Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a "benchmark" map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above-and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ±6% to ±53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ±5% and ca. ±1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.forest biomass | forest height | microwave and optical imaging | error propagation | carbon cycling

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Policies to reduce emissions from deforestation would benefit from clearly derived, spatially explicit, statistically bounded estimates of carbon emissions. Existing efforts derive carbon impacts of land-use change using broad assumptions, unreliable data, or both. We improve on this approach using satellite observations of gross forest cover loss and a map of forest carbon stocks to estimate gross carbon emissions across tropical regions between 2000 and 2005 as 0.81 petagram of carbon per year, with a 90% prediction interval of 0.57 to 1.22 petagrams of carbon per year. This estimate is 25 to 50% of recently published estimates. By systematically matching areas of forest loss with their carbon stocks before clearing, these results serve as a more accurate benchmark for monitoring global progress on reducing emissions from deforestation.

A unique physical feature of paddy rice elds is that rice is grown on ooded soil. During the period of ooding and rice transplanting, there is a large proportion of surface water in a land surface consisting of water, vegetation and soils. The VEGETATION (VGT) sensor has four spectral bands that are equivalent to spectral bands of Landsat TM, and its mid-infrared spectral band is very sensitive to soil moisture and plant canopy water content. In this study we evaluated a VGT-derived normalized diVerence water index (NDWI VGT =(B3 MIR)/ (B3+MIR)) for describing temporal and spatial dynamics of surface moisture. Twenty-seven 10-day composites ( VGT-S10) from 1 March to 30 November 1999 were acquired and analysed for a study area (175 km by 165 km) in eastern Jiangsu Province, China, where a winter wheat and paddy rice double cropping system dominates the landscape. We compared the temporal dynamics and spatial patterns of normalized diVerence vegetation index (NDVI VGT ) and NDWI VGT . The NDWIVGT temporal dynamics were sensitive enough to capture the substantial increases of surface water due to ooding and rice transplanting at paddy rice elds. A land use thematic map for the timing and location of ooding and rice transplanting was generated for the study area. Our results indicate that NDWI and NDVI temporal anomalies may provide a simple and eVective tool for detection of ooding and rice transplanting across the landscape.

From the point of view of biogeochemistry, manure is a complex of organic matter containing minor minerals. When manure is excreted by animals, it undergoes a series of reactions such as decomposition, hydrolysis, ammonia volatilization, nitrification, denitrification, fermentation etc., from which carbon dioxide (CO 2 ), nitrous oxide (N 2 O), methane (CH 4 ) and ammonia (NH 3 ) can be produced. Based on the principles of thermodynamics and reaction kinetics, these reactions are commonly controlled by a group of environmental factors such as temperature, moisture, redox potential, pH, substrate concentration gradient etc. The relations among the environmental factors, the reactions and the gas production have been incorporated in a process-based model, Manure-DNDC, to describe manure organic matter turnover and gas emissions. Using Manure-DNDC, the users can construct a virtual farm by selecting and integrating one or more of the candidate farm facilities (i.e., feedlot, compost, lagoon, anaerobic digester and cropping field) parameterized in the model. Manure-DNDC calculates variations of the environmental factors for each component facility based on its technical specifications, and then utilizes the environmental factors to drive the biogeochemical reactions. To verify the applicability of Manure-DNDC for livestock farms, seven datasets of air emissions measured from farms across the U.S. plus a Scotland pasture were utilized for model tests with encouraging results. A dairy farm in New York was used to assess the impacts of alternative management practices on the gas mitigation. The modeled results showed that a combination of changes in the feed quality, the lagoon coverage and the planted crop type could reduce greenhouse gas emission by 30 % and NH 3 by 36 % at the farm scale.

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Assessment and monitoring of rice agriculture over large areas has been limited by cloud cover, optical sensor spatial and temporal resolutions, and lack of systematic or open access radar. Dense time series of open access Sentinel-1 C-band data at moderate spatial resolution offers new opportunities for monitoring agriculture. This is especially pertinent in South and Southeast Asia where rice is critical to food security and mostly grown during the rainy seasons when high cloud cover is present. In this research application, time series Sentinel-1A Interferometric Wide images (632) were utilized to map rice extent, crop calendar, inundation, and cropping intensity across Myanmar. An updated (2015) land use land cover map fusing Sentinel-1, Landsat-8 OLI, and PALSAR-2 were integrated and classified using a randomforest algorithm. Time series phenological analyses of the dense Sentinel-1 data were then executed to assess rice information across all of Myanmar. The broad land use land cover map identified 186,701 km 2 of cropland across Myanmar with mean out-of-sample kappa of over 90%. A phenological time series analysis refined the cropland class to create a rice mask by extrapolating unique indicators tied to the rice life cycle (dynamic range, inundation, growth stages) from the dense time series Sentinel-1 to map rice paddy characteristics in an automated approach. Analyses show that the harvested rice area was 6,652,111 ha with general (R 2 = 0.78) agreement with government census statistics. The outcomes show strong ability to assess and monitor rice production at moderate scales over a large cloud-prone region. In countries such as Myanmar with large populations and governments dependent upon rice production, more robust and transparent monitoring and assessment tools can help support better decision making. These results indicate that systematic and open access Synthetic Aperture Radar (SAR) can help scale information required by food security initiatives and Monitoring, Reporting, and Verification programs.