Time-series Moderate Resolution Imaging Research Articles

Directional Applicability Analysis of Albedo Retrieval Using Prior BRDF Knowledge

Surface albedo measures the proportion of incoming solar radiation reflected by the Earth’s surface. Accurate albedo retrieval from remote sensing data usually requires sufficient multi-angular observations to account for the surface reflectance anisotropy. However, most middle and high-resolution remote sensing satellites lack the capability to acquire sufficient multi-angular observations. Existing algorithms for retrieving surface albedo from single-direction reflectance typically rely on land cover types and vegetation indices to extract the corresponding prior knowledge of surface anisotropic reflectance from coarse-resolution Bidirectional Reflectance Distribution Function (BRDF) products. This study introduces an algorithm for retrieving albedo from directional reflectance based on a 3 × 3 BRDF archetype database established using the 2015 global time-series Moderate Resolution Imaging Spectro-radiometer (MODIS) BRDF product. For different directions, BRDF archetypes are applied to the simulated MODIS directional reflectance to retrieve albedo. By comparing the retrieved albedos with the MODIS albedo, the BRDF archetype that yields the smallest Root Mean Squared Error (RMSE) is selected as the prior BRDF for the direction. A lookup table (LUT) that contains the optimal BRDF archetypes for albedo retrieval under various observational geometries is established. The impact of the number of BRDF archetypes on the accuracy of albedo is analyzed according to the 2020 MODIS BRDF. The LUT is applied to the MODIS BRDF within specific BRDF archetype classes to validate its applicability under different anisotropic reflectance characteristics. The applicability of the LUT across different data types is further evaluated using simulated reflectance or real multi-angular measurements. The results indicate that (1) for any direction, a specific BRDF archetype can retrieve a high-accuracy albedo from directional reflectance. The optimal BRDF archetype varies with the observation direction. (2) Compared to the prior BRDF knowledge obtained through averaging method, the BRDF archetype LUT based on the 3 × 3 BRDF archetype database can more accurately retrieve the surface albedo. (3) The BRDF archetype LUT effectively eliminates the influence of surface anisotropic reflectance characteristics in albedo retrieval across different scales and types of data.

Evaluating Satellite-Observed Ecosystem Function Changes and the Interaction with Drought in Songnen Plain, Northeast China

Drought is considered one of the devastating natural disasters worldwide. In the context of global climate change, the frequency and intensity of drought have increased, thereby affecting terrestrial ecosystems. To date, the interactions between ecosystem change and drought, especially their mutual lag and cumulative effects is unclear. The Songnen Plain in northeastern China is one of the three major black soil areas in the world and is highly sensitive to global change. Herein, to quantify the interaction between drought and ecosystem function changes in the Songnen Plain, integrating with time-series moderate resolution imaging spectroradiometer (MODIS), leaf area Index (LAI), evapotranspiration (ET), and gross primary productivity (GPP) data, we calculated the standardized precipitation and evapotranspiration index (SPEI) based on the meteorological data, diagnosed the causal relationship between SPEI and the ecosystem function indicators i.e., LAI, ET, and GPP, and analyzed the time-lag and cumulative effects between the degree of drought and three ecosystem function indicators using impulse response analysis. The results showed that the trend of SPEI (2000–2020) was positive in the Songnen Plain, indicating that the drought extent had eased towards wetness. LAI showed insignificant changes (taking up 88.34% of the total area), except for the decrease in LAI found in some forestland and grassland, accounting for 9.43%. The pixels showing a positive trend of ET and GPP occupied 24.86% and 54.94%, respectively. The numbers of pixels with Granger causality between LAI and SPEI (32.31%), SPEI and GPP (52.8%) were greater at the significance 0.05 level. Impulse responses between each variable pair were stronger mainly between the 6th and 8th months, but differed significantly between vegetation types. Grassland and cropland were more susceptible to drought than forest. The cumulative impulse response coefficients values indicated that the mutual impacts between all variables were mainly positive. The increased wetness positively contributed to ecosystem function, and in turn enhanced ecosystem function improved regional drought conditions to some extent. However, in the northeastern forest areas, the SPEI showed a significant negative response to increased ET and GPP, suggesting that the improved physiological functions of forest might lead to regional drought. There were regional differences in the interaction between drought conditions and ecosystem function in the Songnen Plain over the past 21 years.

Comparison of time-integrated NDVI and annual maximum NDVI for assessing grassland dynamics

The saturation of NDVI makes the annual maximum NDVI(NDVImax) underestimate vegetation with high coverage. Time-Integrated NDVI(TI-NDVI) accumulates NDVIs of vegetation at different growing stages in one growing season. TI-NDVI might alleviate the saturation effect of NDVImax when vegetation dynamics are assessed at annual time scale. Taking grassland in Ili Valley at the west end of Tianshan Mountain as the study area, this paper compared NDVImax and TI-NDVI systematically for assessing grassland dynamics and also their correlations with precipitation and temperature. Data of time-series Moderate Resolution Imaging Spectroradiometer(MODIS) NDVI were used and methods of Mann-Kendall trend test, Sen's slope estimator test, coefficient of variation(CV) and partial correlation coefficient were employed. We found that there were 28.69% grassland in Ili Valley where the TI-NDVI and NDVImax showed different changing trends over the years of 2000–2019. TI-NDVI not only revealed more about the spatial and temporal heterogeneities of grassland with high coverage than NDVImax at annual time scale, but also captured more about the close relationship between grassland dynamics and precipitation or temperature in Ili Valley.

Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine

Double cropping is an important cropping system in China, with more than half of China’s cropland adopting the practice. Under the background of global climate change, agricultural policies, and changing farming practices, double-cropping area has changed substantially. However, the spatial-temporal dynamics of double cropping is poorly understood. A better understanding of these dynamics is necessary for the northern limit of double cropping (NLDC) to ensure food security in China and the world and to achieve zero hunger, the second Sustainable Development Goal (SDG). Here, we developed a phenology-based algorithm to identify double-cropping fields by analyzing time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images during the period 2000–2020 using the Google Earth Engine (GEE) platform. We then extracted the NLDC using the kernel density of pixels with double cropping and analyzed the spatial-temporal dynamics of NLDC using the Fishnet method. We found that our algorithm accurately extracted double-cropping fields, with overall, user, and producer accuracies and Kappa coefficients of 95.97%, 96.58%, 92.21%, and 0.91, respectively. Over the past 20 years, the NLDC generally trended southward (the largest movement was 66.60 km) and eastward (the largest movement was 109.52 km). Our findings provide the scientific basis for further development and planning of agricultural production in China.

A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) has been served as an effective tool to assess the terrestrial carbon budget for the entire globe since 2000. However, the current MODIS GPP product neglects the surface heterogeneity in the modeling process and is always generated at 500 m or 1 km resolution, which could cause errors to these estimates over mountainous areas. In this work, the MODIS GPP model (MOD17) was applied to obtain 1 km GPP estimates at eleven mountainous sites. Then, a topographic correction method based on three indexes associated with the spatial heterogeneity of received radiation (TCIAPAR), temperature (TCITMIN), and water (TCIVPD) stresses was developed to reduce GPP errors in these MOD17-simulated estimates. Results showed that a closer relationship between tower-based GPP and MOD17-simulated GPP was achieved after applying the proposed topographic correction method, with the determination coefficient (R2) increased from 0.61 to 0.74 and root mean square error (RMSE) reduced from 24.24 to 14.56 gC m−2 8d−1 at all the eleven mountainous sites. As for the effectiveness of each topographic correction index, an obvious improvement of MOD17-simulated GPP was observed after TCIAPAR correction (increasing R2 by 0.09 and decreasing RMSE by 8.75 gC m−2 8d−1), TCITMIN correction (increasing R2 by 0.05 and decreasing RMSE by 7.80 gC m−2 8d−1), and TCIVPD correction (increasing R2 by 0.06 and decreasing RMSE by 7.89 gC m−2 8d−1), indicating that the spatial heterogeneity information of radiation, temperature, and water within coarse pixels is necessary for improving the MODIS GPP over mountainous areas. It is notable that the combination of the TCIAPAR, TCITMIN, and TCIVPD corrections was found to have the largest improvement for MOD17-simulated GPP (increasing R2 by 0.13 and decreasing RMSE by 9.68 gC m−2 8d−1), indicating that the combined consideration of topographic factors in the correction process might achieve a larger improvement. This study highlights the feasibility of incorporating surface topographic characteristics into current coarse resolution GPP products in obtaining large-scale mountain GPP estimates.

Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

Modeling and Assessment of Land Degradation Vulnerability in Semi-arid Ecosystem of Southern India Using Temporal Satellite Data, AHP and GIS

Globally, land degradation becomes a serious environmental issue in the context of anthropogenic pressure and climate change in the twenty-first century. Modeling and assessment of land degradation vulnerability assume a greater importance especially in the semi-arid ecosystems for sustainable land resource management. Consistent and reliable earth observation satellite datasets, Analytic Hierarchy Process (AHP), and Geographic Information System (GIS) are the powerful tools to model and assess the land degradation vulnerability. The present study was aimed to model and assess the land degradation vulnerability through analysis of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), rainfall, terrain characteristics, and pedological parameters by using AHP and GIS in the semi-arid ecosystem of Rayalaseema region of southern India. The NDVI and LST products derived from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, rainfall products from Tropical Precipitation Measuring Mission (TRMM), terrain characteristics from Shuttle Radar Topography Mission (SRTM) (30 m), and pedological parameters derived from legacy soil datasets were used in the study. The AHP- and GIS-based modeling shows that about 25.3 and 9.9% of the study area were under high and very high vulnerability to land degradation, respectively. Inadequate rainfall and vegetative cover, high temperature, problematic soils, and lack of adequate conservation measures were found to be the main causative environmental factors for land degradation in the study area. The study clearly demonstrates the potential of AHP- and GIS-based modeling in the assessment of land degradation vulnerability by using the time-series MODIS NDVI- and TRMM-based rainfall products, terrain characteristics, and pedological parameters.

Extreme Climate Event and Its Impact on Landscape Resilience in Gobi Region of Mongolia

The dzud, a specific type of climate disaster in Mongolia, is responsible for serious environmental and economic damage. It is characterized by heavy snowfall and severe winter conditions, causing mass livestock deaths that occur through the following spring. These events substantially limit socioeconomic development in Mongolia. In this research, we conducted an analysis of several dzud events (2000, 2001, 2002, and 2010) to understand the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. The present paper also establishes how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (aAIZ), time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and livestock data. We also correlated aAIZ, NDVI, and seasonal precipitation in the varied ecosystems of the study area. The results illustrate that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in humid areas. Another result is that snowy winters can cause a 10 to 20-day early peak in NDVI and a following increase in vegetation growth. During a drought year with dry winter conditions, the vegetation growth phase begins later due to water deficiency, which leads to weaker vegetation growth. Livestock loss and the reduction of grazing pressure play a crucial role in vegetation recovery after extreme climatic events in Mongolia.

Machine learning approaches for rice crop yield predictions using time-series satellite data in Taiwan

ABSTRACT Rice is the most important food crop in Taiwan, directly feeding more than 23 million people in the country. Information on rice production is thus crucial for crop management and food policymaking. This study aims to develop a machine learning (ML) approach for predicting rice crop yields in Taiwan using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data. We processed the data for the period from 2000 to 2018, following three main steps: (1) data pre-processing to generate smooth time-series Normalized Difference Vegetation Index (NDVI) data, (2) establishment of models for yield predictions using the heading date (HD) NDVI value, and the accumulated NDVI value of the dates from heading to maturity (DHM). The data from 2000 to 2017 were used for building predictive models using the random forests (RF) and support vector machines (SVM), leaving the 2018 data for model assessment, and (3) evaluation of model performance. The results compared with the government’s yield statistics indicated good predictions, with the root mean square error (RMSE) and mean absolute error (MAE) values between 7.1% and 11.8%, and Willmott’s index of agreement (d) values between 0.81 and 0.84 for the first crop, and 5.6% and 11.3% and d values between 0.91 and 0.95 for the second crop, respectively. A slight underestimation of yield predictions was observed for both crops, with the relative error (RE) values of −6.5% to −8.2% and −3.8% to −6% for the first and second crops, respectively. The results of regression analysis also confirmed a close agreement between these two datasets, with the correlation coefficient (r) higher than 0.84 (p-value <0.05), in both cases. Although some factors, including mixed-pixel issues, boundary effects, and cloud cover potentially affected the modelling results, our study demonstrated the effectiveness of ML methods for regional rice yield predictions from MODIS NDVI data in Taiwan.

Estimation of Seasonal Evapotranspiration for Crops in Arid Regions Using Multisource Remote Sensing Images

An accurate estimation of evapotranspiration (ET) from crops is crucial in irrigation management, crop yield assessment, and optimal allocation of water resources, particularly in arid regions. This study explores the estimation of seasonal evapotranspiration for crops using multisource remote sensing images. The proposed estimation framework starts with estimating daily evapotranspiration (ETd) values, which are then used to calculate ET estimates during the crop growing season (ETs). We incorporated Landsat images into the surface energy balance algorithm over land (SEBAL) model, and we used the trapezoidal and sinusoidal methods to estimate the seasonal ET. The trapezoidal method used multitemporal ETd images, while the sinusoidal method employs time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images and multitemporal ETd images. Experiments were implemented in the agricultural lands of the Kai-Kong River Basin, Xinjiang, China. The experimental results show that the obtained ETd estimates using the SEBAL model are comparable with those from the Penman–Monteith method. The ETs obtained using the trapezoidal and sinusoidal methods both have a relatively high spatial resolution of 30 m. The sinusoidal method performs better than the trapezoidal method when using low temporal resolution Landsat images. We observed that the omission of Landsat images during the middle stage of crop growth has the greatest impact on the estimation results of ETs using the sinusoidal method. Based on the results of the study, we conclude that the proposed sinusoidal method, with integrated multisource remote sensing images, offers a useful tool in estimating seasonal evapotranspiration for crops in arid regions.

Assessment of spatio-temporal vegetation dynamics in tropical arid ecosystem of India using MODIS time-series vegetation indices

In the present study, we analyzed spatio-temporal vegetation dynamics to identify and delineate the vegetation stress zones in tropical arid ecosystem of Anantapuramu district, Andhra Pradesh, India, using Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Vegetation Anomaly Index (VAI) derived from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day products (MOD13Q1) at 250 m spatial resolution for the growing season (June to September) of 19 years during 2000 to 2018. The 1-month Standardized Precipitation Index (SPI) was computed for 30 years (1989 to 2018) to quantify the precipitation deficit/surplus regions and assess its influence on vegetation dynamics. The growing season mean NDVI and VCI were correlated with growing season mean 1-month SPI of dry (2003) and wet (2007) years to analyze the spatio-temporal vegetation dynamics. The correlation analysis between SPI and NDVI for dry year (2003) showed strong positive correlation (r = 0.89). Analysis of VAI for dry year (2003) indicates that the central, western, and south-western parts of the district reported high vegetation stress with VAI of less than − 2.0. This might be due to the fact that central and south-western parts of the district are more prone to droughts than the other parts of the district. The correlation analysis of SPI, NDVI, and VCI distinctly shows the impact of rainfall on vegetation dynamics. The study clearly demonstrates the robustness of NDVI, VCI, and VAI derived from time-series MODIS data in monitoring the spatio-temporal vegetation dynamics and delineate vegetation stress zones in tropical arid ecosystem of India.

Exploring the Spatio-Temporal Dynamics of Winter Rape on the Middle Reaches of Yangtze River Valley Using Time-Series MODIS Data

Rapeseed is an important oil product in China. China’s current soybean trade issues with major soybean producing countries have caused a large decline in soybean import since 2017. This may bring the increasing needs of rapeseed import, which would have an impact on domestic production. However, our knowledge on the effects of international rapeseed trade on domestic production remains unknown. It is thus important to understand the pattern of rapeseed in China under this scenario, as it may provide necessary information for all relevant stakeholders. With this goal, this study aims to investigate the spatial and temporal patterns of winter rape in China’s major winter rape production region, the Middle Reaches of the Yangtze River Valley (MYR), during 2003–2015 using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data. A decision tree according to the difference in enhanced vegetation index (EVI) profiles of land-cover types was built to extract winter rape. The results show that there is an essential decrease in both the number and density of winter rape patches under the opening global rapeseed market. There are significant hotspots of winter rape gain and loss, within which the loss dominated the trend. The significant cost advantage of rapeseed in the international market may largely reduce the domestic cultivation in China through telecoupling effects. Understanding the spatio-temporal dynamics of winter rape on the MYR has significant economic and policy implications and can provide great supports for the agricultural production, policy-making, and oil products trade in the international market.

Vertical difference of climate change impacts on vegetation at temporal-spatial scales in the upper stream of the Mekong River Basin

As the upper section of the Mekong River Basin, the vegetation quality of the Lancang River Basin (LRB) and the related ecological functions are critical for the whole basin. With time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2015 and local daily climatic data since 1976, their vertical interaction differences were identified. The results showed that the spatial variation in Normalized difference vegetation index (NDVI) of grassland and forest were sensitive to elevation. The NDVI value in the southern area at elevations less than 3000 m was more than 0.80 and decreased to 0.30–0.60 with elevations higher than 4500 m. The general vegetation quality showed a positive trend under climate change over 16 years. The M-K test of daily precipitation and temperature from 12 local weather stations showed that the basin temperature varied more significantly than precipitation. The temporal correlation between NDVI with precipitation as well as temperature at each pixel indicated that temperature was the dominant factor affecting grassland and forest dynamics in the LRB. The interaction between vegetation and climate was more sensitive at elevations lower than 3000 m. Based on the RCP4.5 scenario, the future temperature distribution was predicted, and its impact on NDVI was simulated at the pixel scale. Under future drier and warmer climate conditions, the responded NDVI in the upper stream with higher elevation may increase soil erosion and decrease streamflow. The NDVI in the downstream area will be improved and be able to adapt to the related climate impacts. Because of the large amount of water and biomass in this basin, higher temperatures will accelerate the decomposition of forest foliar litter. Thus, more organic carbon and forest diffuse pollution will be discharged into the water, potentially affecting the water quality of the whole basin.

CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China

The periodic determination of land use changes over large areas is crucial for improving our understanding of land system dynamics. Jiangsu lies at the center of China’s Yangtze Delta and has one of the fastest-developing economies in China. However, it is also a region where serious conflicts exist between the available land resources and the human demand for land. To address these conflicts, it is important to analyze the patterns of land use change in Jiangsu, as they can serve as a useful reference for other rapidly urbanizing regions in China as well as other developing countries. In this study, we propose a method of classification and regression tree-random forest (CART-RF) classification with a multifilter based on time-series Moderate Resolution Imaging Spectroradiometer (MODIS) imaging data. The proposed method integrates the CART decision tree and the random forest algorithms (CART-RF) to obtain accurate yearly land use data for large areas from multivariate time-series remote sensing data and employs a spatial-temporal-logical filter to exclude any abnormal changes in the multivariate time-series pixel data. The obtained results indicated that (1) the CART-RF classifier is effective for land use classification based on the multivariate time-series MODIS data, with the overall classification accuracy being greater than 90%; (2) the use of the proposed combinatorial spatial-temporal-logical filtering method effectively eliminates most anomalous changes and minimizes the effects of “salt-and-pepper” noise; and (3) from 2000 to 2015, land use in Jiangsu province underwent significant and spatiotemporally heterogeneous changes on a province-wide scale, owing to various factors, such as those related to the economy, location, and government policies. These changes were manifested as continuous expansions in the built-up land at the expense of farmland. While this expansion of built-up land has been very rapid in southern Jiangsu, especially in the region close to Yangtze River Delta, it has been relatively slower in northern Jiangsu.

Hydrological Condition Constrains Vegetation Dynamics for Wintering Waterfowl in China’s East Dongting Lake Wetland

As an internationally important wintering region for waterfowls on the East Asian–Australasian Flyway, the national reserve of China’s East Dongting Lake wetland is abundant in animal and plant resources during winter. The hydrological regimes, as well as vegetation dynamics, in the wetland have experienced substantial changes due to global climate change and anthropogenic disturbances, such as the construction of hydroelectric dams. However, few studies have investigated how the wetland vegetation has changed over time, particularly during the wintering season, and how this has directly affected habitat suitability for migratory waterfowl. Thus, it is necessary to monitor the spatio-temporal dynamics of vegetation in the protected wetland and explore the potential factors that alter it. In this study, the data set of time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) from 2000 to 2018 was used to analyze the seasonal dynamics and interannual trends of vegetation over the wintering period from October to January. The results showed that the average NDVI exhibited an overall increasing trend, with the trend rising slowly in recent years. The largest monthly mean NDVI generally occurred in November, which is pertinent to the quantity of wintering waterfowl in the East Dongting Lake wetland. Meanwhile, the mean NDVI in the wintering season is significantly correlated to temperature and water area, with apparent lagging effects. Long-term stability analysis presented a gradually decreasing pattern from the central body of water to the surrounding area. All analyses will help the government to make appropriate management strategies to protect the habitat of wintering waterfowl in the wetland.

Forest drought-induced diversity of Hyrcanian individual-tree mortality affected by meteorological and hydrological droughts by analyzing moderate resolution imaging spectroradiometer products and spatial autoregressive models over northeast Iran

This study sought to assess the spatial variations of physiological responses of Hyrcanian forests to the hazard intensity of meteorological and hydrological droughts for properly assessing drought-induced tree mortality in northeastern Iran. A variety of time series moderate resolution imaging spectroradiometer (MODIS) products and ground-based measurements were applied to derive the multiple dimensions of droughts and forest stresses. Drought hazard intensity was computed with the combination of the severity, frequency and duration of drought dimensions for each variable. The intensity of tree mortality was calculated by Simpson’s diversity index with surveying 30,000 individuals of commercial species suspected to dieback within 100 intact parcels. Spatial autoregressive models were carried out to determine significant meteorological and hydrological drivers that controlling biological responses of forests to drought events and associations of the diversity of tree mortality with these forest responses. Results showed that the hazard intensity of forest water-content-deficit and greenness loss showed higher relationships with the high land surface temperatures and actual evapotranspiration than the precipitation and surface water deficits, however, they did not show significant relationships with the groundwater deficit. Moreover, diversity of tree mortality was associated with forest water-content-deficit from moderate to death stages and with forest greenness loss in the only very high defoliation stage. The critical values of forest droughts and diversity of mortality were recorded for the climax tree species. Understanding satellite-derived physiological responses of forests to droughts might help to assess the intensity of tree mortality widely to adopt appropriate strategies for mitigating the impacts of droughts on the tree species.

Monitoring Spatio-Temporal Changes of Terrestrial Ecosystem Soil Water Use Efficiency in Northeast China Using Time Series Remote Sensing Data.

Soil water use efficiency (SWUE) was proposed as an effective proxy of ecosystem water use efficiency (WUE), which reflects the coupling of the carbon–water cycle and function of terrestrial ecosystems. The changes of ecosystem SWUE at the regional scale and their relationships with the environmental and biotic factors are yet to be adequately understood. Here, we aim to estimate SWUE over northeast China using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity data and European Space Agency climate change initiative (ESA CCI) soil moisture product during 2007–2015. The spatio-temporal variations in SWUE and their linkages to multiple factors, especially the phenological metrics, were investigated using trend and correlation analysis. The results showed that the spatial heterogeneity of ecosystem SWUE in northeast China was obvious. SWUE distribution varied among vegetation types, soil types, and elevation. Forests might produce higher photosynthetic productivity by utilizing unit soil moisture. The seasonal variations of SWUE were consistent with the vegetation growth cycle. Changes in normalized difference vegetation index (NDVI), land surface temperature, and precipitation exerted positive effects on SWUE variations. The earlier start (SOS) and later end (EOS) of the growing season would contribute to the increase in SWUE. Our results help complement the knowledge of SWUE variations and their driving forces.

Developing a Method to Estimate Maize Area in North and Northeast of China Combining Crop Phenology Information and Time-Series MODIS EVI

Remote sensing (RS) is a convenient technology to estimate the regional cultivation areas of crops. However, the accurate estimation of maize areas using RS over a broad region is a significant challenge due to the large phenology differences and insufficient prior knowledge in space. To address this issue, a new method was developed in this work. In this method, the correlation (r ) and root mean standard error (RMSE) between the time-series moderate resolution imaging spectroradiometer enhanced vegetation index (MODIS EVI) and the standard EVI curve of maize from a reference area are computed. Pixels with a high value of r and a low value of RMSE were identified as maize areas. The phenology information observed at agro-meteorological stations was also used to recognize maize pixels from the pixel-level phenology derived from time-series MODIS EVI. The proposed method provides an accurate characterization of the phenology differences over the study area by making use of the planting and maturity dates only. In addition, the few location-dependent parameters make the recognition of maize planting areas over large regions easier than previous studies. The proposed method was implemented over the Northeast China Plain (NECP) and North China Plain (NCP). The derived results were compared with official statistical results, and a close agreement was observed. At the city level, the satellite-derived estimates agreed well with the statistics with the R 2 (RMSE) of 0.86(110.97 k hm 2 ) in the NECP and 0.76(68.74 k hm 2 ) in the NCP. At the county level, the R 2 (RMSE) is 0.82(25.47 k hm 2 ) in the NECP and 0.75(5.93 k hm 2 ) in the NCP. At both temporal levels, the R 2 (RMSE) results obtained in this work are higher(lower) than those published in other studies. The obtained results indicate that the proposed method is effective in maize area estimation over broad regions.

Identifying crop planting areas using Fourier-transformed feature of time series MODIS leaf area index and sparse-representation-based classification in the North China Plain

ABSTRACTAccurate information of crop distribution is vital for agricultural industry and food security, which gives rise to a strong demand for timely crop mapping. This study applies a method that combines sparse representation and harmonic characteristics derived from time series Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) to identify crop planting areas to the north of the Yellow River in the North China Plain, where winter wheat and maize are widely cultivated. The upper envelope Savitzky–Golay filter was applied on the yearly time series MODIS LAI pixel by pixel to minimize the effects of anomalous values caused by atmospheric variability and cloud contamination. The Fourier transform method was then employed to extract the key parameters from the Savitzky–Golay filtered LAI. Totally about 11 parameters were extracted, including the amplitudes of the terms 0–5 and the phases of the terms 1–5 of the Savitzky–Golay filtered LAI, and were taken as the features for the crop identification. Based on the training samples of the identified crops, which were obtained through field campaigns and Google Earth images, the online dictionary learning algorithm was applied to construct the dictionary for identifying the crops. With the dictionary, orthogonal matching pursuit algorithm was applied to obtain sparse representation coefficients of the identified samples. Then crops were identified using the minimum reconstruction errors which were calculated by the dictionary and the sparse representation coefficients of each category. Winter wheat, spring maize, summer maize, cotton, and orchard planting areas from 2003 to 2016 in the study area were identified. The accuracy of the identification was evaluated by the confusion matrix. Average overall identification accuracy in the 14 years was 78.67%, with a kappa coefficient (κ) of 0.75. Annual overall accuracy from 2003 to 2016 was from 70.57% to 83.71%, and κ were from 0.66 to 0.81. These results indicate that the accuracy of crop identification is high. Compared with traditional classification methods, such as supervised classification, the approach developed in this study is feasible and efficient for identifying crop distribution in the study area. Repeated and expensive ground sampling year after year is not needed, which effectively reduces computation cost. The findings of this study can provide valuable implications for crop condition monitoring and yield estimation in the region.

Crop Cycles and Crop Land Classification in Nepal Using MODIS NDVI

Nepal is an agrarian country located in the Southeast Asian region with unique climate and geographical features. There are three geographical regions in the country: (a) mountain, (b) hill, and (c) plain (termed as himal, pahad, and tarai in the Nepali language) with distinct cropping patterns. Limited land in the hill and mountain regions is farm-friendly due to their topography, climate, and soil quality. The tarai region, which is often called the “food granary” of the country, has been subject to widespread urban expansion since few decades. Since the updated crop-based land use classification information and crop cycle maps of the country are not available yet, invitatory of crop cycles in Nepal (ICCN), detailed monitoring of crop area (DMCA), and its mapping are very essential for the formulation of strategic agricultural plans which ensure food security. This is a simple and operative work for the development of cropping maps with cropland classification of the country using the time series Moderate Resolution Imaging Spectro-radiometer (MODIS) 250 m of 2016. Time series MODIS 250-m vegetation index (VI) data hold the effective results for large-area cropping mapping. All obtainable MODIS normalized difference vegetation index (NDVI) images were grouped and analyzed in TerrSet software and Earth Trends Modeler (ETM) was used for the evaluation of crop profile. The accuracy assessment of crop-based land use classification maps was achieved using a random sampling method. The results demonstrate the substantial information of the crop cycle and crop-based land-cover change in the context of Nepal. Hence, this study is likely to remain as the important reference document for Nepalese scientists and planners for establishing innovative agricultural plans at regional and national levels.