancements caused mainly by local CO₂ emissions in the range of 4.2–7.6 ppm (8.1–14.7 mg m^?3 in STP) These high enhancements imply that large point source contributions are an important factor in determining these enhancements, even if contributions are also made by broader-scale emissions. Additionally, differences in net XCO₂ enhancements and trends between GYG (+ 4.2 ppm (+ 8.2 mg m^?3 in STP), – 0.2 ppm year^?1 (–0.4 mg m^?3 year^?1 in STP)) and SCH (+ 7.6 ppm (+ 14.9 mg m^?3 in STP), + 1.3 ppm year^?1 (+ 2.6 mg· m^?3 year^?1 in STP)) indicate that these closely located footprints (approximately 26 km apart) are separable. These results will be useful in evaluating and reducing uncertainties in regional and local anthropogenic greenhouse gas (GHG) emissions over East Asia.     

Long-term trends and decadal solar variability in ozone near the tropopause over the Indian region

To investigate the long-term trends and effects of decadal solar variability in the upper tropospheric ozone, data obtained from the Stratospheric Aerosol and Gas Experiment II (SAGE II) aboard the Earth Radiation Budget Satellite (ERBS) during the period 1985–2005 were analysed using a multifunctional regression model over the Indian region (8–40° N; 65–100° E). Analysis of time series spanning these years shows statistically insignificant trends (at the two-sigma level (95% confidence level)) at upper tropospheric pressure levels (10?16 km). This period covers two solar cycles, one lasting from 1985 to 1995 and the other from 1996 to 2005; these are referred to as decade I and decade II, respectively. Since temporal variation in ozone number density indicates 11 year periodicity, trends are statistically significant when calculated separately during each solar cycle. Trend analysis indicates statistically significant positive trends (0.7 ± 1.7% to 3.9 ± 2.9% year^?1 during decade I, and 2.2 ± 1.6% to 4.5 ± 3.0% year^?1 during decade II). In general, higher ozone trends are observed during decade II. Seasonal variation in trends during decade II shows increasing trends during the pre-monsoon (0.8?3.8% year^?1), monsoon (0.8?7.1% year^?1), and post-monsoon (2.8?8.0% year^?1) seasons. The annually averaged solar signal in ozone is found to be of the order of around??5 ± 4.3% to??13.8 ± 6.7%/(100 sfu). Results obtained in the present study are also compared with those obtained by other researchers.     

遥感数据融合下的雄安新区NPP时空格局分析

高时空分辨率数据对实现植被生产力动态监测和生态环境评估具有重要意义.以雄安新区为研究区,基于改进的ESTARFM融合模型构建高时空分辨率NDVI数据集,结合改进的CASA模型,模拟和分析了 2000～2018年区域植被NPP的时空变化特征,并探讨气温与降水对NPP的影响.结果表明:①改进的ESTARFM融合模型预测结果...     

Assessment of forest damage caused by an ice storm using multi-temporal remote-sensing images: a case study from Guangdong Province

In early 2008, forest ecosystems in southern China suffered damage due to a severe ice storm disaster. The area and degree of forest damage caused by the ice storm was assessed using Satellite Pour l’Observation de la Terre (SPOT)-Vegetation images for Guangdong Province acquired between 1999 and 2008. By using the maximum value composition method and image thresholding techniques, the forest vegetation loss, expressed as the change in net primary productivity (NPP) and two indicators (I₁, I₂), was estimated. The damage threshold was determined by comparing the standard deviation of pixels of the undamaged areas in 2008 and other years without any disaster, which was 10%. The area of damaged forest vegetation was 47,670 km², with the northern Guangdong Province most seriously affected. The total loss of NPP for forest vegetation was 50,578,055 t (DW) year^?1, with 52 counties (43.7%) suffering forest vegetation damage. Evergreen coniferous forest was most widely affected, but evergreen broad-leaved forest was the most severely damaged vegetation type. Terrain topography influenced the damage to forest vegetation, which was found to increase with increasing elevation and slope gradient. The range and degree of damaged forest determined by remote-sensing data is consistent with the extent of the ice storm, indicating that this study provides a new approach for rapid assessment of forest disasters at a regional scale.     

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.     

Assessing the impact of urban land development on net primary productivity in the southeastern United States

The southeastern United States (SE-US) has undergone one of the highest rates of landscape changes in the country due to changing demographics and land use practices over the last few decades. Increasing evidence indicates that these changes have impacted mesoscale weather patterns, biodiversity and water resources. Since the Southeast has one of the highest rates of land productivity in the nation, it is important to monitor the effects of such changes regularly. Here, we propose a remote sensing based methodology to estimate regional impacts of urban land development on ecosystem structure and function. As an indicator of ecosystem functioning, we chose net primary productivity (NPP), which is now routinely estimated from the MODerate resolution Imaging Spectroradiometer (MODIS) data. We used the MODIS data, a 1992 Landsat-based land cover map and nighttime data derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) for the years 1992/1993 and 2000 to estimate the extent of urban development and its impact on NPP. The analysis based on the nighttime data indicated that in 1992/1993, urban areas amounted to 4.5% of the total land surface of the region. In the year 2000, the nighttime data showed an increase in urban development for the southeastern United States of 1.9%. Estimates derived from the MODIS data indicated that land cover changes due to urban development that took place during the 1992-2000 period reduced annual NPP of the southeastern United States by 0.4%. Despite the uncertainties in sensor fusion and the coarse resolution of the data used in this study, results show that the combination of MODIS products such as NPP with nighttime data could provide rapid assessment of urban land cover changes and their impacts on regional ecosystem resources.     

Assessing the contributions of climate change and human activities to cropland productivity by means of remote sensing

ABSTRACT

It is essential for quantitatively assessing the influences of climate change (CC) and human activities (HA) on cropland productivity to clarify the associated drive mechanisms. However, few studies have quantitatively evaluated their relative contributions to cropland productivity in China. In this study, net primary productivity (NPP) was chosen as an indicator of cropland productivity. The actual NPP (ANPP) represents the combined effects of CC and HA on cropland productivity, and the potential NPP (PNPP) represents the effect of CC on cropland productivity. The difference between the actual and the potential NPP (HNPP) represents the effect of HA on cropland productivity. Meanwhile, we designed six scenarios to quantify the contributions of CC and HA to the inter-annual variation in ANPP. Our results showed that cropland ANPP in China as a whole increased at a rate of 3.68 g C m^?2 year^?1 (p < 0.01) between 2000 and 2015. The contributions of CC and HA to the variations in cropland ANPP were 3.23 g C m^?2 year^?1 (p < 0.01) and 0.45 g C m^?2 year^?1 (p > 0.1), respectively. Furthermore, we found that 79.43% of total cropland area showed an inter-annual increase in ANPP, in which 56.22% of cropland area with increase in ANPP was predominantly influenced by CC, compared to 43.78% predominantly influenced by HA. Conversely, 20.57% of total cropland area showed an inter-annual decrease in ANPP, with 70.62% of cropland area with decrease in ANPP being predominantly influenced by HA compared with only 29.38% predominantly influenced by CC. Overall, CC was the dominant factor affecting increase in cropland ANPP, whereas HA was the dominant factor affecting decrease in cropland ANPP. We conclude that effective measures for protecting cropland resources need to be further strengthened, despite CC primarily promoting cropland ANPP.     

Glacier elevation changes (2012–2016) of the Puruogangri Ice Field on the Tibetan Plateau derived from bi-temporal TanDEM-X InSAR data

This study aims to evaluate the potential of TerraSAR-X (TSX) add-on for Digital Elevation Measurement (TanDEM-X) bi-static synthetic aperture radar (SAR) data sets for the retrieval of glacier digital elevation models (DEMs) and elevation changes over mountain regions. We exploited two pairs of TanDEM-X SAR data sets acquired in 2012 and 2016 over the Puruogangri Ice Field (PIF), which is the largest modern glacier on the Tibetan Plateau (TP). Two fine-detail and high-precision DEMs for 2012 and 2016 over the PIF were generated by differential interferometric processing, and were validated against height measurements from global positioning system (GPS) and Ice, Cloud, and land Elevation Satellite (ICESat) altimetry, yielding a vertical accuracy of 1.91 ± 0.76 m and 1.69 ± 0.83 m, respectively. The elevation changes were derived by differencing the bi-temporal TanDEM-X DEMs and revealed predominant glacier surface thinning on the PIF. An annual surface thinning rate of ?0.317 ± 0.027 m year^?1 was estimated in the period 2012–2016, which is much larger than the estimate of ?0.049 ± 0.200 m year^?1 for the period 2000–2012 reported in previous studies. This accelerating trend of glacier surface thinning might be attributable to the continued increase in summer temperature since the 1980s and decrease in annual precipitation between two periods of investigation. This study demonstrates that comparison of the bi-temporal TanDEM-X DEMs is an efficient method for accurate and detailed retrieval of the latest surface elevation changes of mountain glaciers.