Global patterns of cropland use intensity

This study presents a global scale analysis of cropping intensity, crop duration and fallow land extent computed by using the global dataset on monthly irrigated and rainfed crop areas MIRCA2000. MIRCA2000 was mainly derived from census data and crop calendars from literature. Global cropland extent was 16 million km2 around the year 2000 of which 4.4 million km2 (28%) was fallow, resulting in an average cropping intensity of 0.82 for total cropland extent and of 1.13 when excluding fallow land. The lowest cropping intensities related to total cropland extent were found for Southern Africa (0.45), Central America (0.49) and Middle Africa (0.54), while highest cropping intensities were computed for Eastern Asia (1.04) and Southern Asia (1.0). In remote or arid regions where shifting cultivation is practiced, fallow periods last 3–10 years or even longer. In contrast, crops are harvested two or more times per year in highly populated, often irrigated tropical or subtropical lowlands where multi-cropping systems are common. This indicates that intensification of agricultural land use is a strategy that may be able to significantly improve global food security. There exist large uncertainties regarding extent of cropland, harvested crop area and therefore cropping intensity at larger scales. Satellite imagery and remote sensing techniques provide opportunities for decreasing these uncertainties and to improve the MIRCA2000 inventory

Potential of using remote sensing techniques for global assessment of water footprint of crops

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The WF of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use

Mapping Crop Cycles in China Using MODIS-EVI Time Series

As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data

The application of remote sensing to resource management and environmental quality programs in Kansas

The activities of the Kansas Applied Remote Sensing (KARS) Program during the period April 1, 1982 through Marsh 31, 1983 are described. The most important work revolved around the Kansas Interagency Task Force on Applied Remote Sensing and its efforts to establish an operational service oriented remote sensing program in Kansas state government. Concomitant with this work was the upgrading of KARS capabilities to process data for state agencies through the vehicle of a low cost digital data processing system. The KARS Program continued to take an active role in irrigation mapping. KARS is now integrating data acquired through analysis of LANDSAT into geographic information systems designed for evaluating groundwater resources. KARS also continues to work at the national level on the national inventory of state natural resources information systems

An irrigated area map of the world (1999) derived from remote sensing

Remote sensing / Mapping / GIS / Irrigated sites / Estimation

Contributions of natural and human factors to increases in vegetation productivity in China

Increasing trends in vegetation productivity have been identified for the last three decades for many regions in the northern hemisphere including China. Multiple natural and human factors are possibly responsible for the increases in vegetation productivity, while their relative contributions remain unclear. Here we analyzed the long-term trends in vegetation productivity in China using the satellite-derived normalized difference vegetation index (NDVI) and assessed the relationships of NDVI with a suite of natural (air temperature, precipitation, photosynthetically active radiation (PAR), atmospheric carbon dioxide (CO2) concentrations, and nitrogen (N) deposition) and human (afforestation and improved agricultural management practices) factors. Overall, China exhibited an increasing trend in vegetation productivity with an increase of 2.7%. At the provincial scale, eleven provinces exhibited significant increases in vegetation productivity, and the majority of these provinces are located within the northern half of the country. At the national scale, annual air temperature was most closely related to NDVI and explained 36.8% of the variance in NDVI, followed by afforestation (25.5%) and crop yield (15.8%). Altogether, temperature, total forest plantation area, and crop yield explained 78.1% of the variance in vegetation productivity at the national scale, while precipitation, PAR, atmospheric CO2 concentrations, and N deposition made no significant contribution to the increases in vegetation productivity. At the provincial scale, each factor explained a part of the variance in NDVI for some provinces, and the increases in NDVI for many provinces could be attributed to the combined effects of multiple factors. Crop yield and PAR were correlated with NDVI for more provinces than were other factors, indicating that both elevated crop yield resulting from improved agricultural management practices and increasing diffuse radiation were more important than other factors in increasing vegetation productivity at the provincial scale. The relative effects of the natural and human factors on vegetation productivity varied with spatial scale. The true contributions of multiple factors can be obscured by the correlation among these variables, and it is essential to examine the contribution of each factor while controlling for other factors. Future changes in climate and human activities will likely have larger influences on vegetation productivity in China

THE BIG PICTURE - SATELLITE REMOTE SENSING APPLICATIONS IN RANGELAND ASSESSMENT AND CROP INSURANCE

Livestock Production/Industries, Risk and Uncertainty,

Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data

Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available

Groundwater use for irrigation - a global inventory

Irrigation is the most important water use sector accounting for about 70% of the global freshwater withdrawals and 90% of consumptive water uses. While the extent of irrigation and related water uses are reported in statistical databases or estimated by model simulations, information on the source of irrigation water is scarce and very scattered. Here we present a new global inventory on the extent of areas irrigated with groundwater, surface water or non-conventional sources, and we determine the related consumptive water uses. The inventory provides data for 15 038 national and sub-national administrative units. Irrigated area was provided by census-based statistics from international and national organizations. A global model was then applied to simulate consumptive water uses for irrigation by water source. Globally, area equipped for irrigation is currently about 301 million ha of which 38% are equipped for irrigation with groundwater. Total consumptive groundwater use for irrigation is estimated as 545 km3 yr−1, or 43% of the total consumptive irrigation water use of 1 277 km3 yr−1. The countries with the largest extent of areas equipped for irrigation with groundwater, in absolute terms, are India (39 million ha), China (19 million ha) and the United States of America (17 million ha). Groundwater use in irrigation is increasing both in absolute terms and in percentage of total irrigation, leading in places to concentrations of users exploiting groundwater storage at rates above groundwater recharge. Despite the uncertainties associated with statistical data available to track patterns and growth of groundwater use for irrigation, the inventory presented here is a major step towards a more informed assessment of agricultural water use and its consequences for the global water cycle

A digital global map of irrigated areas : an update for Asia

The Land and Water Development Division of the Food and Agriculture Organization of the United Nations and the Johann Wolfgang Goethe University, Frankfurt am Main, Germany, are cooperating in the development of a global irrigation-mapping facility. This report describes an update of the Digital Global Map of Irrigated Areas for the continent of Asia. For this update, an inventory of subnational irrigation statistics for the continent was compiled. The reference year for the statistics is 2000. Adding up the irrigated areas per country as documented in the report gives a total of 188.5 million ha for the entire continent. The total number of subnational units used in the inventory is 4 428. In order to distribute the irrigation statistics per subnational unit, digital spatial data layers and printed maps were used. Irrigation maps were derived from project reports, irrigation subsector studies, and books related to irrigation and drainage. These maps were digitized and compared with satellite images of many regions. In areas without spatial information on irrigated areas, additional information was used to locate areas where irrigation is likely, such as land-cover and land-use maps that indicate agricultural areas or areas with crops that are usually grown under irrigation. Contents 1. Working Report I: Generation of a map of administrative units compatible with statistics used to update the Digital Global Map of Irrigated Areas in Asia 2. Working Report II: The inventory of subnational irrigation statistics for the Asian part of the Digital Global Map of Irrigated Areas 3. Working Report III: Geospatial information used to locate irrigated areas within the subnational units in the Asian part of the Digital Global Map of Irrigated Areas 4. Working Report IV: Update of the Digital Global Map of Irrigated Areas in Asia, Results Map