Your search found 3 records
1 Hao, W.; Jianhua, W.; Dong, J.. 2003. Modern information technology based directional retrieval of annual precipitation of the Yellow River basin. In Yellow River Conservancy Commission. Proceedings, 1st International Yellow River Forum on River Basin Management – Volume III. Zhengzhou, China: The Yellow River Conservancy Publishing House. pp.286-291.
(Location: IWMI-HQ Call no: 333.91 G592 YEL Record No: H034689)
2 You, N.; Dong, J.. 2020. Examining earliest identifiable timing of crops using all available sentinel 1/2 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 161:109-123. [doi: https://doi.org/10.1016/j.isprsjprs.2020.01.001]
Crop production ; Satellite imagery ; Farmland ; Maize ; Soybeans ; Rice ; Vegetation ; Land cover ; Mapping ; Landsat ; Moderate resolution imaging spectroradiometer ; Forecasting / China / Heilongjiang
(Location: IWMI HQ Call no: e-copy only Record No: H049991)
(14.30 MB)
Timely and accurate information on crop planting areas is critical for estimating crop production, and earlier crop mapping can benefit decision-making related to crop insurance, land rental, supply-chain logistics, and food market. Previous efforts generally produce crop planting area maps after harvest and early season cropping information is rarely available. New opportunities emerge with rapid increase in satellite data acquisition and cloud computing platform such as Google Earth Engine (GEE) which can access and process a vast volume of multi-sensor images. Here we aimed to examine earliest identifiable timing (EIT) of major crops (rice, soybean, and corn) and generate early season crop maps independent of within-year field surveys in the Heilongjiang province, one most important province of grain production in China. The Random Forest classifiers were trained based on early season images and field samples in 2017, then were transferred (applied) to corresponding images in 2018 to obtain resultant maps. Six scenarios with different temporal intervals (10d, 15d, 20d, and 30d) and data integration (Sentinel-2 and Sentinel-1, a total of 16, 450 images) were compared to get the optimal crop maps. The results showed that the Sentinel-2 time series and 10-day composite outperformed in obtaining EITs and crop maps. We found various EITs for the three grain staples. Specifically, rice could be identified in the late transplanting stage (four months before harvest) with F1 score of 0.93, following by corn recognizable in the early heading stage (two months before harvest, with F1 score of 0.92) and soybean in the early pod setting stage (50 days before harvest, with F1 score of 0.91). The crop maps in the EITs based on the classifier transfer approach have comparable accuracies (overall accuracy = 0.91) comparing to the traditional post-season mapping approach based on current year’s all available images and samples (overall accuracy = 0.95). This study suggests the potential of growing fine resolution observations for timely monitoring of crop planting area within season, which provides valuable and timely information for different stakeholders and decision makers.
3 Kim, H.; Wigneron, J.-P.; Kumar, S.; Dong, J.; Wagner, W.; Cosh, M. H.; Bosch, D. D.; Collins, C. H.; Starks, P. J.; Seyfried, M.; Lakshmi, V. 2020. Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions. Remote Sensing of Environment, 251:112052. [doi: https://doi.org/10.1016/j.rse.2020.112052]
Soil moisture ; Estimation ; Irrigated farming ; Dry farming ; Satellite observation ; Vegetation ; Forests ; Evapotranspiration ; Precipitation ; Salinity ; Models
(Location: IWMI HQ Call no: e-copy only Record No: H050079)
(13.10 MB)
Over the past four decades, satellite systems and land surface models have been used to estimate global-scale surface soil moisture (SSM). However, in areas such as densely vegetated and irrigated regions, obtaining accurate SSM remains challenging. Before using satellite and model-based SSM estimates over these areas, we should understand the accuracy and error characteristics of various SSM products. Thus, this study aimed to compare the error characteristics of global-scale SSM over vegetated and irrigated areas as obtained from active and passive satellites and model-based data: Advanced Scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), and Global Land Data Assimilation System (GLDAS). We employed triple collocation analysis (TCA) and caluclated conventional error metrics from in-situ SSM measurements. We also considered all possible triplets from 6 different products and showed the viability of considering the standard deviation of TCA-based numbers in producing robust results.
Over forested areas, it was expected that model-based SSM data might provide more accurate SSM estimates than satellites due to the intrinsic limitations of microwave-based systems. Alternately, over irrigated regions, observation-based SSM data were expected to be more accurate than model-based products because land surface models (LSMs) cannot capture irrigation signals caused by human activities. Contrary to these expectations, satellite-based SSM estimates from ASCAT, SMAP, and SMOS showed fewer errors than ERA5 and GLDAS SSM products over vegetated conditions. Furthermore, over irrigated areas, ASCAT, SMOS, and SMAP outperformed other SSM products; however, model-based data from ERA5 and GLDAS outperformed AMSR2. Our results emphasize that, over irrgated areas, considering satellite-based SSM data as alternatives to model-based SSM data sometimes produces misleading results; and considering model-based data as alternatives to satellite-based SSM data in forested areas can also sometimes be misleading. In addition, we discovered that no products showed much degradation in TCA-based errors under different vegetated conditions, while different irrigation conditions impacted both satellite and model-based SSM data sets.
The present research demonstrates that limitations in satellite and modeled SSM data can be overcome in many areas through the synergistic use of satellite and model-based SSM products, excluding areas where satellite-based data are masked out. In fact, when four satellite and model data sets are used selectively, the probability of obtaining SSM with stronger signal than noise can be close to 100%.
Over forested areas, it was expected that model-based SSM data might provide more accurate SSM estimates than satellites due to the intrinsic limitations of microwave-based systems. Alternately, over irrigated regions, observation-based SSM data were expected to be more accurate than model-based products because land surface models (LSMs) cannot capture irrigation signals caused by human activities. Contrary to these expectations, satellite-based SSM estimates from ASCAT, SMAP, and SMOS showed fewer errors than ERA5 and GLDAS SSM products over vegetated conditions. Furthermore, over irrigated areas, ASCAT, SMOS, and SMAP outperformed other SSM products; however, model-based data from ERA5 and GLDAS outperformed AMSR2. Our results emphasize that, over irrgated areas, considering satellite-based SSM data as alternatives to model-based SSM data sometimes produces misleading results; and considering model-based data as alternatives to satellite-based SSM data in forested areas can also sometimes be misleading. In addition, we discovered that no products showed much degradation in TCA-based errors under different vegetated conditions, while different irrigation conditions impacted both satellite and model-based SSM data sets.
The present research demonstrates that limitations in satellite and modeled SSM data can be overcome in many areas through the synergistic use of satellite and model-based SSM products, excluding areas where satellite-based data are masked out. In fact, when four satellite and model data sets are used selectively, the probability of obtaining SSM with stronger signal than noise can be close to 100%.
Powered by DB/Text WebPublisher, from
