Remote Sensing of Global Croplands for Food Security

Monitoring of global croplands (GCs) is imperative for ensuring sustainable water and food security for the people of the world in the Twenty-first Century. However, the currently available cropland products suffer from major limitations such as: (1) Absence of precise spatial location of the cropped areas; (b) Coarse resolution nature of the map products and their significant uncertainties in areas, locations, and detail; (b) Uncertainties in differentiating irrigated areas from rainfed areas; (c) Absence of crop types and cropping intensities; and (e) Absence of a dedicated web-based data portal for dissemination of the cropland map products. This research aims overcome the above mentioned limitations through development of a set of Global Food Security-support analysis data at 30 m (GFSAD30) resolution consisting of four distinct products: Cropland extent/area, Crop types with focus on the 8 types that occupy 70% of the global cropland areas, Irrigated versus rainfed croplands, and Cropping intensities: single, double, triple, and continuous cropping. These products are produced using multi-resolution time-series remotely sensed data and a suite of automated and\or semi-automated cropland mapping algorithms (ACMAs). Data include Moderate Resolution Imaging Spectroradiometer (MODIS) time-series, and Landsat Time-series from various epochs. Methods include spectral matching techniques (SMTs), automated cropland classification algorithms (ACCA’s), decision tree algorithms (DTAs), and linear discriminant analysis algorithms (LDAA). Massively large big data (MLBD) are computed over several platforms that include parallel computing over NASA NEX supercomputers, and Google Earth Engine (GEE). Large volumes of ground data are sourced through various crowdsourcing mechanisms and integrated on a web platform: croplands.org.https://commons.und.edu/ss-colloquium/1056/thumbnail.jp

Discrimination of tomato plants (solanum lycopersicum) grown under anaerobic baffled reactor effluent, nitrified urine concentrates and commercial hydroponic fertilizer regimes using simulated sensor spectral settings

We assess the discriminative strength of three different satellite spectral settings (HyspIRI, the forthcoming Landsat 9 and Sentinel 2-MSI), in mapping tomato (Solanum lycopersicum Linnaeus) plants grown under hydroponic system, using human-excreta derived materials (HEDM), namely, anaerobic baffled reactor (ABR) effluent and nitrified urine concentrate (NUC) and commercial hydroponic fertilizer mix (CHFM) as main sources of nutrients. Simulated spectral settings of HyspIRI, Landsat 9 and Sentinel 2-MSI were resampled from spectrometric proximally sensed data. Discriminant analysis (DA) was applied in discriminating tomatoes grown under these different nutrient sources. Results showed that the simulated spectral settings of HyspIRI sensor better discriminate tomatoes grown under different fertilizer regimes when compared to Landsat 9 OLI and Sentinel-2 MSI spectral configurations. Using the DA algorithm, HyspIRI exhibited high overall accuracy (OA) of 0.99 and a kappa statistic of 0.99 whereas Landsat OLI and Sentinel-2 MSI exhibited OA of 0.94 and 0.95 and 0.79 and 0.85 kappa statistics, respectively. Simulated HyspIRI wavebands 710, 720, 690, 840, 1370 and 2110 nm, Sentinel 2-MSI bands 7 (783 nm), 6 (740 nm), 5 (705 nm) and 8a (865 nm) as well as Landsat bands 5 (865 nm), 6 (1610 nm), 7 (2200 nm) and 8 (590 nm), in order of importance, were selected as the most suitable bands for discriminating tomatoes grown under different fertilizer regimes. Overall, the performance of simulated HyspIRI, Landsat 9 OLI-2 and Sentinel-2 MSI spectral bands seem to bring new opportunities for crop monitoring

The use of remote sensing data for drought assessment and monitoring in southwest Asia

Drought / Monitoring / Indicators / Assessment / Remote sensing / Asia

Closing of the Krishna Basin: irrigation, streamflow depletion and macroscale hydrology

River basins / Physical geography / Climate / Stream flow / Hydrology / Rainfall runoff relationships / Evapotranspiration / Irrigation programs / Water allocation / Water transfer / Environmental effects / Water quality / India / Krishna River / Andhra Pradesh / Maharashtra / Karnataka

A sweet deal? Sugarcane, water and agricultural transformation in Sub-Saharan Africa

Globally, the area of sugarcane is rising rapidly in response to growing demands for bioethanol and increased sugar demand for human consumption. Despite considerable diversity in production systems and contexts, sugarcane is a particularly “high impact” crop with significant positive and negative environmental and socio-economic impacts. Our analysis is focused on Sub-Saharan Africa (SSA), which is a critical region for continued expansion, due to its high production potential, low cost of production and proximity, and access, to European markets. Drawing on a systematic review of scientific evidence, combined with information from key informants, stakeholders and a research-industry workshop, we critically assess the impacts of sugarcane development on water, soil and air quality, employment, food security and human health. Our analysis shows that sugarcane production is, in general, neither explicitly good nor bad, sustainable nor unsustainable. The impacts of expansion of sugarcane production on the environment and society depend on the global political economy of sugar, local context, quality of scheme, nature of the production system and farm management. Despite threats from climate change and forthcoming changes in the trade relationship with the European Union, agricultural development policies are driving national and international interest and investment in sugarcane in SSA, with expansion likely to play an important role in sustainable development in the region. Our findings will help guide researchers and policy makers with new insights in understanding the situated environmental and social impacts associated with alternative sugar economy models, production technologies and qualities of management

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

Remote sensing / Mapping / GIS / Irrigated sites / Estimation

Indo-Ganges River Basin Land Use/Land Cover (LULC) and Irrigated Area Mapping

This study was conducted to map detailed land use/land cover (LULC) and irrigated area categories in the Ganges and Indus River basins using near-continuous time-series 250 m resolution moderate-resolution imaging spectroradiometer (MODIS) data. The study used a unique data set—a stack of 46 images, 23 MODIS images each of 2-bands, compiled from MODIS terra images for the years 2013 and 2014. Field-plot data were gathered from 553 precise geographic locations covering about 8000 km in the basins. Spatial information on cropland and irrigated area distribution was restricted by the district-level crop statistics published by the state or national governments in India and Pakistan. Statistics were collected by irrigation and agriculture departments, but there was discrepancy in the irrigated area between departments. Water availability in major command areas varied frequently due to rainfall fluctuations, which leads to an inadequate water supply during critical crop growth stages. The study analyzed MODIS 16-day normalized difference vegetation index (NDVI) time-series data acquired for 2013 and 2014 using spectral matching techniques (SMTs). The map output accuracies were evaluated based on independent ground data and compared with subnational level statistics. The producer's and user's accuracies of the cropland classes were between 70% and 85%. The overall accuracy and the kappa coefficient estimated for irrigated areas were both 84%

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

© 2018 The Author(s) Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer's accuracy of 98.8% (errors of omissions = 1.2%), and user's accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer's accuracy of 80% (errors of omissions = 20%), and user's accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA's Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282

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