Decadal variations in NDVI and food production in India

In this study we use long-term satellite, climate, and crop observations to document the spatial distribution of the recent stagnation in food grain production affecting the water-limited tropics (WLT), a region where 1.5 billion people live and depend on local agriculture that is constrained by chronic water shortages. Overall, our analysis shows that the recent stagnation in food production is corroborated by satellite data. The growth rate in annually integrated vegetation greenness, a measure of crop growth, has declined significantly (p < 0.10) in 23 of the WLT cropland area during the last decade, while statistically significant increases in the growth rates account for less than 2. Inmost countries, the decade-long declines appear to be primarily due to unsustainable crop management practices rather than climate alone. One quarter of the statistically significant declines are observed in India, which with the world's largest population of food-insecure people and largest WLT croplands, is a leading example of the observed declines. Here we show geographically matching patterns of enhanced crop production and irrigation expansion with groundwater that have leveled off in the past decade. We estimate that, in the absence of irrigation, the enhancement in dry-season food grain production in India, during 1982-2002, would have required an increase in annual rainfall of at least 30 over almost half of the cropland area. This suggests that the past expansion of use of irrigation has not been sustainable. We expect that improved surface and groundwater management practices will be required to reverse the recent food grain production declines. © 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland

Monitoring of Spatiotemporal Dynamics of Rabi Rice Fallows in South Asia Using Remote Sensing

Cereals and grain legumes are the most important part of human diet and nutrition. The expansion of grain legumes with improved productivity to cater the growing population’s nutritional security is of prime importance and need of the hour. Rice fallows are best niche areas with residual moisture to grow short-duration legumes, thereby achieving intensification. Identifying suitable areas for grain legumes and cereal grains is important in this region. In this context, the goal of this study was to map fallow lands followed by rainy season ( kharif ) rice cultivation or post-rainy ( rabi ) fallows in rice-growing environments between 2005 and 2015 using temporal moderate-resolution imaging spectroradiometer (MODIS) data applying spectral matching techniques. This study was conducted in South Asia where different rice ecosystems exist. MODIS 16 day normalized difference vegetation index (NDVI) at 250 m spatial resolution and season-wise-intensive ground survey data were used to map rice systems and the fallows thereafter ( rabi fallows) in South Asia. The rice maps were validated with independent ground survey data and compared with available subnational-level statistics. Overall accuracy and kappa coefficient estimated for rice classes were 81.5% and 0.79%, respectively, with ground survey data. The derived physical rice area and irrigated areas were highly correlated with the subnational statistics with R ^ 2 values of 94% at the district level for the years 2005–2006 and 2015–2016. Results clearly show that rice fallow areas increased from 2005 to 2015. The results show spatial distribution of rice fallows in South Asia, which are identified as target domains for sustainable intensification of short-duration grain legumes, fixing the soil nitrogen and increasing incomes of small-holder farmers

Fifty Years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation—Summary, Insights, and Highlights of Volume IV

This conclusion presents some closing thoughts on the concepts covered in the preceding chapters of this book. The book focuses on precision farming applications using hyperspectral data. It highlights the need to have high spatial, high spectral, and frequent coverage resolutions of imagery for precision farming applications. The book also presents specific parameters modeled and mapped for precision farming applications and include: soil properties such as organic matter, electrical conductivity, potassium, manganese, pH, soil moisture, and soil salinity. It discusses the importance of flower mapping using the distinctive spectral characteristics of flowers, and studies the challenges and limitations of hyperspectral remote sensing in flower mapping. The book provides a fine synopsis of the existing state of knowledge in characterizing and mapping flowers using hyperspectral data in different environments using different sensors from various platforms. It suggests a clear methodology for modeling and mapping Crop water productivity using remote sensing

Spaceborne Hyperspectral EO-1 Hyperion Data Pre-Processing

Increases in global populations and acceleration in land cover and land use change necessitate the rapid monitoring of such changes to address issues like global food security and global water security. Remote sensing has been instrumental in doing this over the past 50 years. There have also been many advances in remote sensing technology; although many of these advances have been limited to multispectral sensors, we have hyperspectral data from the recently decommissioned Hyperion sensor, and upcoming hyperspectral sensors such as Germany's EnMAP and the National Aeronautics Space Administration's (NASA's) HyspIRI. However, there is no standardized protocol for pre-processing hyperspectral data, such as the Hyperion imagery. We need to establish such protocols for hyperspectral data to facilitate the use of these large datasets efficiently to address ecological questions at global extents. In this chapter, we review methods available for pre-processing Hyperion data and suggest a workflow for Hyperion image pre-processing. Examples of these pre-processing steps are also provided for the Google Earth Engine (GEE) cloud-computing platform, which facilitates studies at a global extent by eliminating the need to store and process imagery on a personal computer. These hyperspectral datasets, once pre-processed, are useful for many applications including vegetation classification, biomass estimation, and crop water productivity estimation

Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB) versus multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM+), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of λ1 (400-2500nm) versus λ2 (400-2500nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742nm and 1175nm (HVI742-1175), (ii) 1296nm and 1054nm (HVI1296-1054), (iii) 1225nm and 697nm (HVI1225-697), and (iv) 702nm and 1104nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051-2331nm spectral range, followed by 10% in the moisture sensitive 970nm, 6% in the red and red-edge (630-752nm), and the remaining 10% distributed between blue (400-500nm), green (501-600nm), and NIR (760-900nm).Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies when using HNBs (>. 90%) over MBBs data (varied between 45 and 84%).Finally, the study highlighted 29 HNBs of Hyperion that are optimal in the study of agricultural crops and potentially significant to the upcoming NASA HyspIRI mission. Determining optimal and redundant bands for a given application will help overcoming the Hughes' phenomenon (or curse of high dimensionality of data). © 2013 Elsevier Inc

Monitoring of spatiotemporal dynamics of rabi rice fallows in South asia using remote sensing

Cereals and grain legumes are the most important part of human diet and nutrition. The expansion of grain legumes with improved productivity to cater the growing population’s nutritional security is of prime importance and need of the hour. Rice fallows are best niche areas with residual moisture to grow short-duration legumes, thereby achieving intensification. Identifying suitable areas for grain legumes and cereal grains is important in this region. In this context, the goal of this study was to map fallow lands followed by rainy season ( kharif ) rice cultivation or post-rainy ( rabi ) fallows in rice-growing environments between 2005 and 2015 using temporal moderate-resolution imaging spectroradiometer (MODIS) data applying spectral matching techniques. This study was conducted in South Asia where different rice ecosystems exist. MODIS 16 day normalized difference vegetation index (NDVI) at 250 m spatial resolution and season-wise-intensive ground survey data were used to map rice systems and the fallows thereafter ( rabi fallows) in South Asia. The rice maps were validated with independent ground survey data and compared with available subnational-level statistics. Overall accuracy and kappa coefficient estimated for rice classes were 81.5% and 0.79%, respectively, with ground survey data. The derived physical rice area and irrigated areas were highly correlated with the subnational statistics with R ^ 2 values of 94% at the district level for the years 2005–2006 and 2015–2016. Results clearly show that rice fallow areas increased from 2005 to 2015. The results show spatial distribution of rice fallows in South Asia, which are identified as target domains for sustainable intensification of short-duration grain legumes, fixing the soil nitrogen and increasing incomes of small-holder farmers