Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing Tsuyoshi Akiyama 1 and Y. Inoue National Institute ofAgro-Environmenlal Sciences, 3—l—l, Kannondai, Tsukuba, Ibaraki 305, Japan M. Shibayama National Grassland Research Institute. Japan Y. Awaya and N. Tanaka Forestry and Forest Products Research Institute, Japan LANDSAT/TM data, which are characterized by high spectral/spatial resolutions, are able to con- tribute to practical agricultural management. In the first part of the paper, the authors review some recent applications of satellite remote sensing in agriculture. Techniques for crop discrimination and mapping have made such rapid progress that we can classify crop types with more than 80% accuracy. The estimation of crop biomass using satellite data, including leaf area, dry and fresh weights, and the prediction of grain yield, has been attempted using various spectral vegetation indi- ces. Plant stresses caused by nutrient deficiency and water deficit have also been analysed successfully. Such information may be useful for farm management. In the latter half of the paper, we introduce the Arctic Science Project, which was carried out under the Science and Technology Agency of Japan collaborating with Finnish scientists. In this project, monitoring of the boreal forest was carried out using LANDSAT data. Changes in the phenology of subarctic ground vegetation, based on spectral properties, were measured by a boom-mounted, four-band spectroradiometer. The turning point dates of the seasonal near-infrared (NIR) and red (R) reflectance factors might indicate the end of growth and the beginning of autumnal tints, respectively. Key words: arctic area, biomass, environment, phenology, spectroradiometer, vegetation, yield 'Current address: Institute for Basin Ecosystem Studies, Gifu University, Yanagido, Gifu 501-11, Japan, e-mail: akiyama®green.gifu-u.ac.jp © Agricultural and Food Science in Finland Manuscript received February 1996 367 Vol. 5 (1996): 367-376. AGRICULTURAL AND FOOD SCIENCE IN FINLAND https://www.c-info.fi/en/info/?token=-dpL0GepEeoCao4H.Lr1tcdgYQva0tUhTZNcVHg.n9jMV73vdnTAJvYtnFZqxE6aV1fmLzc6lcvwL2Tgt8uJg1ZbvZi253V-G8rymr3eLMymm1n8CaALufVCtj5lTs2S4weOItw7AxoysoRoCI6IgyKipxGeNibO_m12rehIrClyMmyR6TxNM4s6qsvE21e2XQUXUxQXKuNxSs-jeXMi2uvLixmrleOBzzdPZKoV-_3LyWFudRwlmfek354RmvwsIeuPvUSmdBFZlEU049Q8Skqh6R4-jhejfgQfN0_2rSFbCTzX5tfGkBY2ZKgpTw0GwxG15Mp2IsJVvC3TS33KmnqErhoO8faodrpcWCKce8y5hQ Akiyama, T. et.ai: Analysing agricultural ecosystems by remote sensing Table 1. Characteristics of earth observing satellites. Satellite/ Launched/ Wavelength Ground sensor yr/revolution range nm Band resolution NOAA/ U.S.A Cl: 580-680 Visible I.lkm AVHRR (1978-) C2; 725-1100 Near-1R 1.1 km 1/2 d C3:3550-3930 Mid-IR 1.1 km C4: 11.5-12.5 n Thermal 1.1 km LANDSAT/ U.S.A. MSS4:SOO-600 Visible 80 m MSS (1972-) MSSS:6OO- 700 Visible 80 m 16(18) d MSS6:7OO- 800 Near-IR 80 m MSS7:BOO-1100 Near-IR 80 m LANDSAT/ U.S.A. TM 1:450-520 Visible 30 m TM (1984-) TM2:520- 600 Visible 30 m 16 d TM3:630- 690 Visible 30 m TM4: 760-900 Near-IR 30 m TM5:1550-1750 Mid-IR 30 m TM7:2080-2350 Mid-IR 30 m TM6:10.4-12.5n Thermal 120 m SPOT/ France Bl: 500-590 Visible 20m HRV (1986-) B2:610-680 Visible 20 m 26 d 83:740-900 Near-IR 20 m B4:510-730 Panchromatic 10 m IRS-IA/ India Bl: 450-520 Visible 72.5 m LISS-I (1988-) 62:520-590 Visible 72.5 m 22 d 63:620-680 Visible 72.5 m 84:770-860 Near-IR 72.5 m ERS-1/ ESA C: 5.3 GHz Microwave 30 m SAR (1991-) 35 d JERS-1/ Japan Bl: 520-600 Visible 18 m OPS (1992-) 62:630-690 Visible 18 m 44 d 63:760-860 Near-IR 18 m SAR L: 1.275 GHz Microwave 18 m Monitoring of crops and environment by earth observation satellites Characteristics of satellite sensors and vegetation indices Satellite sensors More than two decades have passed since LANDSAT-1 was launched by NASA in 1972. At present, several kinds of satellites are orbit- ting and observing the surface of the earth (Ta- ble 1). By the end of this century, still more, new satellites with special missions will be launched. In the NOAA series, two satellites are main- tained in polar orbit, one in a morning orbit and the other in an afternoon orbit. They provide a wide range of data, including sea surface tem- perature, cloud cover, data for land surface stud- ies, temperature and humidity profiles, and ozone concentrations. The LANDSAT and SPOT satellites provide high resolution imagery in the range of visible and infrared bands. They are used extensively for high resolution land surface studies. The ERS series concentrates on global and regional environmental issues, making use ofac- 368 AGRICULTURAL AND FOOD SCIENCE IN FINLAND live microwave techniques, which enable a range of measurements to be made of the land, sea, and ice surface independent of cloud cover. The aim of JERS-1 is to observe the earth using optical sensors and a high resolution syn- thetic aperture radar. Land surveys and monitor- ing of various resources are the main applica- tions of this satellite. Spectral vegetation indices Based on the fact that green vegetation absorbs red (R) wavelengths but reflects near-infrared (NIR) wavelengths, several spectral vegetation indices (VI) derived from satellite-borne data have been proposed. VI, including ratio vegeta- tion index (RVI, NIR/R), difference vegetation index (DVI, NIR-R), normalized vegetation in- dex (NDVI, (NIR-R) / (NIR+R)), are used the most by vegetation scientists. In addition, per- pendicular vegetation index (PVI), soil adjusted vegetation index (SAVI), and K values have been proposed to eliminate background soil effects from vegetation. Crop monitoring from space In recent decades, many attempts have been made at extracting agro-environmental informa- tion at the regional level, and some useful tech- nologies have been developed. As a result, the following information can be obtainedby space- borne sensors with high spectral/spatial resolu- tions, such as LANDSAT /TM and SPOT/HRV: 1)crop inventory and planting acreage estimates, 2) leaf area and phytomass estimates and yield prediction, 3) crop stress detection, and 4) agro- environmental survey including soil, vegetation, water and atmosphere. Crop discrimination and planting acreage estimation From 1974 onwards, in the Large Area Crop In- ventory Experiment (LACIE), jointly undertak- en by NASA, NOAA and USDA, satellite remote sensing technology was applied, on an experi- mental basis, to forecast harvests in the major wheatproducing areas of the world using LAND- SAT/MSS data.As a result, a 1977 real-time fore- cast of the wheat production of the Soviet Un- ion indicated that the system could operate and could be applied to other areas and other crops (MacDonald and Hall 1980). This project was followed by AgRISTARS, which focused on the assessment of crop conditions from space (AgRISTARS 1983). Many attempts at crop dis- criminationand planting acreage estimation have been made world-wide for a variety of crops using LANDSAT/MSS, LANDSAT /TM and SPOT/HRV data. Recent studies and classification accuracies are summarized in Table 2. Most of the studies report accuracies exceeding 80%. To illustrate, Table 3 shows crop discrimination results using LANDSAT/TM data in the Tokachi district of Japan. Here, the major crops were classified with 90% accuracy (Fukuhara et al. 1988). In gener- al, the accuracy ofcrop discrimination is strongly dependent on the spectral/spatial resolution of the satellite sensor, the timely acquisition of data at suitable crop stages, and ample field size for detection purposes. Biomass, leaf area and yield estimation Several spectral vegetation indices (VI) have been proposed to estimate vegetational informa- tion, including biomass, yield and leaf area. RVI, which employs the ratio between NIR and R, is often applied for the estimation of aboveground dry weight, fresh weight and leaf area. Estimates of pasture grass yield at the first cut were made over Tochigi prefecture in cen- tral Japan, using LANDSAT/MSS data collect- ed on May 22, 1979. The analysis was conduct- ed by attempting first a land-use classification to identify pasture and meadows. Secondly, a multiple regression analysis was performed to estimate the yield of the first cutting in each pas- ture plot. To design the regression model, 26 plots with known yield data were prepared in advance. A high correlation was observed be- tween the actual forage yield at the first cutting and the estimated yield based on LANDSAT/ MSS using the four bands (Akiyama et al. 1985). 369 Vol. 5 (1996): 367-376. AGRICULTURAL AND FOOD SCIENCE IN FINLAND Akiyama, T. et.al: Analysing agricultural ecosystems by remote sensing Table 2. Results of crop discrimination studies using satellite data. Country/ Satellite/ Main crops Accuracy Authors District Sensor U.S.A./ Landsat/ Com, Soybean 83.9% Batista et al, 1985 Com belt MSS U.S.A./ Landsat/ Alfalfa, Com, Oats 89.8% Loetal. 1986 Wisconsin MSS Hungary Landsat/ Com, Alfalfa 90% Csillag 1986 MSS Sunflower etc. Argentine/ Landsat/ Com, Soybean 80% Badhwar et al. 1987 Buenos Ires MSS6TM Sorgham etc. U.S.A./ Landsat/ 3 Vegetables >90% Williams et al. 1987 NY State TM 4 Crops >75% Japan/ Landsat/ 7 Crops 90.1% Fukuhara et al. 1988 Tokachi TM Hungary/ SPOT/XS Wheat, Soya XS: 85-88% Biittner et al. 1989 Kiskore Landsat/TM Alfalfa TM: 87-91% U.K./ SPOT/ 10 Vegetation types 71% Jewell 1989 East Anglia HRV 4 Vegetation types 88% Table 3. Results of crop classification studies using LANDSAT/TM data in Tokachi district, Japan (Fuku- hara etal. 1988). Performance (%) Crop name Su Po Ad So Co Wh Pa Fo Others Sugar beet (Su) 96.3 0 0 0 0 0 1.7 0 2.0 Potato (Po) 0 98.5 0 0 0 0 0.5 0 1.0 Adzuki bean(Ad) 0 0 2L2 0.5 1.6 0 0 0 0.6 Soybean (So) 0 0 0 100.0 0 0 0 0 0 Com (Co) 0 0 1.2 0 14.4 0 22.5 2.4 Wheat (Wh) 0 0 0 0 3.5 95J. 0 0.2 1.2 Pasture (Pa) 0 0 1.0 0 0 0 88X) 0 11.0 Forest (Fo) 0 0 1.0 0 6.1 0.6 0.5 902 1.6 Underlined numbers indicate the proportion of crops correctly classified (%). Recent results of grain yield prediction for rice and wheat are presented in Table 4. A high accuracy was attained with a multiple re- gression model using 2 or 3 TM bands for rice. Meteorological damage caused by cold windand flooding are also analysed effectively using sat- ellite data. For example, rice damage caused by flooding was highly correlated with the turbidi- ty of water (Yamagata and Akiyama 1988), which reflected in TM bands 2 and 3. Crop stress detection Plants experience various kinds of stresses dur- ing growth, including stresses caused by atmos- pheric factors, by rhizospheric factors, or biotic stresses caused by a variety of organisms. More- over, in recent years, it has often been reported that toxic materials in the environmentresulting from human activities can inflict damage on crops and forests, as in the case of acid rain. If farmers could detect various crop stresses in the 370 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Table 4. Results ofcrop yield prediction using satellite data. Crops Country/ Satellite/ Date of data Model used Correlation, Authors district sensor acquisition Standard deviation Rice Thailand/ LANDSAT/ 09/Dec/88 MRM (TM 1,5, 7) R 2 = 0.85 to 0.92 Tennakoon et al. 1992 Saraburi TM 27/Dec/88 Growth model Rice India/ IRS-IA/ 12,13/Oct/ Area Weighted SD;-2%t0+14% Patel et al. 1991 Orissa LISS-1 88 Average Ratio Rice Japan/ LANDSAT/ 23/Sep/86 MRM (TM2,3,4,5 R2 = 0.953 Mubektietal. 1991 Ishikari TM & TM4/TM3) Cold wind damaged Japan/ LANDSAT/ 19/Sep/80 MRM (MSSS,6) R=0.91 Miyamaetal. 1983 rice Ishikari MSS Flood damaged Japan/ LANDSAT/ 6/Aug/86 MRM (TM2,3) R=0.972 Yamagataand Akiyama 1988 rice Ibaraki TM Wheat Brazil/ LANDSAT/ 24/Jun/86 Meteo. model R2 =0.65 Rudorff and Batista 1991 Sao Paolo TM 27/Jun/87 TM4/TM3 Wheat Japan/ LANDSAT/ 27/Jun/86 MRM (TM2,3) R2 = 0.66 to 0.79 Shiga 1993 Ishikari TM 29/May/90 Wheat India/ LANDSAT 22/Feb/86 NDVI, RVI Variance Singh et al. 1992 Sultanpur TM Layers method 0.1958 Wheat India/ LANDSAT/ Haryana TM 26/Jan/89 Linear yield-spectral Deviation+l4% Sharmaet al. 1993 IRS/IA/ to index to-18% LISS-1 18/Feb/89 MRM: Multiple Regression Model R; Multiple regression coefficient R 2: Coefficient of determination SD: Standard deviation early stages of an infection or a nutrient defi- ciency before any symptoms appear, a timely intervention could prevent the damage to the yield and save the quality of the final product. Water deficit in plants can be clearly detect- ed by NIR and mid-infrared (MIR) wavelength reflectances, which indicate the thermal chang- es caused by stomatal movement. The same band can be used for the crop stress caused by soil- borne diseases, as it enables to detect the wilt- ing of the leaves (Torigoe 1992). Stresses due to meteorological factors do not always show up early. Spikelet sterility is often caused by low temperatures during the flowering stage of rice in the northern part of Japan. Usually, the fertile ears die off at ripen- ing, while the sterile ears remain green at har- vest time. Using LANDSAT /MSS imagery, Mi- yama et al. (1983) were able to create a map of the cold damage to rice on the Ishikari plane of northen Japan. Agro-environmental monitoring Soil survey Many scientists have classified soil types using satellite data. Information on organic matter and moisture content in the soil is particularly important for farmers. Fukuhara et al. (1980) were able to determine differences in the mois- ture contents of the soil using LANDSAT/MSS data in the Tokachi district in Japan. In the same area, Hatanaka et al.( 1989) classified the organ- ic matter content in the soil, using band 3 re- flectance data from LANDSAT/TM obtained in 371 Vol. 5 (1996): 367-376. AGRICULTURAL AND FOOD SCIENCE IN FINLAND Akiyama, T. et.al: Analysing agricultural ecosystems by remote sensing May, when the surface of the upland field was almost bare. The classification results were plot- ted on a 1/50,000 scale map, and used by offic- ers of the agricultural extension services. Land evaluation It is possible for the developing countries to se- lect the land suitable for agricultural develop- ment using satellite data. These data can provide several thematic maps relating biomass, soil moisture and present landuse. When these the- matic maps are overlaid on existing meshed top- ographic or geographic maps, we can obtain val- ue-added information on the productivity of the land (Akiyama et al. 1987). In recent years, the need to quantify the func- tion of agricultural land has become increasing- ly important in Japan. Attempts have been made to quantify the multiple functions of such land, for example the impact of the cultivation ofpad- dy rice on soil conservation, landscape mainte- nance or the capacity for water purification. Remote sensing by a satellite is a prominent tool for such evaluations. Arctic Science Project with Finland International Cooperative Study on Observation of Variabilities in the Arctic Atmosphere, Hydrosphere and Biosphere, and their Interactions One of our most serious concerns today is the fact that global environment change (chiefly deterioration) is taking place at an unprecedented speed, because of ever-growing human activity. We have come to realize that polar regions are very important areas for understanding proc- esses of global change; no less so than the tropical and mid-latitude areas, where most of the human race live. The ongoing environmental changes are not only global in scale, but also varied, and involve diverse and complicated processes, calling for an interdisciplinary approach to address the is- sue. In this context, in 1990 the Science and Technology Agency of Japan organized a 5-year program "International Cooperative Study on Observation ofVariabilities in the Arctic Atmos- phere, Hydrosphere and Biosphere, and their Interactions" in cooperation with 15 governmen- tal institutes, laboratories and universities. The Arctic Science Project consists of three sections, namely, 1) Arctic oceanography and glaciology, 2) Atmospheric chemistry, 3) Arctic vegetation and agricultural environment. The Arctic vegetation team enrolled the assistance of the Technical Research Centre of Finland (VTT). Turku University and the Agricultural Research Centre of Finland. Here, we report on two topics investigated by the vegetation moni- toring sub-teams in the Arctic Science Project. Vegetation change monitoring in the boreal area This part of the project was conducted by scien- tists in theForestry and Forest Products Research Institute of Japan working together withVTT of Finland. An outline of the collaborative research is given below. The study concentrated on the monitoring of vegetation change using satellite data. Seasonal and successional changes of spectra, as a basis for long term monitoring, were studied over the southern boreal forest zone using LANDSAT/ TM images (Awaya et al. 1995). Two monitoring methods were compared at a test site in Kevo, at the northern tip of Fin- land. The first was boundary detection using Laplacian filtering, and the second biomass change detection by subtraction between two normalized difference vegetation indices (NDVI) ofLANDS AT/MSS images acquired in 1972 and 1987. A zonal shift of arctic vegetation was not captured using these methods, but damage to birch forests by the moth caterpillar were clearly detected. This phenomenon appeared to 372 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 367-376. be caused by a combination of the moth cater- pillar and by temperatures lower than the aver- age in the latter half of the 1960’5. It suggested that the vegetation changes are occurring that are related both ecological processes, i.e. the dam- age and the recovery of the trees, and the effects of global climate change. The seasonal spectral patterns of the south- ern boreal forests were clearly identified in the TM3, TM4 and TMS ofLANDSAT in Hokkai- do, Japan. Though the spectra of the young spruce stands seemed to be affected by mixed broadleaved trees in TM3 and TM4, seasonal spectral patterns could be classified into two types, evergreen and deciduous trees, in TMS (Fig. 1, left). Forest types classified according to the major tree showed constant intensities in a normalized difference ratio of TMS and TM3 (NDS3) over the growing season (Fig.l, right). The successional spectral patterns of the spruce stands varied between seasons and channels. However, the relationship between the digital number and the stand age was expressed using an exponential function (not shown here). Spectral properties of subarctic ground vegetation This experiment was conducted by the National Institute of Agro-Environmental Sciences, and the National Grassland Research Institute of Ja- pan collaborating with VTT ofFinland and Turku University (Shibayama et al. 1995). Ground truth information for remote sensing, to assess the phenology of boreal plants, is need- ed in global scale climate change research ac- tivities. A boom-mounted, four-band spectrora- diometer was installed to measure seasonal ra- diances in the green (520-600nm), red (630- 690 nm), near-infrared (765-900nm) and mid- infrared (1570-1730nm) spectral bands from boreal shrub canopies over the growing season in northernmost Finland at Utsjoki. It measured the spectra of four fixed ground plots, each one once per hour, from early June to mid-Septem- ber between 0600 and 1800 hrs local time. A reflectance panel was also measured a few min- utes before and after measurement of each plot. The plant phenology on each plot was also ob- served weekly during the experiment. Seasonal reflectance factors for each plot in each band were calculatedbased on a new calibration meth- od. It involves a correction for the degradation of the reference panel. A hand-held spectroradi- ometer was also used to measure the plant cano- pies and the reference panel in the early autumn. The turning point dates of the seasonal near-in- frared (765-900nm) and red (630-690nm) re- flectance factors might indicate the end of Fig. 1 Seasonal changes of spectra for different the tree species estimated using TM data (Awaya et al. 1995). The spectral changes, from early spring to late autumn, were drawn using eight TM images taken between 1985 and 1993. The numbers in the figure caption represent the year of planting of the trees. 373 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Akiyama, T. et.ai: Analysing agricultural ecosystems by remote sensing Fig. 2 . Seasonal patterns of daily reflectance factors at bands 560, 658, 833 and 1649nm, and the bi-band ratio of the 560 and 658 nm reflectance factors measured for the plant plots (Plots#l,#2) by the automated four-band spectroradiometer at Kevo. The lateral bars show the 95% confidence intervals of the estimated intersection points (the turning point dates of the radiometric variables) (Shibayama et al. 1995). 374 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 367-376. growth and the beginning of the autumnal tints, respectively. The ratio of the green (520-600nm) and red band reflectance factors, however, seemed to be more accurate in predicting these turning points (Fig. 2). Acknowledgements. We wish to express our gratitude to Dr.T. Häme, Dr. A. Salli and Dr. A. Lohi of the Technical Research Centre of Finland and Dr, M. Alanen and Dr. S. Neuvonen of the Kevo Subarctic Research Institute, Uni- versity of Turku, for their diligent support in the Arctic Science Project. The authors are also deeply grateful to Professor T. Mela for his helpful suggestions. References AgRISTARS Program Management Group 1983, Agri- culture and resources inventory surveys through aero- space remote sensing. Research Report, Fiscal Year 1982, APJ2-0393. p. 1-45. Akiyama,!., Miyama, K., Soemarman, H. & Setlyono, J. 1987. Land evaluation system using Landsat data tor agricultural development. Application of PATTERN meth- od in North Sumatera. Journal of the JapaneseAgricul- tural Systems Society 3(2): 74-89. -, Yasuda,Y., Emori, Y. & Miyama, K. 1985. Grassland diagnosis by remote sensing techniques, 2. First cutting yield estimate of pasture using Landsat multispectral data. The Journal of JapaneseSociety of Grassland Sci- ence 31: 97-103. (in Japanese) Awaya, Y., Tanaka, N., Häme, T. & Lohi, A. 1995. Veg- etational change monitoring in the boreal area using re- mote sensing. Changes in the northern Finland and spec- tral characteristics of spruce. Proceedingsof International Arctic Science Symposium, Tsukuba, D: 30-61. Badhwar G.D., Gargantini,C.E. & Redondo, F.V. 1987. Landsat classification of Argentina summer crops. Re- mote Sensing of Environment 7: 265-281. Batista, G.T., Hixson, M.M. & Bauer, M.E. 1985. LAND- SAT MSS crop classification performance as a function of scene characteristics. International Journal of Remote Sensing 6: 1521-1533. Buttner, G. & Csillag, F. 1989. Comparative study of crop and soil mapping using multitemporal and multispec- tral SPOT and Landsat Thematic Mapper data. Remote Sensing of Environment 29: 241-249. Csillag, F. 1986.Comparison of some classification meth- ods on a test-site. International Journal of Remote Sens- ing 7: 1705-1715. Fukuhara, M., Amano.T. & Miyaji, N. 1988. Creation of cropping map using Landsat TM data. Proceedings of 1988 Annual Meeting of the JapanSociety of Photogram- metry and Remote Sensing, J-2:169-172. (in Japanese) Hayashi, S. Yasuda, Y. Emori. Y. & lisaka, J. 1980. Soil moisture analysis for soil mapping. Proceedings of the Machine Processing of Remotely Sensed Data Sym- posium, Purdue University, Indiana, U S.A, p. 1-10. Hatanaka, T., Shiozaki, H., Fukuhara, M., Miyaji, N. & Saito, G. 1989. Estimation of organic matter contents of upland soils with Landsat TM data. Japanese Journal of Soil Science and Plant Nutrition 69: 426-431. (in Japa- nese) Jewell, N. 1989. An evaluation of multi-date SPOT data for agriculture and land use mapping in the United King- dom. International Journal of Remote Sensing 10: 939- 951. Lo, T.H.C., Scarpace, F.L. & Lillesand, T.M. 1986. Use of multitemporal spectral profiles in agricultural land-cover classification. Photogrammetric Engineering and Remote Sensing 52: 535-544. MacDonald, R.B. & Hall, F.G. 1980. Global crop fore- casting. Science 208: 670-679. Miyama, K. Sato, H. Yasuda, Y. & Emori, Y. 1983. An applied study on a remote sensing techniques to survey agricultural land. Survey technique for geographical dis- tribution of damage to rice from cold weather using Land- sat MSS data. Transactions of JSIDRE 105: 27-35. (in Japanese) Mubekti, Miyama, K. & Ogawa, S. 1991. Study on rice yield distribution by using Landsat TM data. The Hokkai- do National Agricultural Experiment Station, Rural De- velopment Research 5:101-113. Patel, N.K., Ravi, N., Navalgund, R.R., Dash, R.N., Das, K.C. & Patnaik, S. 1991. Estimation of rice yield using IRS-1 A digital data in coastaltract of Orissa. International Journal of Remote Sensing 12: 2259-2266. Rudorff, B.F.T. & Batista, G.T. 1991.Wheat yield esti- mation at the farm level using TM Landsat and agrome- teorological data. International Journalof Remote Sens- ing 12: 2477-2484. Sharma, T., Sudha, K.S. Ravi, N., Navalgund, R.R., Tomar, K.P. Chakravarty, N.V.K. & Das, D.K. 1993. Pro- cedures for wheat yield prediction using Landsat MSS and IRS-1 A data. Intrernational Journal of Remote Sens- ing 14: 2509-2518. Shibayama, M., Salli, A., Hame, T., Iso-livari, L., Morinaga, S., Inoue, Y. & Akiyama, T. 1995. Spectral properties of subarctic ground vegetation during the growth period. -Preliminary results of a 1994 experiment in the northernmost Finland. Proceedings of the Interna- tional Arctic Science Symposium, Tsukuba, Japan, D: 6- 29. Shiga, H. 1993.Agricultural information system in Hokkai- do: Integration of arable land information and applica- tion of remote sensing for land evaluation. Journal of the Japanese Agricultural Systems Society 9: 32-39. (in Japanese) Singh, R., Goyal, R.C., Saha, S.K. & Chhikara, R.S. 375 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Akiyama, T et.al: Analysing agricultural ecosystems by remote sensing 1992. Use of satellite spectral data in crop yield estima- tion surveys. International Journal of Remote Sensing 13: 2583-2592. Tennakoon, 5.8., Murty, V.V.N. & Eiumnoh, A. 1992. Estimation of cropped area and grain yield of rice using remote sensing data. International Journal of Remote Sensing 13: 427-439. Torigoe, Y. 1992. Low input sustainable agriculture and information system: Developing integratedpest manage- ment for soilborne disease. Journal of the Japanese Ag- ricultural Systems Society 8: 149-157. (in Japanese) Williams, V.L., Philipson, W.R. & Philpot, W.D. 1987. Identifying vegetable crops with Landsat Thematic Map- per data. Photogrammetric Engineering and Remote Sensing 53: 187-191. Yamagata, Y. & Akiyama, T. 1988. Flood damage anal- ysis using multi-temporal Landsat Thematic Mapper data. International Journal of Remote Sensing 9: 503-514, SELOSTUS Maatalousekosysteemien analysointi ja sadon ennustaminen kaukokartoituksen avulla Tsuyoshi Akiyama, Y. Inoue, M.Shibayama, Y. Awaya ja N. Tanaka National Institute ofAgro-Environmental Sciences, National Grassland Research Institute ja Forestry and Forest Products Research Institute, Japani Tämän artikkelin alussa arvioidaan muutamia satel- liittikaukokartoituksen uusimpia sovelluksia maata- loudessa ja lopussa esitellään japanilais-suomalainen yhteistyöhanke Arctic Science Project. LANDSAT/ TM-satelliittikuva-aineistoa voidaan käyttää maata- loustuotannon seurannassa, sillä kuvien spektri/spa- tiaalinen tarkkuus on hyvä. Erittely- ja kartoitustek- niikat ovat kehittyneet niin paljon, että viljalajit voi- daan erottaa toisistaan 80 % tarkkuudella. Satelliit- tiaineiston avulla on arvioitu sadon biomassaa, esi- merkiksi lehtialaa sekä kuiva- ja tuorepainoa, ja en- nustettu viljasatoa erilaisia laajaspektrisiä kasvilli- suusindeksejä käyttäen. Myös ravinteiden ja veden puutteen aiheuttamaa stressiä on onnistuttu analysoi- maan, Arctic Science Projectissa seurattiin boreaali- sia metsäalueita LANDSAT-satelliittiaineiston avul- la. Subarktisen pohjakasvillisuuden spektriominai- suuksiin perustuvia fenologisia muutoksia mitattiin nelikanavaisella spektroradiometrillä. Käännekohdat vuodenaikaisissa lähes-infrapuna- ja punaheijastu- missa saattavat osoittaa kasvun päättymistä jaruskan alkua. 376 AGRICULTURAL AND FOOD SCIENCE IN FINLAND