Spatial methane emission modelling from wetlands using geospatial tools

Wetland ecosystems have acquired importance among the scientific community because of their role in biogeochemical cycling and as source and sink of greenhouse gas emissions particularly methane (CH₄) in addition to the ecosystem services that they provide. To estimate the CH₄ emission from wetlands in spatial domain, models incorporating the geospatial tools are required. Accordingly, main focus of this study is to demonstrate the utility of geospatial techniques in assessing the spatial CH₄ emission variability from four different regions of Uttar Pradesh (UP), India, namely, Western, Central, Bundelkhand, and Eastern regions deploying Semi-Automated Empirical CH₄ emission Model (SEMEModel) using Moderate Resolution Imaging Spectro-radiometer data of 2010–2012. SEMEModel is a three-tier model which determines the CH₄ emissions in spatial domain as a function of remote sensing (RS) and Geographic Information System (GIS) derived wetland components including wetland area and corresponding temperature factors coupled with point CH₄ emission coefficients developed via field measurements. Results of the study have shown that eastern region of UP exhibited maximum estimated/modelled CH₄ emissions (43.10 Gg yr^?1) as compared to other regions due to more area being under wetlands whereas central region was found to be the least contributor (0.266 Gg yr^?1) due to the fact that it has minimum wetland area (0.40%) among all the regions. It was observed that estimated/modelled CH₄ emissions depicted an increase by 4.96 orders of magnitude in 2010–2011 and 4.04 orders of magnitude in 2011–2012 when estimated by applying literature-based global CH₄ emission coefficients for UP in place of CH₄ flux values derived in field. It signifies that the upscaling of CH₄ flux values using literature-based CH₄ flux values of one region to another region may not reflect actual values. Therefore, this study not only helps to improve accuracy of CH₄ emission estimates from wetlands but also credibly adjudges that integration of CH₄ flux field measurements with modern tools of RS and GIS will immensely assist to reduce the uncertainties in CH₄ emission predictions done over larger regions.     

Land cover classification of tundra environments in the Arctic Lena Delta based on Landsat 7 ETM+ data and its application for upscaling of methane emissions

The Lena River Delta, situated in Northern Siberia (72.0-73.8° N, 122.0-129.5° E), is the largest Arctic delta and covers 29,000 km². Since natural deltas are characterised by complex geomorphological patterns and various types of ecosystems, high spatial resolution information on the distribution and extent of the delta environments is necessary for a spatial assessment and accurate quantification of biogeochemical processes as drivers for the emission of greenhouse gases from tundra soils. In this study, the first land cover classification for the entire Lena Delta based on Landsat 7 Enhanced Thematic Mapper (ETM+) images was conducted and used for the quantification of methane emissions from the delta ecosystems on the regional scale. Nine land cover classes of aquatic and terrestrial ecosystems in the wetland dominated (72%) Lena Delta could be defined by this classification approach. The mean daily methane emission of the entire Lena Delta was calculated with 10.35 mg CH₄ m^− 2 d^− 1. Taking our multi-scale approach into account we find that the methane source strength of certain tundra wetland types is lower than calculated previously on coarser scales.     

Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10‐day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT‐4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT‐4 vegetation (VGT) 10‐day composite (S10) NDVI data. Three years of AVHRR PAL (1998–2000) and seven years of GIMMS (1998–2004) have been compared to 8 km resampled SPOT‐4 VGT (1998–2004) data. The dynamic range of SPOT‐4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT‐4 VGT NDVI. Ortho‐regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT‐4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT‐4 VGT compared to the PAL/SPOT‐4 VGT.

Analyses on decade data likewise show that a linear relation exists between Spot‐4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot‐4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot‐4 VGT NDVI compared to AVHRR PAL versus Spot‐4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra‐annual variations in the correlation between SPOT‐4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT‐4 VGT cloud‐screening algorithm is insufficient, thereby suppressing the rainy season NDVI.     

Mapping sub-pixel proportional land cover with AVHRR imagery

A problem with NOAA AVHRR imagery is that the intrinsic scale of spatial variation in land cover in the U.K. is usually finer than the scale of sampling imposed by the image pixels. The result is that most NOAA AVHRR pixels contain a mixture of land cover types (sub-pixel mixing). Three techniques for mapping the sub-pixel proportions of land cover classes in the New Forest, U.K. were compared: (i) artificial neural networks (ANN); (ii) mixture modelling; and (iii) fuzzy c -means classification. NOAA AVHRR imagery and SPOT HRV imagery, both for 28 June 1994, were obtained. The SPOT HRV images were classified using the maximum likelihood method, and used to derive the 'known' sub-pixel proportions of each land cover class for each NOAA AVHRR pixel. These data were then used to evaluate the predictions made (using the three techniques and the NOAA AVHRR imagery) in terms of the amount of information provided, the accuracy with which that information is provided, and the ease of implementation. The ANN was the most accurate technique, but its successful implementation depended on accurate co-registration and the availability of a training data set. Supervised fuzzy c -means classification was slightly more accurate than mixture modelling.     

Variation in NDVI values with change in spatial resolution for semi-arid savanna vegetation: a case study in northwestern South Africa

Natural vegetation and crop-greening patterns in semi-arid savannas are commonly monitored using normalized difference vegetation index (NDVI) values from low spatial resolution sensors such as the Advanced Very High Resolution Radiometer (AVHRR) (1 km, 4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m, 500 m). However, because semi-arid savannas characteristically have scattered tree cover, the NDVI values at low spatial resolution suffer from the effect of aggregation of near-infrared and red energy from adjacent vegetated and non-vegetated cover types. This effect is seldom taken into consideration or quantified in NDVI analyses of the vegetation of semi-arid lands. This study examined the effect of pixel size on NDVI values of land-cover features for a semi-arid area, using the 1000 m, 250 m and 10 m pixel sizes. A rainy season Système Pour l'Observation de la Terre 5 (SPOT 5) High Resolution Geometric (HRG) image at 10 m spatial resolution was utilized. Following radiometric and geometric preprocessing, the 10 m pixel size of the image was aggregated to 250 m and 1000 m to simulate imagery at these pixel sizes, and then NDVI images at the spatial resolution scales of 10 m (NDVI_{10 m}), 250 m (NDVI_{250 m}), and 1000 m (NDVI_{1000 m}) derived from the respective images. The simulation of the NDVI_{250 m} image was validated against a concurrent 16 day MODIS NDVI composite (MOD13Q1) image, and the accuracy derived from the validation was generalized to the NDVI_{1000 m} image. With change from low to high spatial resolution, extreme magnitude NDVI values shifted towards the centre (mode) of the resulting approximately Gaussian NDVI distributions. There was a statistically significant difference in NDVI values at the three pixel sizes. Low spatial magnitude vegetation sites (woodland, cropland) had reductions of up to 28% in NDVI value between the NDVI_{10 m} and NDVI_{1000 m} scales. The results indicate that vegetation monitoring using low spatial resolution imagery in semi-arid savannas may only be indicative and needs to be supplemented by higher spatial resolution imagery.     

Linear mixture modelling applied to AVHRR data for crop area estimation

Abstract

A technique for estimating crop coverage using linear mixture modelling of multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data is presented for a study area in northern Greece. This paper identifies some of the problems associated with using satellite sensor data with coarse spatial resolution for crop area estimation. Using satellite sensor imagery with a high spatial resolution to extrapolate ground measurements to AVHRR scales, the paper shows how the mixture model can be applied to AVHRR data in a mixed agricultural system. Crop areas are estimated to an average accuracy of 89 percent on regional scale using this technique. The results show that this linear mixture modelling has potential for operational crop area monitoring on a regional basis.     

Volcano detection and monitoring using AVHRR data: the Krafla eruption, 1984

Abstract. Many volcanic eruptions go essentially unmonitored. Potentially the Advanced Very High Resolution Radiometer (AVHRR), with its global coverage, frequent return period, and sensitivity in the thermal infrared, represents a data source capable of monitoring surface volcanic activity unrecorded by ground observations or other satellite sensors. In this study an attempt is made to demonstrate this potential by extracting information for the 1984 eruption at Krafla, Iceland. Seven cloud-free AVHRR images were available for the 14 day period of eruptive activity. The surface activity was detectable as a major thermal anomaly in all three of the longer wavelength channels and was vigorous enough during one night-time pass to be detectable in the near-infrared channel (0.725-1.1μm). Channel 2 and 4 radiance data were used to calculate the size and temperature of sub-pixel heat sources within the lava flow field, and a heat source at 1050° C was estimated as occupying an area of approximately 240000 m², which was distributed across 20 pixels. Detection and measurement of volcanic heat sources at such short wavelengths using low spatial resolution data has rarely been reported before. Field reports and maps were used to guide and confirm the analysis. Digital number variations within the anomaly could be related to various known features of the eruption. To monitor the eruption a weighted average method was derived and used to sharpen up the images, and the density sliced sharpened images enabled the development of the eruption to be mapped. Results compared well with field reports, suggesting that AVHRR and similar systems could be a useful source of data for monitoring eruptions where contemporaneous field observations are unavailable or incomplete.     

Evaluation of earth observation based long term vegetation trends — Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data

AVHRR (Advanced Very High Resolution Radiometer) GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference vegetation Index) data is available from 1981 to present time. The global coverage 8 km resolution 15-day composite data set has been used for numerous local to global scale vegetation time series studies during recent years. Several aspects however potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. More recent NDVI data sets from both Terra MODIS and SPOT VGT data are considered an improvement over AVHRR and these products in theory provide a possibility to evaluate the accuracy of GIMMS NDVI time series trend analysis for the overlapping period of available data. In this study the accuracy of the GIMMS NDVI time series trend analysis is evaluated by comparison with the 1 km resolution Terra MODIS (MOD13A2) 16-day composite NDVI data, the SPOT Vegetation (VGT) 10-day composite (S10) NDVI data and in situ measurements of a test site in Dahra, Senegal. Linear least squares regression trend analysis on eight years of GIMMS annual average NDVI (2000-2007) has been compared to Terra MODIS (1 km and 8 km resampled) and SPOT VGT NDVI data 1 km (2000-2007). The three data products do not exhibit identical patterns of NDVI trends. SPOT VGT NDVI data are characterised by higher positive regression slopes over the 8-year period as compared to Terra MODIS and AVHRR GIMMS NDVI data, possibly caused by a change in channels 1 and 2 spectral response functions from SPOT VGT1 to SPOT VGT2 in 2003. Trend analysis of AVHRR GIMMS NDVI exhibits a regression slope range in better agreement with Terra MODIS NDVI for semi-arid areas. However, GIMMS NDVI shows a tendency towards higher positive regression slope values than Terra MODIS in more humid areas. Validation of the different NDVI data products against continuous in situ NDVI measurements for the period 2002-2007 in the semi-arid Senegal revealed a good agreement between in situ measurements and all satellite based NDVI products. Using Terra MODIS NDVI as a reference, it is concluded that AVHRR GIMMS coarse resolution NDVI data set is well-suited for long term vegetation studies of the Sahel-Sudanian areas receiving < 1000 mm rainfall, whereas interpretation of GIMMS NDVI trends in more humid areas of the Sudanian-Guinean zones should be done with certain reservations.     

Agreement assessment of NOAA/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas.     

Analysis of seasonal variability of methane over global land area

In this study, we determine the global emission concentration of methane using the scanning imaging absorption spectrometer for atmospheric chartography data. We analyzed land and sea area to investigate the 9-year changes in methane concentrations from 2003 to 2011. Moreover, by subtracting the concentration of methane from land and sea, we can found the methane emission concentration of land. As a result, it is cleared that a big amount of CH₄ emission concentration was found not only in the northern Hemisphere paddy fields but also in the southern Hemisphere broadleaf evergreen areas (central Africa and south America). We also found that the global land CH₄ growth rate is 3–5 ppb/year during 9 years.     

Scale considerations in vegetation monitoring using AVHRR data

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) is currently the only operational remote sensing system capable of providing global daily data which can be used for vegetation monitoring. These data are available with resolution cell sizes ranging from around one to 20 km on a side, though the temporal and spatial extent of cover at each resolution is variable. In this paper Normalized Difference Vegetation Index temporal curves derived from AVHRR at different resolutions are compared over both agricultural and natural tropical vegetation types. For the agricultural regions the length of growing season and major breaks of slope associated with key crop development events are equally well shown at coarse and fine resolution. Detailed examination of the curves reveals differences thought to result from temporal changes in landscape structure. Temporal curves derived from AVHRR data at dilTerent spatial resolutions shows that the spatial organization of both agricultural and natural landscapes, tropical forest in this case, changes throughout a single season. Transitions across major ecological zones are detected across a range of resolutions, though the undersampling employed in the generation of the coarser resolution products is found to exert some limitations on the spatial representivity of these data; this varies both with geographical area and time. These observations highlight the importance of a consideration of scale when using AVHRR data for vegetation monitoring, and emphasize the need for dilTerent scales of observation (both in temporal and spatial terms) for different problems and at different times of the year.     

Spectral,spatial and temporal characteristics of Arctic tundra reflectance

Abstract

The objective was to quantify and analyse the spectral, spatial and temporal variability of solar radiation reflected from arctic tundra vegetation at a study site in the Brooks Range foothills of northern Alaska. Spectral radiance data from hand-held radiometers and the SPOT HRV sensor were sampled along hillslope transects (toposequences) and within four vegetation community types. The spatial trend of normalised difference vegetation index (NDVI) along the toposequences corresponded to variations in the abundance of green vegetation matter and in vegetation composition. A marked temporal increase in the NDVI occurred along the toposequences from the beginning of the growing season (mid-June) to peak green up (end of July). The spectral signatures of three tundra dominant vegetation communities, dry heath, moist tussock and wet sedge, were moderately separable, with dry heath being most separable. The overall separability of the major community types was similar at all times during the growing season, with the most divergent signatures occurring in late July during maximum greenness. Some of the important ecological features of the arctic tundra landscape are not resolved by the SPOT HRV sensor in multi-spectral mode, in spite of its high (20 m) spatial resolution.     

Effet de la reésolution spatiale sur des proprieeacute;teés statistiques des images satellites: une eétude de cas

Abstract

The instruments on such satellites as Landsat or SPOT present the advantage provided by high spatial resolution. This advantage is tempered by their low time resolution. Therefore, it is not always possible to monitor seasonal variations of parameters such as the normalized vegetation index. The AVHRR instrument on board the NOAA satellites has a very high repetitivity but a very low spatial resolution. In our research we proposed to monitor the normalized vegetation index with this low-resolution instrument. It is therefore of interest to examine the relations between high- and low-resolution images for using the AVHRR data as a means of interpolation between two MSS images. This problem is addressed here using satellite images of an important agricultural region in France. In terms of the transformation of the mean radiometric values, it is shown that a linear transformation exists to calculate the AVHRR data from those of the MSS. but the relation presents a strong dependency on the observed scene. The effect on the higher-order statistical properties is studied through the transformation of the images textures by progressively degrading the MSS images. It is shown that a threshold, which depends on the scene, exists on the resolution below which all statistical information disappears.     

Land cover change detection at coarse spatial scales based on iterative estimation and previous state information

This study focuses on the use of coarse spatial resolution (CR, pixel size about 1 km²) remote sensing data for land cover change detection and qualification. Assuming the linear mixing model for CR pixels, the problem is that both the multitemporal class features and the pixel composition in terms of classes are unknown. The proposed algorithm is then based on the iterative alternate estimation of each unknown variable. At each iteration, the class features are estimated, thanks to the knowledge of the composition of some pixels, and then the pixel composition is re-estimated knowing the class features. The subset of known composition pixels is the subset of pixels where no change has occurred, i.e. the previous land cover map is still valid. It is derived automatically by removing at each iteration the pixels where the new composition estimation disagrees with the former one. Finally, for the final estimation of the pixel composition, a Markovian chain model is used to guide the solution, i.e. the previous land cover map is used as a ‘reminder’ or ‘memory’ term.This approach has been first validated using simulated data with different spatial resolution ratios. Then, the detection of forest change with SPOT/VGT-S10 has been considered as an actual application case. Finally, the method has been applied to change detection on the Val de Saône watershed between the 1980s and 2000. The results obtained from three coarse resolution series, NOAA/AVHRR, SPOT/VGT-S10 and SPOT/VGT-P, have been compared.     

A Review of: “Environmental Information Management and Analysis: Ecosystem 10 global scales”. Edited by W. K. MICHENER,J. W. BRUNT and S. G. STAFFORD (London: Taylor &: Francis Ltd, 1994). [Pp. 535.] Price £55,00.

Abstract

Satellite indices of vegetation from the Australian continent were calculated from May 1986 to April 1987 from NOAA-9 AVHRR (Advanced Very High Resolution Radiometer) and Nimbus-7 SMMR (Scanning Multichannel Microwave Radiometer) satellite data. The visible (VIS) and near infrared (N1R) reflectances and their combination, the Normalized Difference (ND) Vegetation Index were calculated from the AVHRR sensor. From the SMMR, the microwave Polarization Difference (PD) was calculated as the difference between the vertically and horizontally polarized brightness temperatures at 37 GHz. The AVHRR data were gridded to match the 25 km spatial resolution of the SMMR 37 GHz data and both data sets were analysed to provide a temporal resolution of one month. Using a one month lag, the ND, PD, VIS and NIR, indices were plotted against rainfall and water balance estimates of evaporation, calculated using the monthly rainfall data and long term averages of pan evaporation from 74 locations covering a range of vegetation types. The monthly plots had wide scatter. This scatter was reduced markedly by aggregating the data over twelve months, leading to the conclusion that direct satellite monitoring of annual evaporation across the Australian continent using PD or VIS is feasible for areas with evaporation less than 600 mm y^?1. The ND relationship was limited by scatter and the PD and VIS relationships by their saturation above 600 mm y^?1, which spanned about two-thirds of the continental range studied. Scatter was reduced and ND had a predictive range above 600 mm y^?1 if evaporation was normalized by evaporative demand. But prior knowledge of potential evaporation is needed in this approach. The NIR reflectance of forests were consistently lower than neighbouring areas of agriculture, thus ND may underpredict the evaporation of forests relative to agriculture. Temporal resolution of the satellite indices over periods of one month could not be evaluated due to spatial and temporal variability of climatic and biological factors not accounted for in the water balance estimates of evaporation.     

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the west African Sahel

Abstract

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.     

A methodology to evaluate the aerosol effective radius based on MODIS aerosol products applicable to other satellite platforms

A strategy to evaluate the effective radius (r _eff) as a function of aerosol retrievals is provided in this work. This methodology is based on the MODerate resolution Imaging Spectroradiometer (MODIS) aerosol products, using the 0.66 and 0.87 µm bands. These data have been studied from February 2000 to December 2005 in a grid situated at Subtropical North‐east Atlantic region. To reduce the number of MODIS useful variables a Factorial Analysis by Principal Components has been applied, decreasing the aerosol parameters from 18 to five. With these parameters, backscattering ratios and asymmetry factors at 0.66 and 0.87 µm besides the Ångström parameter, a lineal multivariate analysis technique has been applied to find the combination of variables that better evaluate the r _eff. The standard error for the predicted value of r _eff is ±0.09 µm. The expression obtained here has the advantage that it can be applied to other remote sensors like AVHRR/NOAA, HRV/SPOT, TM/LANDSAT, and so on, with long time series.     

A method for detecting large-scale forest cover change using coarse spatial resolution imagery

Many large countries, including Canada, rely on earth observation as a practical and cost-effective means of monitoring their vast inland ecosystems. A potentially efficient approach is one that detects vegetation changes over a hierarchy of spatial scales ranging from coarse to fine. This paper presents a Change Screening Analysis Technique (Change-SAT) designed as a coarse filter to identify the location and timing of large (>5-10 km²) forest cover changes caused by anthropogenic and natural disturbances at an annual, continental scale. The method uses change metrics derived from 1-km multi-temporal SPOT VEGETATION and NOAA AVHRR imagery (reflectance, temperature, and texture information) and ancillary spatial variables (proximity to active fires, roads, and forest tenures) in combination with logistic regression and decision tree classifiers. Major forest changes of interest include wildfires, insect defoliation, forest harvesting, and flooding. Change-SAT was tested for 1998-2000 using an independent sample of change and no-change sites over Canada. Overall accuracy was 94% and commission error, especially critical for large-area change applications, was less than 1%. Regions identified as having major or widespread changes could be targeted for more detailed investigation and mapping using field visits, aerial survey, or fine resolution EO methods, such as those being applied under Canadian monitoring programs. This multi-resolution approach could be used as part of a forest monitoring system to report on carbon stocks and forest stewardship.     

Crop condition and yield simulations using Landsat and MODIS

Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6×1.6-km² grid scale were comparable to county yields reported by the USDA-National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.     

Spatiotemporal modelling of methane flux from the rice fields of India using remote sensing and GIS

Rice fields have been accredited as an important source of anthropogenic methane, with estimates of annual emission ranging from 47 to 60 Tg per year, representing 8.5–10.9% of total emission from all sources. In this study, attempts have been made to derive the spatial and temporal pattern of methane emitted from the rice lands of India using an integrated methodology involving satellite remote sensing and geographic information system (GIS) techniques. Multidate SPOT VGT 10‐day Normalized Difference Vegetation Index (NDVI) composite data for a complete year were used to map the rice area, delineate single‐ and double‐cropped rice areas, crop calendar and growth stages. Rainfall, digital elevation and irrigation data were integrated to stratify the rice area into distinct categories related to methane emission. Preliminary analysis of the methane emission pattern was carried out using published values. The results show that around 91% of total methane emission results from wet‐season rice, contributing 4.66 Tg per year. The temporal pattern shows that August and September are the months of peak emission during the wet season, and March and April during the dry season.