Chaoqing Yu

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the Midwestern US with low errors for both corn (R2 = 0.77; RMSE = 0.89 MT/ha) and soybeans (R2 = 0.66; RMSE = 0.38 MT/ha). Because the proposed algorithm does not require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.

Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions

Agricultural droughts can create serious threats to food security. Tools for dynamic prediction of drought impacts on yields over large geographical regions can provide valuable information for drought management. Based on the DeNitrification-DeComposition (DNDC) model, the current research proposes a Drought Risk Analysis System (DRAS) that allows for the scenario-based analysis of drought-induced yield losses. We assess impacts on corn yields using two case studies, the 2012 U.S.A. drought and the 2000 and 2009 droughts in Liaoning Province, China. The results show that the system is able to perform daily simulations of corn growth and to dynamically evaluate the large-scale grain production in both regions. It is also capable of mapping the up-to-date yield losses on a daily basis, the additional losses under different drought development scenarios, and the yield-based drought return periods at multiple scales of geographic regions. In addition, detailed information about the water-stress process, biomass development, and the uncertainty of drought impacts on crop growth at a specific site can be displayed in the system. Remote sensing data were used to map the areas of drought-affected crops for comparison with the modeling results. Beyond the conventional drought information from meteorological and hydrological data, this system can provide comprehensive and predictive yield information for various end-users, including farmers, decision makers, insurance agencies, and food consumers. A Drought Risk Analysis System for daily evaluating and predicting large-scale drought-induced yield losses during the 2012 U.S. drought.Detailed information available for the crop water-stresses processes, biomass development, and uncertainties of drought impacts.Dynamic and scale-dependent drought return periods according to yield losses demonstrated in the case study of Liaoning, China.Remotely sensed data provided essential information for mapping and verifying the drought-affected areas.

A GIS-supported impact assessment of the hierarchical flood-defense systems on the plain areas of the Taihu Basin, China

The Taihu Basin is located in the east coast of China, with a total area of 36,895 km2. Low-lying floodplain areas occupy about 83% of the basin. The threat of frequent floods to this economically important area has stimulated construction of enormous flood-defense projects along the complex system of rivers and lakes. Digital modeling of flooding processes and quantitative assessment of flood damages in this basin remain challenging due to the complexity. This article reports on an approach to simulate the flooding processes, which integrates hydrological and hydraulic modeling with dike-reliability analysis and socioeconomic information within a GIS platform. A new algorithm is introduced to calculate the influence of the flood-defense systems on spatial distributions of floodwater and consequential damages. Scenario analysis indicates that the modeling is particularly sensitive to the assumed rainfall, dike reliability, and the pump capacities within local polders. The model is validated by comparison with observations from historical flood records. The analysis reveals that the defense systems have significantly reduced the basin-wide flood risk and changed the spatial distributions of floodwater. Such a GIS-based approach can be potentially used to assess the benefit from construction of flood defenses and to avoid unintended spatial redistribution of flooding.

Using a global reference sample set and a cropland map for area estimation in China

A technically transparent and freely available reference sample set for validation of global land cover mapping was recently established to assess the accuracies of land cover maps with multiple resolutions. This sample set can be used to estimate areas because of its equal-area hexagon-based sampling design. The capabilities of these sample set-based area estimates for cropland were investigated in this paper. A 30-m cropland map for China was consolidated using three thematic maps (cropland, forest and wetland maps) to reduce confusion between cropland and forest/wetland. We compared three area estimation methods using the sample set and the 30 m cropland map. The methods investigated were: (1) pixel counting from a complete coverage map, (2) direct estimation from reference samples, and (3) model-assisted estimation combining the map with samples. Our results indicated that all three methods produced generally consistent estimates which agreed with cropland area measured from an independent national land use dataset. Areas estimated from the reference sample set were less biased by comparing with a National Land Use Dataset of China (NLUD-C). This study indicates that the reference sample set can be used as an alternative source to estimate areas over large regions.

Exploring the correlations between ten monthly climatic variables and the vegetation index of four different crop types at the global scale

ABSTRACT The relationship between vegetation index (VI) and climatic variables such as temperature (TEP) and precipitation (PRE) at local, regional and global scales are conventionally analysed to understand the responses of vegetation to climate change. Those unique responses also afford opportunities for using climate variables to discriminate vegetation types. This paper presents a data-driven analysis to explore correlations between ten monthly climatic variables (temperature, precipitation, potential evapotranspiration (PET), vapour pressure (VAP), wet days (WET), and others) and monthly VIs of four different crop types (maize, rice, soybeans, and wheat) at global scale. The purpose is to show the VI–climate correlations in a spatially explicit way, laying the foundation for better crop type mapping by integrating climatic variables and remote sensing. The results show large variations in VI–climate correlation for different crop types and regions. Most cropland areas in the world show strong correlations between VI and VAP, and other variables such as WET, PET, and monthly average daily minimum temperature (TMN). This result encourages future studies using additional climate variables (in addition to TMP and PRE) for detailed vegetation/crop-type mapping.

Comparison of country-level cropland areas between ESA-CCI land cover maps and FAOSTAT data

ABSTRACT Long-term time series of spatially explicit cropland maps are essential for global crop modelling and climate change studies. The spatial resolution and temporal continuity of global cropland maps have been improving and several global data sets are released recently. Here, we calculated country-level cropland areas from the annual land-cover (LC) maps produced by the European Space Agency Climate Change Initiative (ESA-CCI) project and from the Food and Agricultural Organization of the United Nations statistical data (FAOSTAT) from 1992 to 2014. Because these two data sets used different approaches for generating the cropland data, we further quantified the consistency/difference in cropland areas and temporal changes between both products. Using log-transformed the time-averaged country-level cropland areas, a good linear relationship was found between these two products across different countries. However, only 8% of countries (mostly Organization for Economic Co-operation and Development countries) showed cropland area difference smaller than 1% between ESA-CCI and FAOSTAT. The cropland areas (without mosaic cropland types) from ESA-CCI are lower than the areas from FAOSTAT in 26% of countries but higher in 66% of countries. The magnitude of the latter difference (i.e. higher estimates of ESA-CCI than FAOSTAT) would be further amplified if crop intensity was taken into account. In addition, opposite temporal trends of cropland areas were found between these two data sets in 41% of countries. Although there are uncertainties in ESA-CCI LC maps, resulting from remote-sensing techniques such as mixed pixels, spectral similar objects, and same subject with different spectrum, the long time series and relatively high resolution of this product help us to understand the differences between satellite-based and inventory-based data sets and thus identify the possible strategies to improve the accuracy of satellite-based LC products.

Assessing the Impacts of Extreme Agricultural Droughts in China Under Climate and Socioeconomic Changes

Agricultural food production in China is deeply vulnerable to extreme droughts. Although there are many studies to evaluate this issue from different aspects, comprehensive assessments with full consideration of climate change, crop rotations, irrigation effects, and socioeconomic factors in broad scales have not been well addressed. Considering both the probability of drought occurrence and the consequential yield losses, here we propose an integrated approach for assessing past and future agricultural drought risks that relies on multimodel ensemble simulations calibrated for rice, maize, and wheat (RMW) in China. Our results show that irrigation has reduced drought-related yield losses by 31 ± 2\%; the largest reductions in food production were primarily attributable to socioeconomic factors rather than droughts during 1955–2014. Unsustainable water management, especially groundwater management, could potentially cause disastrous consequences in both food production and water supply in extreme events. Our simulations project a rise of 2.5 3.3\% in average rice, maize, and wheat productivity before 2050 but decrease thereafter if climate warming continues. The frequency of extreme agricultural droughts in China is projected to increase under all examined Representative Concentration Pathway (RCP). A current 100-year drought is projected to occur once every 30 years under RCP 2.6, once every 13 years under RCP 4.5, and once every 5 years under RCP 8.5. This increased occurrence of severe droughts would double the rate of drought-induced yield losses in the largest warming scenario. Policies for future food security should prioritize sustainable intensification and conservation of groundwater, as well as geographically balanced water resource and food production.

Regional disparities in warm season rainfall changes over arid eastern–central Asia

Multiple studies have reported a shift in the trend of warm season rainfall over arid eastern–central Asia (AECA) around the turn of the new century, from increasing over the second half of the twentieth century to decreasing during the early years of the twenty-first. Here, a closer look based on multiple precipitation datasets reveals important regional disparities in these changes. Warm-season rainfall increased over both basin areas and mountain ranges during 1961–1998 due to enhanced moisture flux convergence associated with changes in the large-scale circulation and increases in atmospheric moisture content. Despite a significant decrease in warm-season precipitation over the high mountain ranges after the year 1998, warm season rainfall has remained large over low-lying basin areas. This discrepancy, which is also reflected in changes in river flow, soil moisture, and vegetation, primarily results from disparate responses to enhanced warming in the mountain and basin areas of AECA. In addition to changes in the prevailing circulation and moisture transport patterns, the decrease in precipitation over the mountains has occurred mainly because increases in local water vapor saturation capacity (which scales with temperature) have outpaced the available moisture supply, reducing relative humidity and suppressing precipitation. By contrast, rainfall over basin areas has been maintained by accelerated moisture recycling driven by rapid glacier retreat, snow melt, and irrigation expansion. This trend is unsustainable and is likely to reverse as these cryospheric buffers disappear, with potentially catastrophic implications for local agriculture and ecology.

Difficult to map regions in 30 m global land cover mapping determined with a common validation dataset

ABSTRACT The free availability of decametre global satellite images and high-performance supercomputing provides opportunities for the development of many global products, including land cover, forest change, water, and cropland. However, some regions are particularly hard to map. Identification of these regions aids the understanding of map accuracy issues. In this study, we analysed seven maps produced with different algorithms/approaches but using the same classification system and training samples. A common validation dataset was used to identify regions incorrectly classified by all maps. These were defined as difficult to map regions (DMRs). They covered around 16% of the world’s ice-free terrestrial areas. Our analysis indicated that (1) grassland, shrubland, forest, and cropland were the most common land-cover types that could not be correctly classified, but impervious surfaces had the greatest proportion of misclassification; (2) incorrect classification mainly occurred in tropical/subtropical grassland/savanna/shrubland and desert/xeric shrubland; (3) the spatial distribution of DMRs was almost consistent with slope/elevation changes along latitude/longitude; and (4) the hotspot areas of land-cover mapping studies did not align with the DMRs. Our results suggest that there is a need for further work on DMRs to improve global land-cover mapping accuracy.

Spatial-temporal patterns of features selected using random forests: a case study of corn and soybeans mapping in the US

ABSTRACT It is not easy in remote sensing field to distinguish corn and soybean mapping for the similarity of the mixed summer crops. To understand the variations better and generate corn and soybean maps more accurately, more accurate mapping of these two crops is required. However, classifying different crops with remote sensing is not easy. Finding the discriminating features to use in a mapping classifier can lead to higher-precision mapping. In this paper, we used feature selection of random forests to analyse the most important phenological features for mapping corn and soybeans in the US Corn Belt (also called the Extended Corn Belt, ECB), and identified the spatial and temporal patterns of these features in the ECB. Results show that all the states in the ECB have very similar patterns: Temporal pattern showed that different years always reflected the same pattern. Date-related and phenology curve-related features were the most important. Also, the whole phenology curve was a prerequisite for more accurate mapping. These patterns make the variations more understandable and also support the further research to consider the most important variables for detailed classification.