Remote sensing provides key inputs to a wide range of models and methods developed for quantifying forest carbon. In particular, carbon inventory methods recommended by IPCC require biomass data and a suite of forest disturbance products. Significant progress has been made in deriving these products by leveraging publicly available remote sensing assets, including observations acquired by the legendary Landsat mission and new systems launched within the past decade, including Sentinel-2, Sentinel-1, GEDI, and ICESAT-2. With the L-band NISAR and P-band BIOMASS missions to be launched in 2023, the Earth' s land surfaces will be imaged by optical and multi-band ( including C-, L-, and P-bands) radar systems that can provide global, sub-weekly observations at sub-hectare spatial resolutions for public use. Fine scale products derived from these observations will be crucial for developing monitoring, reporting, and verification ( MRV) capabilities needed to support carbon trade, REDD+, and other market-driven tools aimed at achieving climate mitigation goals through forest management at all levels. Following a brief discussion of the roles of forests in the global carbon cycle and the wide range of models and methods available for evaluating forest carbon dynamics, this paper provides an overview of recent progress and forthcoming opportunities in using remote sensing to map forest structure and biomass, detect forest disturbances, determine disturbance attribution, quantify disturbance intensity, and estimate harvested timber volume. Advances in these research areas require large quantities of well—distributed reference data to calibrate remote sensing algorithms and to validate the derived products. In addition, two of the forest carbon pools-dead organic matter and soil carbon—are difficult to monitor using modern remote sensing capabilities. Carefully designed inventory programs are needed to collect the required reference data as well as the data needed to estimate dead organic matter and soil carbon.

Chengquan HUANG;Weishu GONG;Yong PANG

Department of Geographical Sciences,University of Maryland,Maryland 20742,USA;Institute of Forest Resource Informa-tion Techniques,Chinese Academy of Forestry,Beijing 100091,China

测绘学报（英文版）

[1]Chengquan HUANG;Weishu GONG;Yong PANG-.Remote Sensing and Forest Carbon Monitoring—a Review of Recent Pro-gress, Challenges and Opportunities)[J].测绘学报（英文版）,2022(02):124-147

legendary,ICESAT,NISAR,BIOMASS,REDD+,Carefully

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