Daily Composition Research Articles

Annual Minimum Snow/Ice Extent Variations over Greenland since 2000: Ice Sheet, Peripheral Areas, and Relation to Ice Mass Balance

Abstract A novel satellite image processing technique was utilized to produce an annual time series of the minimum snow/ice (MSI) extent over the entire Greenland landmass for the period 2000–22. The information was derived from the Moderate Resolution Imaging Spectroradiometer 10-day clear-sky composites over the April–September period. The data products were generated from 250-m swath imagery. The annual aggregates were downscaled to a 150-m grid for consistency with data on margins of Greenland available from the Geological Survey of Denmark and Greenland (GEUS) and the Greenland Ice Mapping Project. Interannual variations in the MSI extent were derived and analyzed for each of the seven major glacier basins in Greenland split into the main ice sheet, represented by a static map, and the peripheral areas from which all variations originated. Four of the seven regions demonstrated statistically significant negative trends in the MSI extent. The entire Greenland area also showed a declining snow/ice extent although this was not statistically significant. The region-wide and peripheral snow/ice extent varied from a minimum of 1.807 × 106 km2 (1.449 × 105 km2 for peripheral areas) observed in 2012 to a maximum of 1.860 × 106 km2 (1.977 × 105 km2) observed in 2006 with an average value of 1.829 × 106 km2 (1.664 × 105 km2). The derived MSI variations showed a statistically significant correlation with the near-surface 2-m air temperature from the ERA5-Land reanalysis and Greenland ice mass balance from GEUS for all catchments, with correlation coefficients for the entire area equal to −0.74 and 0.53, respectively. The mapping of many peripheral glaciers and ice shelves included in the glaciology databases and utilized for the IPCC reporting is not always consistent with our results and requires improvement, especially in coastal areas.

Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks

Satellite time series data, widely used for land cover classification, often contain missing values due to cloud contamination, which can negatively affect classification. Numerous strategies have been developed to reconstruct the missing values to produce regular time series for machine learning classifiers, among which the compositing followed by the linear interpolation is most widely used. However, the classification improvement of linear interpolation for land cover classification has not been examined. Recently developed deep learning models such as long short term memory (LSTM) and Transformer allow such examination as they can classify time series with missing values. In this study, we compared the time series composites with missing values (without linear interpolation) and the linearly interpolated time series composites (without missing values) for land cover classification. About 18 thousand Harmonized Landsat Sentinel-2 (HLS) images acquired over Amur River Basin of China (890,308 km2) in 2021 were composited to 14 16-day periods. Two time series composites were classified, i.e., (i) the 16-day composites without interpolation that have on average 15.35% 16-day periods with missing values and (ii) the linearly interpolated 16-day composites with no missing values. The classifications showed that (1) between classifications with and without linear interpolation there was < 0.2% overall accuracy differences for the bidirectional LSTM (Bi-LSTM) and < 0.5% for the Transformer both of which were smaller than model training randomness; and (2) the computation time can be saved using composites without linear interpolation. The findings suggested that it is unnecessary to use the time-consuming linear interpolation in Bi-LSTM and Transformer-based land cover classifications. The findings were confirmed by experiments for sensitivity to the number of cloud-free composites and to different classification legends using crop type classifications. It implied the linear interpolation algorithm cannot reconstruct reliable time series for land cover classifications and historical use of such method is more about mitigating the inability of traditional classifiers to handle missing values rather than improving classifications. Linear interpolation is not necessary for LSTM and Transformer with capability to handle missing values. The training datasets and developed codes in this study are made publicly available.

Spectral correlation in MODIS water-leaving reflectance retrieval uncertainty.

Spectral remote sensing reflectance, Rrs(λ) (sr-1), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved Rrs. Furthermore, since the associated algorithms require Rrs at multiple spectral bands, the spectral (i.e., band-to-band) error covariance in Rrs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into Rrs, as retrieved using NASA's multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in Rrs. The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in Rrs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chla, mg/m3) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd(490), m-1). Accounting for the error covariance in Rrs generally reduces the estimated relative uncertainty in chla by ∼1-2% (absolute value) in waters with chla < 0.25 mg/m3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chla > 0.35 mg/m3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For Kd(490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in Rrs is further verified through forward-calculating chla from MODIS-retrieved and in situ Rrs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chla can be achieved over deep ocean waters (chla ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in Rrs, some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

MODIS Time Series Reveal New Maximum Records of Defoliated Area by Ormiscodes amphimone in Deciduous Nothofagus Forests, Southern Chile

Outbreaks of the Ormiscodes amphimone moth are among the largest biotic disturbances in South America, defoliating vast areas of native Nothofagus pumilio forests in the Chilean and Argentinian Patagonia in the last decade. Using MODIS 16-day composites of the enhanced vegetation index and the new functions of the latest release of the “npphen” R-package, we identified new maximum records of continuously defoliated area in the Aysén region (Chilean Patagonia). This approach allowed us to detect 55,193 ha and 62,344 ha of extremely defoliated N. pumilio forest in 2019 and 2022, respectively, in an area locally known as “Mallín Grande”. Extreme defoliation was accounted for by means of negative EVI anomalies with values falling among 5% of the lowest EVI records of the reference period (2000–2010). These new 2019 and 2022 outbreaks in Mallín Grande were the largest reported insect outbreaks in South American Patagonia in this century.

Improving crop mapping in Brazil’s Cerrado from a data cubes-derived Sentinel-2 temporal analysis

The Sentinel-2/MultiSpectral Instrument (S2/MSI) expands the frequency of satellite observations, which is relevant to elaborate detailed and timely land use and land cover (LULC) classifications. However, storing, managing, and processing big data is costly and challenging, inducing a dimensionality reduction by modeling images as composite products. Contrastingly, it obliterates the temporal resolution improvement. As LULC changes are subtle over time, little is said about how much detail we lost by degrading temporal resolution. Data cube architectures enable storing, accessing, and modeling big data, mitigating losses. Brazil Data Cube (BDC) produces multidimensional data cube collections from different medium-resolution satellite data for Brazil, including S2/MSI. Here, we evaluated three BDC S2/MSI data cubes (two 16-day composites and one unblended, with the MSI original temporal resolution) to map a dynamic-and-representative region in the far-Western Bahia agricultural belt frontier, Cerrado biome, at crop type level. We incorporate spectral indices, ground samples, and crop calendars into a Random Forest-based temporal analysis. Overall accuracies (0.91 and 0.92 for composites, and 0.96 reached for the unblended) highlight the S2/MSI temporal resolution for improving mapping tasks. Given the impact of the cropland frontier expansion over Cerrado in Brazil’s commodity production, detecting subtle landscape variations can improve agri-environmental policies.

Optical remote sensing (Sentinel-3 OLCI) used to monitor dissolved organic carbon in the Lena River, Russia

In the past decades the Arctic has experienced stronger temperature increases than any other region globally. Shifts in hydrological regimes and accelerated permafrost thawing have been observed and are likely to increase mobilization of organic carbon and its transport through rivers into the Arctic Ocean. In order to better quantify changes to the carbon cycle, Arctic rivers such as the Lena River in Siberia need to be monitored closely. Since 2018, a sampling program provides frequent in situ observations of dissolved organic carbon (DOC) and colored dissolved organic matter (CDOM) of the Lena River. Here, we utilize this ground truth dataset and aim to test the potential of frequent satellite observations to spatially and temporally complement and expand these observations. We explored all available overpasses (~3250) of the Ocean and Land Colour Instrument (OLCI) on Sentinel-3 within the ice-free periods (May – October) for four years (2018 to 2021) to develop a new retrieval scheme to derive concentrations of DOC. OLCI observations with a spatial resolution of ~300 m were corrected for atmospheric effects using the Polymer algorithm. The results of this study show that using this new retrieval, remotely sensed DOC concentrations agree well with in situ DOC concentrations (MAPD=10.89%, RMSE=1.55 mg L−1, r²=0.92, n=489). The high revisit frequency and wide swath of OLCI allow it to capture the entire range of DOC concentrations and their seasonal variability. Estimated satellite-derived DOC export fluxes integrated over the ice-free periods of 2018 to 2021 show a high interannual variability and agree well with flux estimates from in situ data (RMSD=0.186 Tg C, MAPD=4.05%). In addition, 10-day OLCI composites covering the entire Lena River catchment revealed increasing DOC concentration and local sources of DOC along the Lena from south to north. We conclude that moderate resolution satellite imagers such as OLCI are very capable of observing DOC concentrations in large/wide rivers such as the Lena River despite the relatively coarse spatial resolution. The global coverage of remote sensing offers the expansion to more rivers in order to improve our understanding of the land-ocean carbon fluxes in a changing climate.

Sentinel-1 Backscatter Time Series for Characterization of Evapotranspiration Dynamics over Temperate Coniferous Forests

Forests’ ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount importance for research and monitoring purposes. While there are many biophysical properties, the focus of this study is on the in-depth analysis of the connection between the C-band Copernicus Sentinel-1 SAR backscatter and evapotranspiration (ET) estimates based on in situ meteorological data and the FAO-based Penman–Monteith equation as well as the well-established global terrestrial ET product from the Terra and Aqua MODIS sensors. The analysis was performed in the Free State of Thuringia, central Germany, over coniferous forests within an area of 2452 km2, considering a 5-year time series (June 2016–July 2021) of 6- to 12-day Sentinel-1 backscatter acquisitions/observations, daily in situ meteorological measurements of four weather stations as well as an 8-day composite of ET products of the MODIS sensors. Correlation analyses of the three datasets were implemented independently for each of the microwave sensor’s acquisition parameters, ascending and descending overpass direction and co- or cross-polarization, investigating different time series seasonality filters. The Sentinel-1 backscatter and both ET time series datasets show a similar multiannual seasonally fluctuating behavior with increasing values in the spring, peaks in the summer, decreases in the autumn and troughs in the winter months. The backscatter difference between summer and winter reaches over 1.5 dB, while the evapotranspiration difference reaches 8 mm/day for the in situ measurements and 300 kg/m2/8-day for the MODIS product. The best correlation between the Sentinel-1 backscatter and both ET products is achieved in the ascending overpass direction, with datasets acquired in the late afternoon, and reaches an R2-value of over 0.8. The correlation for the descending overpass direction reaches values of up to 0.6. These results suggest that the SAR backscatter signal of coniferous forests is sensitive to the biophysical property evapotranspiration under some scenarios.

АНАЛИЗ СПУТНИКОВЫХ ИЗМЕРЕНИЙ ВЕГЕТАЦИОННОГО ИНДЕКСА В ПРЕДГОРНЫХ И РАВНИННЫХ РАЙОНАХ РСО-АЛАНИЯ

To analyze the seasonal and long-term variability of the normalized relative vegetation index NDVI, the multichannel MODIS spectroradiometer measurements with a 10x10 km resolution over a 20-year period of time are used. On the territory of the RNO-Alania in the foothill areas and on the plains near the border of the Stavropol Territory five geographical locations are selected, located at different distances from the Northern spurs of the Caucasian Ridge. For each geographical location the sets of time series are obtained for: 16-day NDVI composites, daily temperature at an altitude of 2 m and precipitation for the 20-years period from 01.01.2000 to 31.12.2019. By calculating the integral characteristic for an annual period of time for every location, the series of 20 annual values are obtained. According to the obtained series, three 20-year trends are built and the trend parameters of a first-order polynomial are calculated using the least squares method.&#x0D; The NDVI trend slope switch is observed in the Mozdok-Russkoe location for transition from the time period 2000–2009 to the period 2010–2019, when the slope changes from positive to negative. Similar NDVI trend slope switches are observed for other locations. This may be due changing the level of soil moisture over time, taking into account mainly the downward trends in precipitation. The trend slope of the NDVI integral characteristic 5–15 % increases with the increasing of the distance from the mountain ridge. The temperature trends for all locations are ascending, that indicates an average increase of the number of warm days from year to year. At the same time, the maximum temperature during the summer period does not significantly increase. The trend of the integral characteristic for the amount of precipitation is mainly downward. However, its specific values have a fairly large spread, which means that the amount of precipitation varies significantly from year to year. In addition, the amount of precipitation decreases with distance from mountain ridges.&#x0D; These factors should be taken into account when choosing agricultural products for cultivation and planning additional land-reclamations in the flat and foothill areas on the south of the Russian Federation.

ESTIMATION OF NDVI FOR CLOUDY PIXELS USING MACHINE LEARNING

Abstract. The Normalized Difference Vegetation Index (NDVI) is a useful index for vegetation monitoring. However, due to cloud cover the observations of NDVI are discrete and vary in the intensity. Therefore, there is a need to estimate the NDVI during cloud cover using alternative sources of satellite observations. The main objective of this study is to estimate NDVI during cloudy conditions using moderate resolution multi-spectral and synthetic aperture radar (SAR) observations. Two approaches were identified: 1) pixel replacement and 2) machine learning based regression analysis to estimate cloud free NDVI. Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day NDVI composite, Sentinel-1 SAR and cloud masked Sentinel-2 multi-spectral observations were collected for entire cropping season. The satellite observations were selected only for agricultural areas by applying the agriculture, non-agriculture land use land cover mask. Machine learning algorithms such as Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR) were used for NDVI estimation. Regression analysis was performed using Sentinel-2 NDVI as an independent variable and VV, VH, Cross Ratio (i.e., VV/VH), and MODIS NDVI as dependent variables. NDVI of the cloudy pixel was estimated using the trained regression models over the agriculture areas. A regression model was trained and applied to each Sentinel-2 tile that covers an area of 100 km × 100 km. The RFR and SVR showed the highest R2 of 0.73 and a RMSE of 0.12. A visual comparison of time series graphs showed good alignment between actual (Sentinel-2) and predicted NDVI and usual crop growth trend.

Impact of temporal compositing on nighttime light data and its applications

In recent decades, nighttime light (NTL) images have been widely explored to portray human footprints. Most of the studies used monthly or yearly temporal composite NTL products as a solution for invalid observations due to cloud coverage and outlier signals. However, the impact of temporal compositing on NTL data and its applications remains largely unclear. Here, we utilized over 180,000 daily NTL tiles from NASA's Black Marble VIIRS product (VNP46A2, 2012–2020), covering 230 cities from China and the United States, to delve into the influence of temporal compositing on valid pixel coverage and spatiotemporal pattern of NTL data and the performance of three representative types of NTL-based applications. Our analysis showed temporal compositing was an imperative and efficient solution to the prevailing invalid observations. On average a 16-day composite was required to ensure at least 95% of valid pixel coverage for a city, where a longer composite period was needed for cities in a pluvial temperate climate zone. Compositing daily NTL data into a 3-day to 31-day period markedly reduced its spatiotemporal variation and incurred a 3–9 nWatts/cm2/sr, or 22%–37%, absolute difference in NTL magnitude, which was particularly high in developed cities and intra-city areas. We attributed such effect to the number of valid observations available for generating the composite data and the extremely high variation in daily NTL stemmed from human activities, as well as the uncertainties in VNP46 product and VIIRS instrument. The impact of temporal compositing on NTL-based applications varied greatly, from insignificant to very sensitive, across application types and spaces. Our analysis provides a comprehensive understanding of the capability and uncertainties in NTL data processing and applications, facilitating end-users to make the best use of NTL observations in high temporal frequency.

Crop classification based on the spectrotemporal signature derived from vegetation indices and accumulated temperature

ABSTRACT Due to differences in environmental factors, the phenology of the same crop is different every year, causing divergent performances of the classifier built by spectral or time-series features Here, we proposed a random forest classifier (RFC) based on an asymmetric double S curve model fitted by accumulated temperature (AT) and Vegetation Index (VI), which can be applied in different years without ground samples. We built AT and VI time series from Moderate Resolution Imaging Spectroradiometer 8-day composites of land surface temperatures and Sentinel-2 and Landsat-8, respectively. The RFC was trained by characteristics from the asymmetric double S curve. We prepared RFC by ground samples of 2018 and 2019 and then mapped crops of the same region in 2017. Results indicated that, compared with diverse VI-AT series, the overall accuracy based on universal normalized vegetation index (UNVI) was the best of all (2017: F1 = 0.91, 2018: F1 = 0.92, 2019: F1 = 0.91) and better than that based on the UNVI-TIME series (2017: F1 = 0.84, 2018: F1 = 0.81, 2019: F1 = 0.88). It proved that the classification features from the VI-AT series have smaller intra-class differences in 2017, 2018, and 2019.

Detecting wetland encroachment and urban agriculture land classification in Uganda using hyper-temporal remote sensing.

Background: Urbanization is an important indicator of economic growth and social change but is associated with environmental degradation, which threatens the sustainable growth of African cities. One of the most vulnerable ecosystems in urban areas are wetlands. In Uganda, wetlands cover an area of 11% of the country's land area. Half of the wetland areas in Ugandan cities have been converted to industrial and residential areas, and urban agriculture. There is limited information on the extent of wetland conversion or utilization for urban agriculture. The objective of this study was to investigate the extent of wetlands lost in two Ugandan cities, Wakiso and Kampala, in the last 30 years. Secondly, we extracted crop agriculture in the wetlands of Kampala and Wakiso from hyper-temporal satellite image analysis in an attempt to produce a spatial detail of wetland encroachment maps of urban agriculture using a reproducible mapmaking method. Methods: Using a field survey and free remote sensing data from Landsat TM 1986 and Landsat ETM 2016 we classified the rate of wetland loss and encroachment between the years 1986 and 2016. We used MODIS NDVI 16-day composites at a 500-meter spatial resolution to broaden the analysis to distinguish distinctive crops and crop mixtures in the encroached wetlands for urban agriculture using the ISODATA clustering algorithm. Results: Over 30 years, 72,828 ha (73%) of the Wakiso-Kampala wetlands have been lost meanwhile agriculture areas have doubled. Of this 16,488 ha (23%) were converted from wetlands. All cultivated agriculture in Kampala was in the wetlands while in Wakiso, 73% of crop agriculture was in the wetlands. The major crops grown in these urban wetlands were banana (20%), sugarcane (22%), maize (17%), Eucalyptus trees (12%), sweet potatoes (10%), while ornamental nurseries, pine trees, vegetables, and passion fruits were each at 5%.

Land Cover Dynamics on the Lower Ganges–Brahmaputra Delta: Agriculture–Aquaculture Transitions, 1972–2017

Aquaculture in tropical and subtropical developing countries has expanded in recent years. This practice is controversial due to its potential for serious economic, food security, and environmental impacts—especially for intensive operations in and near mangrove ecosystems, where many shrimp species spawn. While considerable effort has been directed toward understanding aquaculture impacts, maps of spatial extent and multi-decade spatiotemporal dynamics remain sparse. This is in part because aquaculture ponds (ghers) can be challenging to distinguish from other shallow water targets on the basis of water-leaving radiance alone. Here, we focus on the Lower Ganges–Brahmaputra Delta (GBD), one of the most expansive areas of recent aquaculture growth on Earth and adjacent to the Sundarbans mangrove forest, a biodiversity hotspot. We use a combination of MODIS 16-day EVI composites and 45 years (1972–2017) of Landsat observations to characterize dominant spatiotemporal patterns in the vegetation phenology of the area, identify consistent seasonal optical differences between flooded ghers and other land uses, and quantify the multi-decade expansion of standing water bodies. Considerable non-uniqueness exists in the spectral signature of ghers on the GBD, propagating into uncertainty in estimates of spatial extent. We implement three progressive decision boundaries to explicitly quantify this uncertainty and provide liberal, moderate, and conservative estimates of flooded gher extent on three different spatial scales. Using multiple extents and multiple thresholds, we quantify the size distribution of contiguous regions of flooded gher extent at ten-year intervals. The moderate threshold shows standing water area within Bangladeshi polders to have expanded from less than 300 km2 in 1990 to over 1400 km2 in 2015. At all three scales investigated, the size distribution of standing water bodies is increasingly dominated by larger, more interconnected networks of flooded areas associated with aquaculture. Much of this expansion has occurred in immediate proximity to the Bangladeshi Sundarbans.

Disaggregated PROBA-V data allows monitoring individual crop phenology at a higher observation frequency than Sentinel-2

Satellite-based monitoring of crop phenology is commonly built on the analysis of Vegetation Index (VI) time series by extracting phenological metrics. The relatively fine detection of the various timings in crops growth during their development cycle depends on the density and regularity of valid observations. Medium spatial resolution (MSR) daily observations provide consistent cloud-free n-day composite time series, suitable for phenological applications, but do not offer an adequate spatial resolution. MSR pixels are generally mixed pixels or “mixels”, composed of several land cover classes, which complicate crop-specific monitoring from space. To address the MSR mixel problem, we implemented a spatial disaggregation (SD) approach that estimates a crop-specific VI based on the crop fraction in a mixel provided by a land use map. First, SD was applied on synthetic MSR data (i.e. Sentinel-2 data aggregated at 300 m) in order to test the method in an ideal case. After validation, the method was applied to PROBA-V data, using 300 m and 10-day composites over a large area around Paris, for four main crops (i.e. winter cereals, spring barley, oilseed rape and maize) in 2016–2017. The evaluation of SD was done by comparing disaggregated data with reference data (i.e. Sentinel-2 10 m). Indeed, two main results were observed, i) SD was able to reconstruct the crop-specific VI time series of all crops and ii) PROBA-V data increased the number of crop-specific VI valid observations at certain stages of the crop's growth period compared to Sentinel-2 data, this with a consistent and regular revisit throughout the growth cycle. In conclusion, SD can be used to improve the exploitation of MSR data in seasonal crop monitoring, especially during the transition periods when the VI of crops are likely to change quickly. This paves the way for monitoring crop phenology over fragmented landscapes, from sensors such as MODIS or SPOT-VEGETATION, even for years before Sentinel-2 launch.

Influence of Near Real-Time Green Vegetation Fraction Data on Numerical Weather Prediction by WRF over North China

The green vegetation fraction (GVF) can greatly influence the partitioning of surface sensible and latent heat fluxes in numerical weather prediction (NWP) models. However, the multiyear averaged monthly GVF climatology—the most commonly used representation of the vegetation state in models—cannot capture the real-time vegetation state well. In this study, a near real-time (NRT) GVF dataset generated from an 8-day composite of the normalized difference vegetation index is compared with the 10-yr averaged monthly GVF provided by the WRF model. The annual variability of the GVF over North China is examined in detail. Many differences between the two GVF datasets are found over dryland, grassland, and cropland/grassland mosaic areas. Two experiments using different GVF datasets are performed to assess the impacts of GVF on forecasts of screen-level temperature and humidity. The results show that using NRT GVF can lead to a widespread reduction of 2-m temperature forecast errors from April to October. Evaluation against in-situ observations shows that the positive impact on 2-m temperature forecasts in the morning is more distinct than that in the afternoon. Our study demonstrates that NRT GVF can provide a more realistic representation of the vegetation state, which in turn helps to improve short-range forecasts in arid and semiarid regions of North China. Moreover, our study shows that the negative effect of using NRT GVF is closely related to the initial soil moisture.

Modelling climate change impacts on regional net primary productivity in Turkey.

This study projects and models the terrestrial net primary productivity (NPP) considering the representative concentration pathways (RCPs) scenarios of Turkey using remote-sensing-based biogeochemical modelling techniques. Changes in annual NPP between 2000-2010 and 2070-2080 were projected with the biogeochemical ecosystem model NASA-Carnegie Ames Stanford Approach (CASA). A multi-temporal data set, including 16-day MODIS composites with a spatial resolution of 250m, was used within the CASA model. The 5th Assessment Report (AR5) of the IPCC presented several scenarios for RCPs named RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 that laid the foundation for the future climate projections. The futuristic NPP modelling was based on the assumptions of maintaining CO2 level in the range of 421 to 936ppm and a rise in temperature from 1.1 to 2.6°C. The NPP in Turkey averaged 1232g C m2year-1 as per the model results. Considering 2000-2010 as the baseline period, the NPP was modelled within the range of 9.6 and 316g C m2year-1. Modelled average NPP was 1332.4g C m2year-1 per year between 2061 and 2080. The forest productivity was also estimated to be increased up to 113g C m-2year-1 under the climate change scenarios. However, there were minor differences in the projected average NPP under the baseline period covering years from 2000 to 2080 from those under RCPs. It appeared that variation in temperature and precipitation as a result of climate change affected the terrestrial NPP. The regional environmental and socio-economic consequences of climate change on diverse landscapes such as Turkey were properly modelled and analysed to understand the spatial variation of climate change impacts on vegetation. Changes in NPP imply that forests in Turkey could be carbon sinks in the future as their current potential that would profile Turkey's climate mitigation. This is one of the pioneering studies to estimate the future changes of regional NPP in Turkey by integrating various spatial inputs and a biogeochemical model.

Minimum Snow/Ice Extent over the Northern Circumpolar Landmass in 2000–19: How Much Snow Survives the Summer Melt?

AbstractA novel satellite image processing technique developed at the Canada Centre for Remote Sensing has been utilized to produce annual time series of the minimum snow/ice (MSI) extent over the northern circumpolar landmass area (9,000 km × 9,000 km) for 2000–19. The information has been derived from the Moderate Resolution Imaging Spectroradiometer 10-day clear-sky composites generated at 250-m spatial resolution over the April–September period. Derived interannual variations agree very well with the warm-season average surface air temperatures from the European reanalysis (ERA5). The region-average correlation coefficient is −0.78. The total MSI extent demonstrated a statistically significant declining trend equal to −1,477 km2 yr−1. Results have been compared with data from the Randolph Glacier Inventory (RGI 6.0). The comparison points to a significant contribution of minimum seasonal snow cover relative to RGI glacierized areas. Quantitative estimates obtained for the first time showed that the region-average snow extent that survives the summer melt and resides outside of RGI area can be as high as 15% (or 53 × 103 km2) while in the northern Canadian Arctic it can reach 41% (or 43 × 103 km2). The derived MSI time series data can be recommended to the glacier and land-cover scientific community as a source of validation data and annual updates of snow and ice maps over the northern circumpolar landmass.

Суточная и сезонная динамика температуры поверхности города Волгограда

Цель – установить особенности суточной и сезонной динамики температуры поверхности в городе Волгограде (на примере 2018 года, «типового» по климатическим характеристикам) и выяснить, зависит ли её сезонный ход от особенностей подстилающей поверхности городской территории. Материалы и методы. Для выбора года исследования был проанализирован 20-летний ход температур воздуха в Волгограде, а также учитывалось наличие непрерывных данных о температурах подстилающей поверхности. Определение температуры городской поверхности проводилось путем обработки снимков теплого полугодия информационного продукта MOD11A2 (8-ми дневные растровые композиты дневной и ночной температур разрешением около 1 км). Геоинформационная обработка снимков осуществлялась в программе QGIS. По данным Global Land Cover (GLC) определены основные типы подстилающей поверхности на территории Волгограда. Результаты и обсуждение. Доля запечатанных поверхностей составляет 38,2 %. Составлена карта среднегодовых температур поверхности в Волгограде, отражающая основные особенности пространственного распределения положительных и отрицательных тепловых аномалий. Установлено, что сезонный ход среднесуточных, дневных и ночных температур подстилающей поверхности и приземного слоя воздуха в Волгограде представляет собой обращенную ветвями вниз параболу с максимумом, приходящимся на период 26 июня – 6 июля. Ход среднесуточных температур воздуха повторяет сезонный ход температуры поверхности города (r = 0,99). Выводы. Картографирование на основе данных MODIS с разрешением 1 км позволяет выявить пространственное распространение крупных положительных и отрицательных аномалий на территории города и его окрестностей. Выявлена значительная вариабельность значений среднесуточной температуры подстилающей поверхности в районах с высокой долей водных объектов, водно-болотных угодий и низкой степенью запечатанности почвенного покрова.

Landfast Ice Mapping Using MODIS Clear-Sky Composites: Application for the Banks Island Coastline in Beaufort Sea and Comparison with Canadian Ice Service Data

Landfast ice (LFI) is a prominent climatological feature in the Canadian Arctic. LFI is generally defined as immobile near-shore ice that remains fast along the coast and forms seaward from the land. It affects the coastline dynamics, is important for the near-shore ecosystems, wildlife, and human socio-economic activities. A method is proposed for mapping the LFI using time series of 10-day clear-sky composites derived at the Canada Center for Remote Sensing (CCRS) from the Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m imagery. The delineation of coastal zone ice utilizes simultaneous analysis of the mean and standard deviation of MODIS monthly reflectance maps. The application of this method is demonstrated for a 20-year period (2000–2019) over the coastal zone of Banks Island in the Beaufort Sea. Detailed analyses have been conducted for three LFI parameters: (1) the total area (spatial extent) occupied by LFI; (2) the distance from the coast to the outer seaward LFI edge, and (3) the water depth at the outer seaward LFI edge. Comparison with the Canadian Ice Service (CIS) data demonstrates good agreement. The average correlation coefficients between CIS and CCRS time series in April-June, when the area reaches a maximum, are equal to 0.87–0.88. The mean differences (CIS-CCRS) are 344 km2 (5,464 km2 vs 5,120 km2) or 6.3% for the spatial extent; 1.3 km (17.6 km vs 16.3 km) or 7.4% for the distance; −2.7 m (−27.4 m vs −24.7 m) or 10% for the water depth. Because the CCRS method uses monthly statistics, it tends to exclude potentially more mobile continuous landfast ice zones than the CIS analysis which is based on data collected on a specific date. The long-term trends of the LFI seasonal cycle in our region of interest since 2000 have shown a tendency for an earlier break-up, later onset, and longer ice-free period; however, these trends are not statistically significant.

On optimizing a MODIS-based framework for in-season corn yield forecast

Accurate forecast of corn yields is important for decision making regarding food and energy management strategies. In this work, we developed an unprecedented optimized framework for the MODIS-based mid-season corn yield forecasting over five producing states of the United States: Illinois, Indiana, Iowa, Nebraska, and Ohio. We evaluated Enhanced Vegetation Index (EVI)-based forecasts under different schemes accounting for: different machine learning techniques with a mid-season composite or multi-temporal composites as inputs, four (county-, district-, state-, and global-based) training domains, and 16-day composites versus daily interpolated composites involving the day of pixels as predictors. Under the best identified scheme, we compared the EVI-based forecasts with those based on Normalized Difference Vegetation Index (NDVI), Leaf area Index (LAI) and Fraction of Absorbed Photosynthetic Active Radiation (FPAR). EVI and NDVI were transformed to LAI (named as LAIEVI and LAINDVI) as predictors for producing EVI- and NDVI-based forecasts. We evaluated both county- and state-level forecasts using the percent error (PE), mean absolute PE (MAPE) and determination coefficient (R2). The linear regression models driven by the single latest composite in mid-season often outperformed elastic net and random forest models driven by multi-temporal composites. The forecast performance decreased with longer subsets of EVI composites being used. The performance under the different training domains varied by states and forecast level (county or state), although the changes within states were mostly non-significant except in Nebraska. The forecasts based on 16-day and daily composites performed similarly, indicating that the use of information about the day of pixel composite provides no additional benefit to the yield forecast. For the best EVI-based schemes, the medium annual MAPE (PE) at the county (state) level varied between 6.1% (2.4%) and 7.7% (5.3%) across states while the medium annual R2 (interannual R2) varied between 0.54 (0.59) and 0.82 (0.86). Results suggested that, while EVI was, in general, the best predictor for the Corn Belt as a whole, the adequacy of the EVI- and NDVI-based forecasts varied by states and largely exceeded that of the LAI- and FPAR-based forecasts. Compared with the EVI-based forecasts, the NDVI-based forecasts performed better in Iowa (MAPE’s 0.9% and 1.43% lower at the county and state level), similar in Nebraska and worse in the other states. Overall, the best state-level forecasts consistently outperformed concurrent National Agricultural Statistical Service (NASS) forecasts.