Cristina Milesi, Arindam Samanta, Hirofumi Hashimoto, K. Krishna Kumar, Sangram Ganguly, Prasad S. Thenkabail, Ashok N. Srivastava, Ramakrishna R. Nemani and Ranga B. Myneni
Article: Decadal Variations in NDVI and Food Production in India
Remote Sens. 2010, 2(3), 758-776; doi:10.3390/rs2030758 - published online 11 March 2010
Show/Hide AbstractAbstract: 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%. In most 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.

Jose Damian Ruiz Sinoga, Ricardo Remond Noa and Danai Fernandez Perez
Article: An Analysis of the Spatial Colonization of Scrubland Intrusive Species in the Itabo and Guanabo Watershed, Cuba
Remote Sens. 2010, 2(3), 740-757; doi:10.3390/rs2030740 - published online 9 March 2010
Show/Hide AbstractAbstract: During the last twenty years, numerous agricultural and farming areas of Cuba have seen a marked increase in invading plants; among the most common species found is the Marabú (Dychrostachys cinerea) and the Aroma (Acacia farnesiana). In the present study, an analysis was carried out of the expansion of these species over the last two decades, in the river basin of the Guanabo (17 km north-east of Havana). This was done by digital processing of satellite images and an analysis of the spatial and statistical data of the Geographical Information System (GIS). The zones most affected by this scrubland were mapped and a study of how natural factors may have influenced land use and the tendency of these species to increase was carried out.

Wen-Qin Wang, Jingye Cai and Qicong Peng
Review: Near-Space Microwave Radar Remote Sensing: Potentials and Challenge Analysis
Remote Sens. 2010, 2(3), 717-739; doi:10.3390/rs2030717 - published online 9 March 2010
Show/Hide AbstractAbstract: Near-space, defined as the region between 20 km and 100 km, offers many new capabilities that are not accessible to low earth orbit (LEO) satellites and airplanes, because it is above storm and not constrained by either the orbital mechanics of satellites or the high fuel consumption of airplanes. By placing radar transmitter/receiver in near-space platforms, many functions that are currently performed with satellites or airplanes could be performed in a cheaper way. Inspired by these advantages, this paper introduces several near-space vehicle-based radar configurations, such as near-space passive bistatic radar and high-resolution wide-swath (HRWS) synthetic aperture radar (SAR). Their potential applications, technical challenges and possible solutions are investigated. It is shown that near-space is a satisfactory solution to some specific remote sensing applications. Firstly, near-space passive bistatic radar using opportunistic illuminators offers a solution to persistent regional remote sensing, which is particularly interest for protecting homeland security or monitoring regional environment. Secondly, near-space provides an optimal solution to relative HRWS SAR imaging. Moreover, as motion compensation is a common technical challenge for the described radars, an active transponder-based motion compensation is also described.

Ignacio Melendez-Pastor, Jose Navarro-Pedreño, Magaly Koch, Ignacio Gómez and Encarni I. Hernández
Article: Land-Cover Phenologies and Their Relation to Climatic Variables in an Anthropogenically Impacted Mediterranean Coastal Area
Remote Sens. 2010, 2(3), 697-716; doi:10.3390/rs2030697 - published online 2 March 2010
Show/Hide AbstractAbstract: Mediterranean coastal areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. Vegetation index time series provide a useful way to monitor vegetation phenological variations. This study quantitatively describes Enhanced Vegetation Index (EVI) temporal changes for Mediterranean land-covers from the perspective of vegetation phenology and its relation with climate. A time series from 2001 to 2007 of the MODIS Enhanced Vegetation Index 16-day composite (MOD13Q1) was analyzed to extract anomalies (by calculating z-scores) and frequency domain components (by the Fourier Transform). Vegetation phenology analyses were developed for diverse land-covers for an area in south Alicante (Spain) providing a useful way to analyze and understand the phenology associated to those land-covers. Time series of climatic variables were also analyzed through anomaly detection techniques and the Fourier Transform. Correlations between EVI time series and climatic variables were computed. Temperature, rainfall and radiation were significantly correlated with almost all land-cover classes for the harmonic analysis amplitude term. However, vegetation phenology was not correlated with climatic variables for the harmonic analysis phase term suggesting a delay between climatic variations and vegetation response.

Sudhanshu Sekhar Panda, Daniel P. Ames and Suranjan Panigrahi
Article: Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques
Remote Sens. 2010, 2(3), 673-696; doi:10.3390/rs2030673 - published online 1 March 2010
Show/Hide AbstractAbstract: Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS modeling approach, and GPS usage) and data mining techniques for model development. Higher-end image processing techniques are followed to establish more precision. The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques. Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota, USA. These indices were: (a) red and near-infrared (NIR) based normalized difference vegetation index (NDVI), (b) green and NIR based green vegetation index (GVI), (c) red and NIR based soil adjusted vegetation index (SAVI), and (d) red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for corn yield during 3 years (1998, 1999, and 2001) and for the pooled data of these 3 years. Initially, Back-propagation Neural Network (BPNN) models were developed, including 16 models (4 indices * 4 years including the data from the pooled years) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction. The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images. In all three years, it provided higher prediction accuracies, coefficient of determination (r²), and lower standard error of prediction than the models involving GVI, NDVI, and SAVI image information. The GVI, NDVI, and SAVI models for all three years provided average testing prediction accuracies of 24.26% to 94.85%, 19.36% to 95.04%, and 19.24% to 95.04%, respectively while the PVI models for all three years provided average testing prediction accuracies of 83.50% to 96.04%. The PVI pool model provided better average testing prediction accuracy of 94% with respect to other vegetation models, for which it ranged from 89–93%. Similarly, the PVI pool model provided coefficient of determination (r²) value of 0.45 as compared to 0.31–0.37 for other index models. Log₁₀ data transformation technique was used to enhance the prediction ability of the PVI models of years 1998, 1999, and 2001 as it was chosen as the preferred index. Another model (Transformed PVI (Pool)) was developed using the log₁₀ transformed PVI image information to show its global application. The transformed PVI models provided average corn yield prediction accuracies of 90%, 97%, and 98% for years 1998, 1999, and 2001, respectively. The pool PVI transformed model provided as average testing accuracy of 93% along with r² value of 0.72 and standard error of prediction of 0.05 t/ha.

Ruba Amarin, Christopher Ruf and Linwood Jones
Article: Impact of Spatial Resolution on Wind Field Derived Estimates of Air Pressure Depression in the Hurricane Eye
Remote Sens. 2010, 2(3), 665-672; doi:10.3390/rs2030665 - published online 1 March 2010
Show/Hide AbstractAbstract: Measurements of the near surface horizontal wind field in a hurricane with spatial resolution of order 1–10 km are possible using airborne microwave radiometer imagers. An assessment is made of the information content of the measured winds as a function of the spatial resolution of the imager. An existing algorithm is used which estimates the maximum surface air pressure depression in the hurricane eye from the maximum wind speed. High resolution numerical model wind fields from Hurricane Frances 2004 are convolved with various HIRAD antenna spatial filters to observe the impact of the antenna design on the central pressure depression in the eye that can be deduced from it.